From e053100e3756284b2be0a6f60494fcca9e0b477d Mon Sep 17 00:00:00 2001 From: AlexanderKroll <74175710+AlexanderKroll@users.noreply.github.com> Date: Fri, 4 Nov 2022 15:58:33 +0100 Subject: [PATCH] Updating jupyter notebooks --- ...substrate database from GOA database.ipynb | 2664 +++--- ...onal datasets (for re-traing ESM-1b).ipynb | 8076 +++++++++++++++++ ...> 1_2a - Loading Mou et al datasets.ipynb} | 0 ... 1_2b - Loading Yang et al datasets.ipynb} | 0 ... - Training gradient boosting models.ipynb | 1432 ++- ...g additional machine learning models.ipynb | 658 ++ notebooks_and_code/3 Plots and figures.ipynb | 932 +- ... Network (pretraining KM prediction).ipynb | 793 ++ ...4_1 - Training Graph Neural Network.ipynb} | 1159 ++- 9 files changed, 13241 insertions(+), 2473 deletions(-) create mode 100644 notebooks_and_code/1_1 - Creating enzyme-substrate database from GOA database - Additional datasets (for re-traing ESM-1b).ipynb rename notebooks_and_code/{1_2 - Loading Mou et al datasets.ipynb => 1_2a - Loading Mou et al datasets.ipynb} (100%) rename notebooks_and_code/{1_1 - Loading Yang et al datasets.ipynb => 1_2b - Loading Yang et al datasets.ipynb} (100%) create mode 100644 notebooks_and_code/2_4 - Training additional machine learning models.ipynb create mode 100644 notebooks_and_code/4_0 - Training Graph Neural Network (pretraining KM prediction).ipynb rename notebooks_and_code/{4_0 - Training Graph Neural Network.ipynb => 4_1 - Training Graph Neural Network.ipynb} (54%) diff --git a/notebooks_and_code/1_0 - Creating enzyme-substrate database from GOA database.ipynb b/notebooks_and_code/1_0 - Creating enzyme-substrate database from GOA database.ipynb index 32420ae..b9eca08 100644 --- a/notebooks_and_code/1_0 - Creating enzyme-substrate database from GOA database.ipynb +++ b/notebooks_and_code/1_0 - Creating enzyme-substrate database from GOA database.ipynb @@ -12,7 +12,10 @@ "### 4. Splitting the dataset in training and testing set\n", "### 5. Calculating enzyme and substrate representations\n", "### 6. Adding negative data points\n", - "### 7. Adding task-specific enzyme representations" + "### 7. Adding task-specific enzyme representations (extra token)\n", + "### 8. Adding task-specific metabolite representations\n", + "### 9. Adding task-specific enzyme representations (mean representations)\n", + "### 10. Adding ECFP vectors of different dimensions" ] }, { @@ -24,7 +27,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\alexk\\projects\\SubFinder\\notebooks_and_code\n" + "C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\n" ] } ], @@ -69,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -97,7 +100,6 @@ " Definition\n", " Name\n", " RHEA ID\n", - " KEGG ID\n", " \n", " \n", " \n", @@ -107,7 +109,6 @@ " \"Catalysis of the reaction: all-trans-hexapren...\n", " trans-hexaprenyltranstransferase activity\n", " 20836\n", - " NaN\n", " \n", " \n", " 1\n", @@ -115,7 +116,6 @@ " \"Catalysis of the reaction: lactose + H2O = D-...\n", " lactase activity\n", " 10076\n", - " NaN\n", " \n", " \n", " 2\n", @@ -123,7 +123,6 @@ " \"Catalysis of the reaction: adenine + H2O = hy...\n", " adenine deaminase activity\n", " 23688\n", - " NaN\n", " \n", " \n", " 3\n", @@ -131,7 +130,6 @@ " \"Catalysis of the reaction: peptidyl-tRNA(1) +...\n", " peptidyltransferase activity\n", " NaN\n", - " NaN\n", " \n", " \n", " 4\n", @@ -139,7 +137,6 @@ " \"Catalysis of the reaction: succinate + accept...\n", " succinate dehydrogenase activity\n", " 16357\n", - " NaN\n", " \n", " \n", "\n", @@ -153,15 +150,15 @@ "3 GO:0000048 \"Catalysis of the reaction: peptidyl-tRNA(1) +... \n", "4 GO:0000104 \"Catalysis of the reaction: succinate + accept... \n", "\n", - " Name RHEA ID KEGG ID \n", - "0 trans-hexaprenyltranstransferase activity 20836 NaN \n", - "1 lactase activity 10076 NaN \n", - "2 adenine deaminase activity 23688 NaN \n", - "3 peptidyltransferase activity NaN NaN \n", - "4 succinate dehydrogenase activity 16357 NaN " + " Name RHEA ID \n", + "0 trans-hexaprenyltranstransferase activity 20836 \n", + "1 lactase activity 10076 \n", + "2 adenine deaminase activity 23688 \n", + "3 peptidyltransferase activity NaN \n", + "4 succinate dehydrogenase activity 16357 " ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -193,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -202,7 +199,27 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4086" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df.loc[~pd.isnull(df[\"RHEA ID\"])])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -230,7 +247,6 @@ " Definition\n", " Name\n", " RHEA ID\n", - " KEGG ID\n", " \n", " \n", " \n", @@ -240,7 +256,6 @@ " \"Catalysis of the reaction: all-trans-hexapren...\n", " trans-hexaprenyltranstransferase activity\n", " 20836\n", - " NaN\n", " \n", " \n", " 1\n", @@ -248,7 +263,6 @@ " \"Catalysis of the reaction: lactose + H2O = D-...\n", " lactase activity\n", " 10076\n", - " NaN\n", " \n", " \n", " 2\n", @@ -256,7 +270,6 @@ " \"Catalysis of the reaction: adenine + H2O = hy...\n", " adenine deaminase activity\n", " 23688\n", - " NaN\n", " \n", " \n", " 3\n", @@ -264,7 +277,6 @@ " \"Catalysis of the reaction: peptidyl-tRNA(1) +...\n", " peptidyltransferase activity\n", " NaN\n", - " NaN\n", " \n", " \n", " 4\n", @@ -272,7 +284,6 @@ " \"Catalysis of the reaction: succinate + accept...\n", " succinate dehydrogenase activity\n", " 16357\n", - " NaN\n", " \n", " \n", " ...\n", @@ -280,7 +291,6 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", " 6582\n", @@ -288,7 +298,6 @@ " \"Catalysis of the reaction: ATP + H2O = ADP + ...\n", " clathrin-uncoating ATPase activity\n", " NaN\n", - " NaN\n", " \n", " \n", " 6583\n", @@ -296,7 +305,6 @@ " \"Catalysis of the reaction: acetyl-CoA + cytid...\n", " rRNA cytidine N-acetyltransferase activity\n", " NaN\n", - " NaN\n", " \n", " \n", " 6584\n", @@ -304,7 +312,6 @@ " \"Catalysis of the reaction: 2-polyprenyl-3-met...\n", " 2-polyprenyl-3-methyl-5-hydroxy-6-methoxy-1,4-...\n", " NaN\n", - " NaN\n", " \n", " \n", " 6585\n", @@ -312,7 +319,6 @@ " \"Catalysis of the reaction: 2-polyprenyl-6-hyd...\n", " 2-polyprenyl-6-hydroxyphenol O-methyltransfera...\n", " NaN\n", - " NaN\n", " \n", " \n", " 6586\n", @@ -320,11 +326,10 @@ " \"Catalysis of the reaction: cytosylglucuronic ...\n", " cytosylglucuronate decarboxylase activity\n", " NaN\n", - " NaN\n", " \n", " \n", "\n", - "

6587 rows × 5 columns

\n", + "

6587 rows × 4 columns

\n", "" ], "text/plain": [ @@ -341,23 +346,23 @@ "6585 GO:1990888 \"Catalysis of the reaction: 2-polyprenyl-6-hyd... \n", "6586 GO:1990965 \"Catalysis of the reaction: cytosylglucuronic ... \n", "\n", - " Name RHEA ID KEGG ID \n", - "0 trans-hexaprenyltranstransferase activity 20836 NaN \n", - "1 lactase activity 10076 NaN \n", - "2 adenine deaminase activity 23688 NaN \n", - "3 peptidyltransferase activity NaN NaN \n", - "4 succinate dehydrogenase activity 16357 NaN \n", - "... ... ... ... \n", - "6582 clathrin-uncoating ATPase activity NaN NaN \n", - "6583 rRNA cytidine N-acetyltransferase activity NaN NaN \n", - "6584 2-polyprenyl-3-methyl-5-hydroxy-6-methoxy-1,4-... NaN NaN \n", - "6585 2-polyprenyl-6-hydroxyphenol O-methyltransfera... NaN NaN \n", - "6586 cytosylglucuronate decarboxylase activity NaN NaN \n", + " Name RHEA ID \n", + "0 trans-hexaprenyltranstransferase activity 20836 \n", + "1 lactase activity 10076 \n", + "2 adenine deaminase activity 23688 \n", + "3 peptidyltransferase activity NaN \n", + "4 succinate dehydrogenase activity 16357 \n", + "... ... ... \n", + "6582 clathrin-uncoating ATPase activity NaN \n", + "6583 rRNA cytidine N-acetyltransferase activity NaN \n", + "6584 2-polyprenyl-3-methyl-5-hydroxy-6-methoxy-1,4-... NaN \n", + "6585 2-polyprenyl-6-hydroxyphenol O-methyltransfera... NaN \n", + "6586 cytosylglucuronate decarboxylase activity NaN \n", "\n", - "[6587 rows x 5 columns]" + "[6587 rows x 4 columns]" ] }, - "execution_count": 2, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -383,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -422,7 +427,6 @@ " Definition\n", " Name\n", " RHEA ID\n", - " KEGG ID\n", " substrates\n", " \n", " \n", @@ -433,7 +437,6 @@ " \"Catalysis of the reaction: all-trans-hexapren...\n", " trans-hexaprenyltranstransferase activity\n", " 20836\n", - " NaN\n", " [all-trans-hexaprenyl diphosphate, isopentenyl...\n", " \n", " \n", @@ -442,7 +445,6 @@ " \"Catalysis of the reaction: lactose + H2O = D-...\n", " lactase activity\n", " 10076\n", - " NaN\n", " [lactose, H2O ]\n", " \n", " \n", @@ -451,7 +453,6 @@ " \"Catalysis of the reaction: adenine + H2O = hy...\n", " adenine deaminase activity\n", " 23688\n", - " NaN\n", " [adenine, H2O ]\n", " \n", " \n", @@ -460,7 +461,6 @@ " \"Catalysis of the reaction: peptidyl-tRNA(1) +...\n", " peptidyltransferase activity\n", " NaN\n", - " NaN\n", " [peptidyl-tRNA(1), aminoacyl-tRNA(2) ]\n", " \n", " \n", @@ -469,7 +469,6 @@ " \"Catalysis of the reaction: succinate + accept...\n", " succinate dehydrogenase activity\n", " 16357\n", - " NaN\n", " [succinate, acceptor ]\n", " \n", " \n", @@ -479,7 +478,6 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", " 6582\n", @@ -487,7 +485,6 @@ " \"Catalysis of the reaction: ATP + H2O = ADP + ...\n", " clathrin-uncoating ATPase activity\n", " NaN\n", - " NaN\n", " [ATP, H2O ]\n", " \n", " \n", @@ -496,7 +493,6 @@ " \"Catalysis of the reaction: acetyl-CoA + cytid...\n", " rRNA cytidine N-acetyltransferase activity\n", " NaN\n", - " NaN\n", " [acetyl-CoA, cytidine ]\n", " \n", " \n", @@ -505,7 +501,6 @@ " \"Catalysis of the reaction: 2-polyprenyl-3-met...\n", " 2-polyprenyl-3-methyl-5-hydroxy-6-methoxy-1,4-...\n", " NaN\n", - " NaN\n", " [2-polyprenyl-3-methyl-5-hydroxy-6-methoxy-1,4...\n", " \n", " \n", @@ -514,7 +509,6 @@ " \"Catalysis of the reaction: 2-polyprenyl-6-hyd...\n", " 2-polyprenyl-6-hydroxyphenol O-methyltransfera...\n", " NaN\n", - " NaN\n", " [2-polyprenyl-6-hydroxyphenol, S-adenosyl-L-me...\n", " \n", " \n", @@ -523,12 +517,11 @@ " \"Catalysis of the reaction: cytosylglucuronic ...\n", " cytosylglucuronate decarboxylase activity\n", " NaN\n", - " NaN\n", " [cytosylglucuronic acid, H(+) ]\n", " \n", " \n", "\n", - "

6587 rows × 6 columns

\n", + "

6587 rows × 5 columns

\n", "" ], "text/plain": [ @@ -545,18 +538,18 @@ "6585 GO:1990888 \"Catalysis of the reaction: 2-polyprenyl-6-hyd... \n", "6586 GO:1990965 \"Catalysis of the reaction: cytosylglucuronic ... \n", "\n", - " Name RHEA ID KEGG ID \\\n", - "0 trans-hexaprenyltranstransferase activity 20836 NaN \n", - "1 lactase activity 10076 NaN \n", - "2 adenine deaminase activity 23688 NaN \n", - "3 peptidyltransferase activity NaN NaN \n", - "4 succinate dehydrogenase activity 16357 NaN \n", - "... ... ... ... \n", - "6582 clathrin-uncoating ATPase activity NaN NaN \n", - "6583 rRNA cytidine N-acetyltransferase activity NaN NaN \n", - "6584 2-polyprenyl-3-methyl-5-hydroxy-6-methoxy-1,4-... NaN NaN \n", - "6585 2-polyprenyl-6-hydroxyphenol O-methyltransfera... NaN NaN \n", - "6586 cytosylglucuronate decarboxylase activity NaN NaN \n", + " Name RHEA ID \\\n", + "0 trans-hexaprenyltranstransferase activity 20836 \n", + "1 lactase activity 10076 \n", + "2 adenine deaminase activity 23688 \n", + "3 peptidyltransferase activity NaN \n", + "4 succinate dehydrogenase activity 16357 \n", + "... ... ... \n", + "6582 clathrin-uncoating ATPase activity NaN \n", + "6583 rRNA cytidine N-acetyltransferase activity NaN \n", + "6584 2-polyprenyl-3-methyl-5-hydroxy-6-methoxy-1,4-... NaN \n", + "6585 2-polyprenyl-6-hydroxyphenol O-methyltransfera... NaN \n", + "6586 cytosylglucuronate decarboxylase activity NaN \n", "\n", " substrates \n", "0 [all-trans-hexaprenyl diphosphate, isopentenyl... \n", @@ -571,10 +564,10 @@ "6585 [2-polyprenyl-6-hydroxyphenol, S-adenosyl-L-me... \n", "6586 [cytosylglucuronic acid, H(+) ] \n", "\n", - "[6587 rows x 6 columns]" + "[6587 rows x 5 columns]" ] }, - "execution_count": 3, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -605,7 +598,116 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\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 11\u001b[0m \u001b[0mCHEBI_ID_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_substrate_IDs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mIDs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCHEBI_IDs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mLines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLines\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mdf_RHEA\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_RHEA\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"RHEA ID\"\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mRHEA_ID\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"CHEBI_ID_list\"\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mCHEBI_ID_list\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 14\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mappend\u001b[1;34m(self, other, ignore_index, verify_integrity, sort)\u001b[0m\n\u001b[0;32m 8963\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mignore_index\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8964\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 8965\u001b[1;33m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8966\u001b[0m )\n\u001b[0;32m 8967\u001b[0m ).__finalize__(self, method=\"append\")\n", + "\u001b[1;32m~\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 310\u001b[0m )\n\u001b[1;32m--> 311\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 313\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[0;32m 305\u001b[0m )\n\u001b[0;32m 306\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 307\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 308\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 309\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36mget_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 531\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 532\u001b[0m new_data = concatenate_managers(\n\u001b[1;32m--> 533\u001b[1;33m \u001b[0mmgrs_indexers\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnew_axes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconcat_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbm_axis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 534\u001b[0m )\n\u001b[0;32m 535\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\pandas\\core\\internals\\concat.py\u001b[0m in \u001b[0;36mconcatenate_managers\u001b[1;34m(mgrs_indexers, axes, concat_axis, copy)\u001b[0m\n\u001b[0;32m 224\u001b[0m \u001b[0mfastpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 225\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 226\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_concatenate_join_units\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjoin_units\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconcat_axis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 227\u001b[0m \u001b[0mfastpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 228\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\pandas\\core\\internals\\concat.py\u001b[0m in \u001b[0;36m_concatenate_join_units\u001b[1;34m(join_units, concat_axis, copy)\u001b[0m\n\u001b[0;32m 490\u001b[0m to_concat = [\n\u001b[0;32m 491\u001b[0m \u001b[0mju\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_reindexed_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mempty_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mempty_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupcasted_na\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mupcasted_na\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 492\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mju\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mjoin_units\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 493\u001b[0m ]\n\u001b[0;32m 494\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\pandas\\core\\internals\\concat.py\u001b[0m in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 490\u001b[0m to_concat = [\n\u001b[0;32m 491\u001b[0m \u001b[0mju\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_reindexed_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mempty_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mempty_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupcasted_na\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mupcasted_na\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 492\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mju\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mjoin_units\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 493\u001b[0m ]\n\u001b[0;32m 494\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\pandas\\core\\internals\\concat.py\u001b[0m in \u001b[0;36mget_reindexed_values\u001b[1;34m(self, empty_dtype, upcasted_na)\u001b[0m\n\u001b[0;32m 443\u001b[0m \u001b[0mempty_dtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mempty_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 444\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 445\u001b[1;33m \u001b[0mmissing_arr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mempty_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 446\u001b[0m \u001b[0mmissing_arr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 447\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mmissing_arr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "df_RHEA = pd.DataFrame(columns = [\"RHEA ID\", \"CHEBI IDs\", \"CHEBI_ID_list\"])\n", + "\n", + "file1 = open(join(CURRENT_DIR, \"..\" ,\"data\", \"reaction_data\", \"rhea-reactions.txt\"), 'r')\n", + "Lines = file1.readlines()\n", + "\n", + "while True:\n", + " try:\n", + " end = Lines.index('///\\n')\n", + " entry = Lines[:end]\n", + " RHEA_ID, CHEBI_IDs = extract_RHEA_ID_and_CHEBI_IDs(entry)\n", + " CHEBI_ID_list = get_substrate_IDs(IDs = CHEBI_IDs)\n", + " Lines = Lines[end+1:]\n", + " df_RHEA = df_RHEA.append({\"RHEA ID\" : RHEA_ID, \"CHEBI_ID_list\" : CHEBI_ID_list}, ignore_index = True)\n", + " except ValueError:\n", + " break\n", + " \n", + "df_RHEA[\"RHEA ID\"] = [float(ID.split(\":\")[-1]) for ID in df_RHEA[\"RHEA ID\"]]\n", + "df_RHEA.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"reaction_data\", \"RHEA_reaction_df.pkl\"))\n", + "df_RHEA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (b)(ii) Mapping CHEBI IDs to df_catalytic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_catalytic[\"RHEA ID\"] = [float(ID) for ID in df_catalytic[\"RHEA ID\"]]\n", + "df_catalytic = df_catalytic.merge(df_RHEA, how = \"left\", on = [\"RHEA ID\"])\n", + "df_catalytic.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (b)(iii) Creating DataFrame with all combinations of GO Terms and single substrates:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_substrates = pd.DataFrame(columns = [\"GO ID\", \"molecule\", \"molecule ID\", \"RHEA ID\"])\n", + "df_catalytic[\"CHEBI_ID_list\"].loc[pd.isnull(df_catalytic[\"CHEBI_ID_list\"])] = \"\"\n", + "\n", + "for ind in df_catalytic.index:\n", + " GO_ID = df_catalytic[\"GO ID\"][ind]\n", + " RHEA_ID = df_catalytic[\"RHEA ID\"][ind]\n", + " \n", + " if len(df_catalytic[\"CHEBI_ID_list\"][ind]) > 0:\n", + " IDs = df_catalytic[\"CHEBI_ID_list\"][ind]\n", + " for ID in IDs:\n", + " df_substrates = df_substrates.append({\"GO ID\" : GO_ID, \"molecule\": np.nan, \"molecule ID\" : ID,\n", + " \"RHEA ID\" : RHEA_ID},\n", + " ignore_index = True)\n", + " else:\n", + " metabolites = df_catalytic[\"substrates\"][ind]\n", + " for met in metabolites:\n", + " df_substrates = df_substrates.append({\"GO ID\" : GO_ID, \"molecule\": met, \"molecule ID\" : np.nan,\n", + " \"RHEA ID\" : RHEA_ID},\n", + " ignore_index = True)\n", + " \n", + "df_substrates.reset_index(inplace = True, drop = True)\n", + "df_substrates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (c) Trying to map all substrates without a CHEBI ID to an identifier:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -629,553 +731,101 @@ " \n", " \n", " \n", - " RHEA ID\n", - " CHEBI IDs\n", - " CHEBI_ID_list\n", + " metabolites\n", " \n", " \n", " \n", " \n", " 0\n", - " 10000.0\n", - " NaN\n", - " [CHEBI:15377, CHEBI:16459]\n", + " xanthan\n", " \n", " \n", " 1\n", - " 10001.0\n", - " NaN\n", - " [CHEBI:15377, CHEBI:16459]\n", + " indan-1-ol\n", " \n", " \n", " 2\n", - " 10002.0\n", - " NaN\n", - " [CHEBI:28938, CHEBI:31011]\n", + " oxidosqualene\n", " \n", " \n", " 3\n", - " 10003.0\n", - " NaN\n", - " [CHEBI:15377, CHEBI:16459]\n", + " galactogen\n", " \n", " \n", " 4\n", - " 10004.0\n", - " NaN\n", - " [CHEBI:17484]\n", + " (histone)-arginine\n", " \n", " \n", " ...\n", " ...\n", - " ...\n", - " ...\n", " \n", " \n", - " 54195\n", - " 66623.0\n", - " NaN\n", - " [CHEBI:71550, CHEBI:15379, CHEBI:57618]\n", + " 2402\n", + " GDP-mannose = mannan(n+1)\n", " \n", " \n", - " 54196\n", - " 66624.0\n", - " NaN\n", - " [CHEBI:16175, CHEBI:15379, CHEBI:57618]\n", + " 2403\n", + " a 3-oxo-dodecanoyl-[acp]\n", " \n", " \n", - " 54197\n", - " 66625.0\n", - " NaN\n", - " [CHEBI:16175, CHEBI:15379, CHEBI:57618]\n", + " 2404\n", + " coenzyme A or its derivatives\n", " \n", " \n", - " 54198\n", - " 66626.0\n", - " NaN\n", - " [CHEBI:71541, CHEBI:15378, CHEBI:15377, CHEBI:...\n", + " 2405\n", + " DNA with alkylated base\n", " \n", " \n", - " 54199\n", - " 66627.0\n", - " NaN\n", - " [CHEBI:16175, CHEBI:15379, CHEBI:57618]\n", + " 2406\n", + " 2,4-dihydroxy-hept-trans-2-ene-1,7-dioate\n", " \n", " \n", "\n", - "

54200 rows × 3 columns

\n", + "

2407 rows × 1 columns

\n", "" ], "text/plain": [ - " RHEA ID CHEBI IDs CHEBI_ID_list\n", - "0 10000.0 NaN [CHEBI:15377, CHEBI:16459]\n", - "1 10001.0 NaN [CHEBI:15377, CHEBI:16459]\n", - "2 10002.0 NaN [CHEBI:28938, CHEBI:31011]\n", - "3 10003.0 NaN [CHEBI:15377, CHEBI:16459]\n", - "4 10004.0 NaN [CHEBI:17484]\n", - "... ... ... ...\n", - "54195 66623.0 NaN [CHEBI:71550, CHEBI:15379, CHEBI:57618]\n", - "54196 66624.0 NaN [CHEBI:16175, CHEBI:15379, CHEBI:57618]\n", - "54197 66625.0 NaN [CHEBI:16175, CHEBI:15379, CHEBI:57618]\n", - "54198 66626.0 NaN [CHEBI:71541, CHEBI:15378, CHEBI:15377, CHEBI:...\n", - "54199 66627.0 NaN [CHEBI:16175, CHEBI:15379, CHEBI:57618]\n", + " metabolites\n", + "0 xanthan \n", + "1 indan-1-ol\n", + "2 oxidosqualene \n", + "3 galactogen\n", + "4 (histone)-arginine \n", + "... ...\n", + "2402 GDP-mannose = mannan(n+1)\n", + "2403 a 3-oxo-dodecanoyl-[acp] \n", + "2404 coenzyme A or its derivatives\n", + "2405 DNA with alkylated base\n", + "2406 2,4-dihydroxy-hept-trans-2-ene-1,7-dioate \n", "\n", - "[54200 rows x 3 columns]" + "[2407 rows x 1 columns]" ] }, - "execution_count": 5, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_RHEA = pd.DataFrame(columns = [\"RHEA ID\", \"CHEBI IDs\", \"CHEBI_ID_list\"])\n", - "\n", - "file1 = open(join(CURRENT_DIR, \"..\" ,\"data\", \"reaction_data\", \"rhea-reactions.txt\"), 'r')\n", - "Lines = file1.readlines()\n", - "\n", - "while True:\n", - " try:\n", - " end = Lines.index('///\\n')\n", - " entry = Lines[:end]\n", - " RHEA_ID, CHEBI_IDs = extract_RHEA_ID_and_CHEBI_IDs(entry)\n", - " CHEBI_ID_list = get_substrate_IDs(IDs = CHEBI_IDs)\n", - " Lines = Lines[end+1:]\n", - " df_RHEA = df_RHEA.append({\"RHEA ID\" : RHEA_ID, \"CHEBI_ID_list\" : CHEBI_ID_list}, ignore_index = True)\n", - " except ValueError:\n", - " break\n", - " \n", - "df_RHEA[\"RHEA ID\"] = [float(ID.split(\":\")[-1]) for ID in df_RHEA[\"RHEA ID\"]]\n", - "df_RHEA.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"reaction_data\", \"RHEA_reaction_df.pkl\"))\n", - "df_RHEA" + "metabolites = []\n", + "for ind in df_substrates.index:\n", + " if pd.isnull(df_substrates[\"molecule ID\"][ind]):\n", + " metabolites = metabolites + [df_substrates[\"molecule\"][ind]]\n", + " \n", + "df_unmapped = pd.DataFrame(data = {\"metabolites\" : list(set(metabolites))})\n", + "df_unmapped" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### (b)(ii) Mapping CHEBI IDs to df_catalytic" + "#### (c)(i) Mapping metabolite names to KEGG compound synonym database:" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GO IDDefinitionNameRHEA IDKEGG IDsubstratesCHEBI IDsCHEBI_ID_list
0GO:0000010\"Catalysis of the reaction: all-trans-hexapren...trans-hexaprenyltranstransferase activity20836.0NaN[all-trans-hexaprenyl diphosphate, isopentenyl...NaN[CHEBI:58179, CHEBI:128769]
1GO:0000016\"Catalysis of the reaction: lactose + H2O = D-...lactase activity10076.0NaN[lactose, H2O ]NaN[CHEBI:15377, CHEBI:17716]
2GO:0000034\"Catalysis of the reaction: adenine + H2O = hy...adenine deaminase activity23688.0NaN[adenine, H2O ]NaN[CHEBI:16708, CHEBI:15378, CHEBI:15377]
3GO:0000048\"Catalysis of the reaction: peptidyl-tRNA(1) +...peptidyltransferase activityNaNNaN[peptidyl-tRNA(1), aminoacyl-tRNA(2) ]NaNNaN
4GO:0000104\"Catalysis of the reaction: succinate + accept...succinate dehydrogenase activity16357.0NaN[succinate, acceptor ]NaN[CHEBI:13193, CHEBI:30031]
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" - ], - "text/plain": [ - " GO ID Definition \\\n", - "0 GO:0000010 \"Catalysis of the reaction: all-trans-hexapren... \n", - "1 GO:0000016 \"Catalysis of the reaction: lactose + H2O = D-... \n", - "2 GO:0000034 \"Catalysis of the reaction: adenine + H2O = hy... \n", - "3 GO:0000048 \"Catalysis of the reaction: peptidyl-tRNA(1) +... \n", - "4 GO:0000104 \"Catalysis of the reaction: succinate + accept... \n", - "\n", - " Name RHEA ID KEGG ID \\\n", - "0 trans-hexaprenyltranstransferase activity 20836.0 NaN \n", - "1 lactase activity 10076.0 NaN \n", - "2 adenine deaminase activity 23688.0 NaN \n", - "3 peptidyltransferase activity NaN NaN \n", - "4 succinate dehydrogenase activity 16357.0 NaN \n", - "\n", - " substrates CHEBI IDs \\\n", - "0 [all-trans-hexaprenyl diphosphate, isopentenyl... NaN \n", - "1 [lactose, H2O ] NaN \n", - "2 [adenine, H2O ] NaN \n", - "3 [peptidyl-tRNA(1), aminoacyl-tRNA(2) ] NaN \n", - "4 [succinate, acceptor ] NaN \n", - "\n", - " CHEBI_ID_list \n", - "0 [CHEBI:58179, CHEBI:128769] \n", - "1 [CHEBI:15377, CHEBI:17716] \n", - "2 [CHEBI:16708, CHEBI:15378, CHEBI:15377] \n", - "3 NaN \n", - "4 [CHEBI:13193, CHEBI:30031] " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_catalytic[\"RHEA ID\"] = [float(ID) for ID in df_catalytic[\"RHEA ID\"]]\n", - "df_catalytic = df_catalytic.merge(df_RHEA, how = \"left\", on = [\"RHEA ID\"])\n", - "df_catalytic.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (b)(iii) Creating DataFrame with all combinations of GO Terms and single substrates:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
GO IDmoleculemolecule ID
0GO:0000010NaNCHEBI:58179
1GO:0000010NaNCHEBI:128769
2GO:0000016NaNCHEBI:15377
3GO:0000016NaNCHEBI:17716
4GO:0000034NaNCHEBI:16708
............
14649GO:1990887S-adenosyl-L-methionineNaN
14650GO:19908882-polyprenyl-6-hydroxyphenolNaN
14651GO:1990888S-adenosyl-L-methionineNaN
14652GO:1990965cytosylglucuronic acidNaN
14653GO:1990965H(+)NaN
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14654 rows × 3 columns

\n", - "
" - ], - "text/plain": [ - " GO ID molecule molecule ID\n", - "0 GO:0000010 NaN CHEBI:58179\n", - "1 GO:0000010 NaN CHEBI:128769\n", - "2 GO:0000016 NaN CHEBI:15377\n", - "3 GO:0000016 NaN CHEBI:17716\n", - "4 GO:0000034 NaN CHEBI:16708\n", - "... ... ... ...\n", - "14649 GO:1990887 S-adenosyl-L-methionine NaN\n", - "14650 GO:1990888 2-polyprenyl-6-hydroxyphenol NaN\n", - "14651 GO:1990888 S-adenosyl-L-methionine NaN\n", - "14652 GO:1990965 cytosylglucuronic acid NaN\n", - "14653 GO:1990965 H(+) NaN\n", - "\n", - "[14654 rows x 3 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_substrates = pd.DataFrame(columns = [\"GO ID\", \"molecule\", \"molecule ID\"])\n", - "df_catalytic[\"CHEBI_ID_list\"].loc[pd.isnull(df_catalytic[\"CHEBI_ID_list\"])] = \"\"\n", - "\n", - "for ind in df_catalytic.index:\n", - " GO_ID = df_catalytic[\"GO ID\"][ind]\n", - " \n", - " if len(df_catalytic[\"CHEBI_ID_list\"][ind]) > 0:\n", - " IDs = df_catalytic[\"CHEBI_ID_list\"][ind]\n", - " for ID in IDs:\n", - " df_substrates = df_substrates.append({\"GO ID\" : GO_ID, \"molecule\": np.nan, \"molecule ID\" : ID},\n", - " ignore_index = True)\n", - " else:\n", - " metabolites = df_catalytic[\"substrates\"][ind]\n", - " for met in metabolites:\n", - " df_substrates = df_substrates.append({\"GO ID\" : GO_ID, \"molecule\": met, \"molecule ID\" : np.nan},\n", - " ignore_index = True)\n", - " \n", - "df_substrates.reset_index(inplace = True, drop = True)\n", - "df_substrates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (c) Trying to map all substrates without a CHEBI ID to an identifier:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
metabolites
0xanthan
1indan-1-ol
2oxidosqualene
3galactogen
4(histone)-arginine
......
2402GDP-mannose = mannan(n+1)
2403a 3-oxo-dodecanoyl-[acp]
2404coenzyme A or its derivatives
2405DNA with alkylated base
24062,4-dihydroxy-hept-trans-2-ene-1,7-dioate
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2407 rows × 1 columns

\n", - "
" - ], - "text/plain": [ - " metabolites\n", - "0 xanthan \n", - "1 indan-1-ol\n", - "2 oxidosqualene \n", - "3 galactogen\n", - "4 (histone)-arginine \n", - "... ...\n", - "2402 GDP-mannose = mannan(n+1)\n", - "2403 a 3-oxo-dodecanoyl-[acp] \n", - "2404 coenzyme A or its derivatives\n", - "2405 DNA with alkylated base\n", - "2406 2,4-dihydroxy-hept-trans-2-ene-1,7-dioate \n", - "\n", - "[2407 rows x 1 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metabolites = []\n", - "for ind in df_substrates.index:\n", - " if pd.isnull(df_substrates[\"molecule ID\"][ind]):\n", - " metabolites = metabolites + [df_substrates[\"molecule\"][ind]]\n", - " \n", - "df_unmapped = pd.DataFrame(data = {\"metabolites\" : list(set(metabolites))})\n", - "df_unmapped" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (c)(i) Mapping metabolite names to KEGG compound synonym database:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1870,7 +1520,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_substrates.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"GOA_data\", \"df_GO_catalytic.pkl\"))" + "df_substrates.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"GOA_data\", \"df_GO_catalytic_2.pkl\"))" ] }, { @@ -1882,11 +1532,31 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "df_substrates = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"GOA_data\", \"df_GO_catalytic.pkl\"))" + "df_substrates = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"GOA_data\", \"df_GO_catalytic_2.pkl\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "824" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df_catalytic) - len(set(df_substrates[\"GO ID\"]))" ] }, { @@ -1906,11 +1576,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "df = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"GOA_data\", \"df_GO_catalytic.pkl\"))\n", + "df = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"GOA_data\", \"df_GO_catalytic_2.pkl\"))\n", "catalytic_go_terms = list(set(df[\"GO ID\"]))" ] }, @@ -2009,7 +1679,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -2025,7 +1695,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 25, "metadata": { "scrolled": true }, @@ -2341,6 +2011,7 @@ " Uniprot ID\n", " molecule ID\n", " evidence\n", + " RHEA ID\n", " \n", " \n", " \n", @@ -2349,106 +2020,122 @@ " A0A009IHW8\n", " CHEBI:15377\n", " exp\n", + " 16301\n", " \n", " \n", " 1\n", " A0A009IHW8\n", " CHEBI:57540\n", " exp\n", + " 16301\n", " \n", " \n", " 2\n", " A0A022PMU5\n", " C00030\n", " phylo\n", + " NaN\n", " \n", " \n", " 3\n", " A0A022PN36\n", " CHEBI:57318\n", " phylo\n", + " 22432\n", " \n", " \n", " 4\n", " A0A022PN36\n", " CHEBI:57540\n", " phylo\n", + " 22432\n", " \n", " \n", " ...\n", " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 495070\n", " Z4YNJ9\n", " CHEBI:15378\n", " phylo\n", + " 19721\n", " \n", " \n", " 495071\n", " Z4YNJ9\n", " CHEBI:15379\n", " phylo\n", + " 19721\n", " \n", " \n", " 495072\n", " Z4YNJ9\n", " CHEBI:57394\n", " phylo\n", + " 19721\n", " \n", " \n", " 495073\n", " Z4YNJ9\n", " C00154\n", " phylo\n", + " NaN\n", " \n", " \n", " 495074\n", " Z4YNJ9\n", " C00030\n", " phylo\n", + " NaN\n", " \n", " \n", "\n", - "

462406 rows × 3 columns

\n", + "

476866 rows × 4 columns

\n", "" ], "text/plain": [ - " Uniprot ID molecule ID evidence\n", - "0 A0A009IHW8 CHEBI:15377 exp\n", - "1 A0A009IHW8 CHEBI:57540 exp\n", - "2 A0A022PMU5 C00030 phylo\n", - "3 A0A022PN36 CHEBI:57318 phylo\n", - "4 A0A022PN36 CHEBI:57540 phylo\n", - "... ... ... ...\n", - "495070 Z4YNJ9 CHEBI:15378 phylo\n", - "495071 Z4YNJ9 CHEBI:15379 phylo\n", - "495072 Z4YNJ9 CHEBI:57394 phylo\n", - "495073 Z4YNJ9 C00154 phylo\n", - "495074 Z4YNJ9 C00030 phylo\n", + " Uniprot ID molecule ID evidence RHEA ID\n", + "0 A0A009IHW8 CHEBI:15377 exp 16301\n", + "1 A0A009IHW8 CHEBI:57540 exp 16301\n", + "2 A0A022PMU5 C00030 phylo NaN\n", + "3 A0A022PN36 CHEBI:57318 phylo 22432\n", + "4 A0A022PN36 CHEBI:57540 phylo 22432\n", + "... ... ... ... ...\n", + "495070 Z4YNJ9 CHEBI:15378 phylo 19721\n", + "495071 Z4YNJ9 CHEBI:15379 phylo 19721\n", + "495072 Z4YNJ9 CHEBI:57394 phylo 19721\n", + "495073 Z4YNJ9 C00154 phylo NaN\n", + "495074 Z4YNJ9 C00030 phylo NaN\n", "\n", - "[462406 rows x 3 columns]" + "[476866 rows x 4 columns]" ] }, - "execution_count": 17, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_UID_MID = pd.DataFrame(columns =[\"Uniprot ID\", \"molecule ID\", \"evidence\"])\n", + "df_UID_MID = pd.DataFrame(columns =[\"Uniprot ID\", \"molecule ID\", \"evidence\", \"RHEA ID\"])\n", "\n", "for ind in df_GO_UID.index:\n", " if ind >= -1:\n", " GO_ID = df_GO_UID[\"GO Term\"][ind]\n", + " try:\n", + " RHEA_ID = list(df_catalytic[\"RHEA ID\"].loc[df_catalytic[\"GO ID\"] == GO_ID])[0]\n", + " except:\n", + " RHEA_ID = np.nan\n", + " print(GO_ID)\n", " UID = df_GO_UID[\"Uniprot ID\"][ind]\n", " evidence = df_GO_UID[\"evidence\"][ind]\n", " met_IDs = list(df_substrates[\"molecule ID\"].loc[df_substrates[\"GO ID\"] == GO_ID])\n", " for met_ID in met_IDs:\n", " df_UID_MID = df_UID_MID.append({\"Uniprot ID\" : UID, \"molecule ID\" : met_ID,\n", - " \"evidence\": evidence}, ignore_index = True)\n", + " \"evidence\": evidence, \"RHEA ID\" : RHEA_ID}, ignore_index = True)\n", " if ind % 1000 ==1:\n", " print(ind)\n", " \n", @@ -2463,7 +2150,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2490,6 +2177,7 @@ " Uniprot ID\n", " molecule ID\n", " evidence\n", + " RHEA ID\n", " \n", " \n", " \n", @@ -2498,90 +2186,101 @@ " A0A009IHW8\n", " CHEBI:15377\n", " exp\n", + " 16301\n", " \n", " \n", " 1\n", " A0A009IHW8\n", " CHEBI:57540\n", " exp\n", + " 16301\n", " \n", " \n", " 10167\n", " A0A059TC02\n", " CHEBI:16731\n", " exp\n", + " 10620\n", " \n", " \n", " 10168\n", " A0A059TC02\n", " CHEBI:57287\n", " exp\n", + " 10620\n", " \n", " \n", " 10169\n", " A0A059TC02\n", " CHEBI:58349\n", " exp\n", + " 10620\n", " \n", " \n", " ...\n", " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 495053\n", " X5KCU3\n", " C00535\n", " exp\n", + " NaN\n", " \n", " \n", " 495054\n", " X5KCU9\n", " C00535\n", " exp\n", + " NaN\n", " \n", " \n", " 495055\n", " X5KJC0\n", " C00535\n", " exp\n", + " NaN\n", " \n", " \n", " 495056\n", " X5L1L5\n", " C00535\n", " exp\n", + " NaN\n", " \n", " \n", " 495057\n", " X5L565\n", " C00535\n", " exp\n", + " NaN\n", " \n", " \n", "\n", - "

28112 rows × 3 columns

\n", + "

29603 rows × 4 columns

\n", "" ], "text/plain": [ - " Uniprot ID molecule ID evidence\n", - "0 A0A009IHW8 CHEBI:15377 exp\n", - "1 A0A009IHW8 CHEBI:57540 exp\n", - "10167 A0A059TC02 CHEBI:16731 exp\n", - "10168 A0A059TC02 CHEBI:57287 exp\n", - "10169 A0A059TC02 CHEBI:58349 exp\n", - "... ... ... ...\n", - "495053 X5KCU3 C00535 exp\n", - "495054 X5KCU9 C00535 exp\n", - "495055 X5KJC0 C00535 exp\n", - "495056 X5L1L5 C00535 exp\n", - "495057 X5L565 C00535 exp\n", + " Uniprot ID molecule ID evidence RHEA ID\n", + "0 A0A009IHW8 CHEBI:15377 exp 16301\n", + "1 A0A009IHW8 CHEBI:57540 exp 16301\n", + "10167 A0A059TC02 CHEBI:16731 exp 10620\n", + "10168 A0A059TC02 CHEBI:57287 exp 10620\n", + "10169 A0A059TC02 CHEBI:58349 exp 10620\n", + "... ... ... ... ...\n", + "495053 X5KCU3 C00535 exp NaN\n", + "495054 X5KCU9 C00535 exp NaN\n", + "495055 X5KJC0 C00535 exp NaN\n", + "495056 X5L1L5 C00535 exp NaN\n", + "495057 X5L565 C00535 exp NaN\n", "\n", - "[28112 rows x 3 columns]" + "[29603 rows x 4 columns]" ] }, - "execution_count": 18, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -2590,6 +2289,26 @@ "df_UID_MID.loc[df_UID_MID[\"evidence\"] == \"exp\"]" ] }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(476866, 29603)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df_UID_MID), len(df_UID_MID.loc[df_UID_MID[\"evidence\"] == \"exp\"])" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2599,7 +2318,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -2626,6 +2345,7 @@ " Uniprot ID\n", " molecule ID\n", " evidence\n", + " RHEA ID\n", " \n", " \n", " \n", @@ -2634,90 +2354,101 @@ " A0A009IHW8\n", " CHEBI:57540\n", " exp\n", + " 16301\n", " \n", " \n", " 2\n", " A0A022PMU5\n", " C00030\n", " phylo\n", + " NaN\n", " \n", " \n", " 3\n", " A0A022PN36\n", " CHEBI:57318\n", " phylo\n", + " 22432\n", " \n", " \n", " 4\n", " A0A022PN36\n", " CHEBI:57540\n", " phylo\n", + " 22432\n", " \n", " \n", - " 6\n", + " 5\n", " A0A022PN36\n", - " CHEBI:16469\n", + " CHEBI:57318\n", " phylo\n", + " 16105\n", " \n", " \n", " ...\n", " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 495066\n", " Z4YHZ5\n", " CHEBI:16810\n", " phylo\n", + " 18945\n", " \n", " \n", " 495067\n", " Z4YHZ5\n", " CHEBI:50342\n", " phylo\n", + " 18945\n", " \n", " \n", " 495072\n", " Z4YNJ9\n", " CHEBI:57394\n", " phylo\n", + " 19721\n", " \n", " \n", " 495073\n", " Z4YNJ9\n", " C00154\n", " phylo\n", + " NaN\n", " \n", " \n", " 495074\n", " Z4YNJ9\n", " C00030\n", " phylo\n", + " NaN\n", " \n", " \n", "\n", - "

369461 rows × 3 columns

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378482 rows × 4 columns

\n", "" ], "text/plain": [ - " Uniprot ID molecule ID evidence\n", - "1 A0A009IHW8 CHEBI:57540 exp\n", - "2 A0A022PMU5 C00030 phylo\n", - "3 A0A022PN36 CHEBI:57318 phylo\n", - "4 A0A022PN36 CHEBI:57540 phylo\n", - "6 A0A022PN36 CHEBI:16469 phylo\n", - "... ... ... ...\n", - "495066 Z4YHZ5 CHEBI:16810 phylo\n", - "495067 Z4YHZ5 CHEBI:50342 phylo\n", - "495072 Z4YNJ9 CHEBI:57394 phylo\n", - "495073 Z4YNJ9 C00154 phylo\n", - "495074 Z4YNJ9 C00030 phylo\n", + " Uniprot ID molecule ID evidence RHEA ID\n", + "1 A0A009IHW8 CHEBI:57540 exp 16301\n", + "2 A0A022PMU5 C00030 phylo NaN\n", + "3 A0A022PN36 CHEBI:57318 phylo 22432\n", + "4 A0A022PN36 CHEBI:57540 phylo 22432\n", + "5 A0A022PN36 CHEBI:57318 phylo 16105\n", + "... ... ... ... ...\n", + "495066 Z4YHZ5 CHEBI:16810 phylo 18945\n", + "495067 Z4YHZ5 CHEBI:50342 phylo 18945\n", + "495072 Z4YNJ9 CHEBI:57394 phylo 19721\n", + "495073 Z4YNJ9 C00154 phylo NaN\n", + "495074 Z4YNJ9 C00030 phylo NaN\n", "\n", - "[369461 rows x 3 columns]" + "[378482 rows x 4 columns]" ] }, - "execution_count": 19, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -2736,6 +2467,134 @@ "df_UID_MID" ] }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(378482, 23384)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df_UID_MID), len(df_UID_MID.loc[df_UID_MID[\"evidence\"] == \"exp\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6219" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "29603-23384" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19270" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df_UID_MID.loc[df_UID_MID[\"evidence\"] == \"exp\"].loc[~pd.isnull(df_UID_MID[\"RHEA ID\"])])" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "df_UID_MID_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\", \"df_UID_MID_train.pkl\" ))\n", + "df_UID_MID_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_substrate_data\", \"df_UID_MID_test.pkl\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15051" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_UID_MID2 = df_UID_MID.loc[df_UID_MID[\"evidence\"] == \"exp\"]\n", + "df = df_UID_MID_train.loc[df_UID_MID_train[\"evidence\"] == \"exp\"]\n", + "df.drop(columns = \"evidence\", inplace = True)\n", + "df_train = df.merge(df_UID_MID2, on = [\"Uniprot ID\", \"molecule ID\"], how = \"left\")\n", + "\n", + "df = df_UID_MID_test.loc[df_UID_MID_test[\"evidence\"] == \"exp\"]\n", + "df.drop(columns = \"evidence\", inplace = True)\n", + "df_test = df.merge(df_UID_MID2, on = [\"Uniprot ID\", \"molecule ID\"], how = \"left\")\n", + "\n", + "len(df_train.loc[~pd.isnull(df_train[\"RHEA ID\"])]) +len(df_test.loc[~pd.isnull(df_test[\"RHEA ID\"])])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "200634" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_UID_MID2 = df_UID_MID.loc[df_UID_MID[\"evidence\"] == \"phylo\"]\n", + "df = df_UID_MID_train.loc[df_UID_MID_train[\"evidence\"] == \"phylo\"]\n", + "df.drop(columns = \"evidence\", inplace = True)\n", + "df_train = df.merge(df_UID_MID2, on = [\"Uniprot ID\", \"molecule ID\"], how = \"left\")\n", + "\n", + "df = df_UID_MID_test.loc[df_UID_MID_test[\"evidence\"] == \"phylo\"]\n", + "df.drop(columns = \"evidence\", inplace = True)\n", + "df_test = df.merge(df_UID_MID2, on = [\"Uniprot ID\", \"molecule ID\"], how = \"left\")\n", + "\n", + "len(df_train.loc[~pd.isnull(df_train[\"RHEA ID\"])]) +len(df_test.loc[~pd.isnull(df_test[\"RHEA ID\"])])" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2897,7 +2756,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -2950,232 +2809,59 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# cluster the fasta files\n", - "cluster_folder = join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_data\", 'clusters')\n", - "start_folder = cluster_folder\n", - "cluster_all_levels(start_folder, \n", - " cluster_folder, \n", - " filename='all_sequences')" - ] - }, - { - "cell_type": "code", - "execution_count": 29, + "execution_count": 53, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " cluster\n", - "count 230802.000000\n", - "mean 61688.071425\n", - "std 37173.511476\n", - "min 0.000000\n", - "25% 29925.250000\n", - "50% 59270.000000\n", - "75% 92366.000000\n", - "max 133172.000000" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "cd-hit -i C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences.fasta -o C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_100.fasta -c 1.0 -n 5 -T 1 -M 2000 -d 0\n", + "cd-hit -i C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_100.fasta -o C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_90.fasta -c 0.9 -n 5 -T 1 -M 2000 -d 0\n", + "cd-hit -i C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_90.fasta -o C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_80.fasta -c 0.8 -n 5 -T 1 -M 2000 -d 0\n", + "cd-hit -i C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_80.fasta -o C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_70.fasta -c 0.7 -n 5 -T 1 -M 2000 -d 0\n", + "cd-hit -i C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_70.fasta -o C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_60.fasta -c 0.6 -n 4 -T 1 -M 2000 -d 0\n", + "cd-hit -i C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_60.fasta -o C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_50.fasta -c 0.5 -n 3 -T 1 -M 2000 -d 0\n", + "cd-hit -i C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_50.fasta -o C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\\..\\data\\enzyme_data\\clusters\\all_sequences_clustered_sequences_40.fasta -c 0.4 -n 2 -T 1 -M 2000 -d 0\n" + ] + } + ], + "source": [ + "# cluster the fasta files\n", + "cluster_folder = join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_data\", 'clusters')\n", + "start_folder = cluster_folder\n", + "cluster_all_levels(start_folder, \n", + " cluster_folder, \n", + " filename='all_sequences')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ { - "data": { - "text/html": [ - "
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"execution_count": 36, + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "UNIPROT_df = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_data\", \"Uniprot_df.pkl\"))\n", + "df_UID_MID = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_substrate_data\", \"df_UID_MID.pkl\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "training_UIDs, testing_UIDs = list(set(df_UID_MID_train[\"Uniprot ID\"])), list(set(df_UID_MID_test[\"Uniprot ID\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ - "UNIPROT_df = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_data\", \"Uniprot_df_with_ESM1b.pkl\"))" + "df_UID_MID_train = df_UID_MID.loc[df_UID_MID[\"Uniprot ID\"].isin(training_UIDs)]\n", + "df_UID_MID_test = df_UID_MID.loc[df_UID_MID[\"Uniprot ID\"].isin(testing_UIDs)]" ] }, { @@ -4283,14 +3989,14 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ - "UIDs_with_rep = list(set(UNIPROT_df[\"Uniprot ID\"].loc[UNIPROT_df[\"ESM1b\"] != \"\"]))\n", + "#UIDs_with_rep = list(set(UNIPROT_df[\"Uniprot ID\"].loc[UNIPROT_df[\"ESM1b\"] != \"\"]))\n", "\n", - "df_UID_MID_train = df_UID_MID_train.loc[df_UID_MID_train[\"Uniprot ID\"].isin(UIDs_with_rep)]\n", - "df_UID_MID_test = df_UID_MID_test.loc[df_UID_MID_test[\"Uniprot ID\"].isin(UIDs_with_rep)]\n", + "#df_UID_MID_train = df_UID_MID_train.loc[df_UID_MID_train[\"Uniprot ID\"].isin(UIDs_with_rep)]\n", + "#df_UID_MID_test = df_UID_MID_test.loc[df_UID_MID_test[\"Uniprot ID\"].isin(UIDs_with_rep)]\n", "\n", "df_UID_MID_train = df_UID_MID_train.sample(frac = 1, random_state = 1).reset_index(drop = True)\n", "df_UID_MID_test = df_UID_MID_test.sample(frac = 1, random_state = 1).reset_index(drop = True)" @@ -4305,7 +4011,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -4321,20 +4027,20 @@ "df_UID_MID_test = df_UID_MID_test.loc[df_UID_MID_test[\"ECFP\"] != \"\"]\n", "df_UID_MID_train = df_UID_MID_train.loc[df_UID_MID_train[\"ECFP\"] != \"\"]\n", "\n", - "df_UID_MID_train.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\", \"df_UID_MID_train.pkl\"))\n", - "df_UID_MID_test.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_substrate_data\", \"df_UID_MID_test.pkl\"))" + "#df_UID_MID_train.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\", \"df_UID_MID_train.pkl\"))\n", + "#df_UID_MID_test.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_substrate_data\", \"df_UID_MID_test.pkl\"))" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "We have 274030 entries with phylogenetic evidence and 18351 entries with experimental evidence\n", + "We have 281104 entries with phylogenetic evidence and 19073 entries with experimental evidence\n", "\n", " experimental dataset:\n", "Number of different enzymes: 12156, Number of different substrates: 1379\n", @@ -4371,13 +4077,15 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ "df_chebi_to_inchi = pd.read_csv(join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data\", \"chebiID_to_inchi.tsv\"), sep = \"\\t\")\n", "mol_folder = \"C:\\\\Users\\\\alexk\\\\mol-files\\\\\"\n", "\n", + "count = 0\n", + "\n", "def get_mol(met_ID):\n", " is_CHEBI_ID = (met_ID[0:5] == \"CHEBI\")\n", " is_InChI = (met_ID[0:5] == \"InChI\")\n", @@ -4485,6 +4193,10 @@ " if lower_bound <0:\n", " break\n", " \n", + " if lower_bound < 0.7:\n", + " global count\n", + " count +=1\n", + " \n", " new_metabolites = random.sample(metabolites, min(3,len(metabolites)))\n", "\n", " for met in new_metabolites:\n", @@ -4506,7 +4218,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 103, "metadata": { "scrolled": true }, @@ -4520,443 +4232,458 @@ "Time: 0.0 [min]\n", "0\n", "100\n", - "Time: 0.05 [min]\n", - "3\n", + "Time: 0.08 [min]\n", + "0\n", "200\n", - "Time: 0.11 [min]\n", - "8\n", - "300\n", "Time: 0.17 [min]\n", - "11\n", + "3\n", + "300\n", + "Time: 0.27 [min]\n", + "7\n", "400\n", - "Time: 0.23 [min]\n", - "20\n", + "Time: 0.34 [min]\n", + "18\n", "500\n", - "Time: 0.29 [min]\n", - "32\n", + "Time: 0.41 [min]\n", + "33\n", "600\n", - "Time: 0.35 [min]\n", - "44\n", + "Time: 0.48 [min]\n", + "51\n", "700\n", - "Time: 0.4 [min]\n", - "59\n", + "Time: 0.55 [min]\n", + "66\n", "800\n", - "Time: 0.46 [min]\n", - "71\n", + "Time: 0.63 [min]\n", + "80\n", "900\n", - "Time: 0.52 [min]\n", - "88\n", + "Time: 0.7 [min]\n", + "94\n", "1000\n", - "Time: 0.57 [min]\n", - "104\n", + "Time: 0.79 [min]\n", + "112\n", "1100\n", - "Time: 0.63 [min]\n", - "117\n", + "Time: 0.87 [min]\n", + "124\n", "1200\n", - "Time: 0.69 [min]\n", - "133\n", + "Time: 0.96 [min]\n", + "140\n", "1300\n", - "Time: 0.75 [min]\n", - "145\n", + "Time: 1.04 [min]\n", + "151\n", "1400\n", - "Time: 0.81 [min]\n", - "154\n", + "Time: 1.13 [min]\n", + "163\n", "1500\n", - "Time: 0.87 [min]\n", - "168\n", + "Time: 1.23 [min]\n", + "173\n", "1600\n", - "Time: 0.93 [min]\n", - "184\n", + "Time: 1.31 [min]\n", + "183\n", "1700\n", - "Time: 0.99 [min]\n", - "206\n", + "Time: 1.41 [min]\n", + "191\n", "1800\n", - "Time: 1.05 [min]\n", - "215\n", + "Time: 1.5 [min]\n", + "202\n", "1900\n", - "Time: 1.1 [min]\n", - "222\n", + "Time: 1.59 [min]\n", + "215\n", "2000\n", - "Time: 1.16 [min]\n", - "234\n", + "Time: 1.66 [min]\n", + "227\n", "2100\n", - "Time: 1.22 [min]\n", - "248\n", + "Time: 1.75 [min]\n", + "236\n", "2200\n", - "Time: 1.28 [min]\n", - "257\n", + "Time: 1.84 [min]\n", + "240\n", "2300\n", - "Time: 1.34 [min]\n", - "263\n", + "Time: 1.93 [min]\n", + "246\n", "2400\n", - "Time: 1.41 [min]\n", - "280\n", + "Time: 2.02 [min]\n", + "264\n", "2500\n", - "Time: 1.47 [min]\n", - "294\n", + "Time: 2.11 [min]\n", + "274\n", "2600\n", - "Time: 1.53 [min]\n", - "308\n", + "Time: 2.2 [min]\n", + "288\n", "2700\n", - "Time: 1.59 [min]\n", - "316\n", + "Time: 2.28 [min]\n", + "297\n", "2800\n", - "Time: 1.65 [min]\n", - "329\n", + "Time: 2.36 [min]\n", + "303\n", "2900\n", - "Time: 1.71 [min]\n", - "337\n", + "Time: 2.44 [min]\n", + "309\n", "3000\n", - "Time: 1.77 [min]\n", - "347\n", + "Time: 2.52 [min]\n", + "327\n", "3100\n", - "Time: 1.83 [min]\n", - "361\n", + "Time: 2.62 [min]\n", + "342\n", "3200\n", - "Time: 1.9 [min]\n", - "373\n", + "Time: 2.7 [min]\n", + "356\n", "3300\n", - "Time: 1.98 [min]\n", - "382\n", + "Time: 2.79 [min]\n", + "368\n", "3400\n", - "Time: 2.05 [min]\n", - "396\n", + "Time: 2.88 [min]\n", + "386\n", "3500\n", - "Time: 2.11 [min]\n", - "405\n", + "Time: 2.97 [min]\n", + "401\n", "3600\n", - "Time: 2.17 [min]\n", - "420\n", + "Time: 3.06 [min]\n", + "409\n", "3700\n", - "Time: 2.23 [min]\n", - "428\n", + "Time: 3.15 [min]\n", + "421\n", "3800\n", - "Time: 2.3 [min]\n", - "435\n", + "Time: 3.24 [min]\n", + "425\n", "3900\n", - "Time: 2.37 [min]\n", - "443\n", + "Time: 3.36 [min]\n", + "449\n", "4000\n", - "Time: 2.43 [min]\n", - "454\n", + "Time: 3.45 [min]\n", + "456\n", "4100\n", - "Time: 2.49 [min]\n", - "465\n", + "Time: 3.53 [min]\n", + "462\n", "4200\n", - 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"Time: 3.39 [min]\n", - "593\n", + "Time: 4.83 [min]\n", + "587\n", "5600\n", - "Time: 3.46 [min]\n", - "605\n", + "Time: 4.92 [min]\n", + "598\n", "5700\n", - "Time: 3.52 [min]\n", - "611\n", + "Time: 5.02 [min]\n", + "609\n", "5800\n", - "Time: 3.58 [min]\n", - "623\n", + "Time: 5.12 [min]\n", + "617\n", "5900\n", - "Time: 3.65 [min]\n", - "637\n", + "Time: 5.22 [min]\n", + "629\n", "6000\n", - "Time: 3.72 [min]\n", - "652\n", + "Time: 5.31 [min]\n", + "636\n", "6100\n", - "Time: 3.78 [min]\n", - "664\n", + "Time: 5.41 [min]\n", + "643\n", "6200\n", - "Time: 3.85 [min]\n", - "669\n", + "Time: 5.5 [min]\n", + "647\n", "6300\n", - "Time: 3.91 [min]\n", - "679\n", + "Time: 5.6 [min]\n", + "655\n", "6400\n", - "Time: 3.97 [min]\n", - "688\n", + "Time: 5.7 [min]\n", + "660\n", "6500\n", - "Time: 4.04 [min]\n", - "694\n", + "Time: 5.8 [min]\n", + "666\n", "6600\n", - "Time: 4.1 [min]\n", - "699\n", + "Time: 5.9 [min]\n", + "682\n", "6700\n", - "Time: 4.17 [min]\n", - "709\n", + "Time: 6.01 [min]\n", + "698\n", "6800\n", - 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"Time: 9.91 [min]\n", - "1226\n", + "Time: 14.35 [min]\n", + "1206\n", "14400\n", - "Time: 10.02 [min]\n", - "1228\n", + "Time: 14.47 [min]\n", + "1210\n", "14500\n", - "Time: 10.12 [min]\n", - "1234\n", + "Time: 14.6 [min]\n", + "1213\n", "14600\n", - "Time: 10.23 [min]\n", - "1236\n" + "Time: 14.75 [min]\n", + "1218\n", + "14700\n", + "Time: 14.89 [min]\n", + "1226\n", + "14800\n", + "Time: 15.03 [min]\n", + "1230\n", + "14900\n", + "Time: 15.17 [min]\n", + "1231\n", + "15000\n", + "Time: 15.35 [min]\n", + "1238\n", + "15100\n", + "Time: 15.5 [min]\n", + "1243\n" ] }, { @@ -4983,6 +4710,7 @@ " Uniprot ID\n", " molecule ID\n", " evidence\n", + " RHEA ID\n", " ECFP\n", " Binding\n", " type\n", @@ -4991,46 +4719,51 @@ " \n", " \n", " 0\n", - " Q5B2F7\n", - " CHEBI:57344\n", + " P51567\n", + " C00002\n", " exp\n", - " 0100000001000000000000000000000001000000000000...\n", + " NaN\n", + " 0000000001000000000000000000000000000000000000...\n", " 1\n", " NaN\n", " \n", " \n", " 1\n", - " Q9SAH9\n", - " CHEBI:58349\n", + " P08539\n", + " CHEBI:37565\n", " exp\n", - " 0000000001000000100000100000000000000000000000...\n", + " 19669\n", + " 0000001000000000000000000000000000000000000000...\n", " 1\n", " NaN\n", " \n", " \n", " 2\n", - " Q8IPJ6\n", - " CHEBI:57776\n", + " Q84WW2\n", + " CHEBI:57955\n", " exp\n", - " 0000000000000000000000000000010001000000000000...\n", + " 12556\n", + " 0000000000000100000000000000000000000000000000...\n", " 1\n", " NaN\n", " \n", " \n", " 3\n", - " A0A1D5PCZ1\n", + " P33279\n", " C00002\n", " exp\n", + " NaN\n", " 0000000001000000000000000000000000000000000000...\n", " 1\n", " NaN\n", " \n", " \n", " 4\n", - " O22765\n", - " CHEBI:33384\n", + " Q8JGL9\n", + " CHEBI:456216\n", " exp\n", - " 0100000000000000000000000000000000000000000000...\n", + " 18159\n", + " 0000000001000000000000000000000000000000000000...\n", " 1\n", " NaN\n", " \n", @@ -5042,47 +4775,53 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", - " 55413\n", - " P43123\n", - " CHEBI:30616\n", + " 57646\n", + " P09884\n", + " C00002\n", + " NaN\n", " NaN\n", " NaN\n", " 0\n", " NaN\n", " \n", " \n", - " 55414\n", - " Q8RVK9\n", - " C00002\n", + " 57647\n", + " P09884\n", + " CHEBI:30616\n", + " NaN\n", " NaN\n", " NaN\n", " 0\n", " NaN\n", " \n", " \n", - " 55415\n", - " Q8RVK9\n", - " CHEBI:30616\n", + " 57648\n", + " P39177\n", + " CHEBI:29919\n", + " NaN\n", " NaN\n", " NaN\n", " 0\n", " NaN\n", " \n", " \n", - " 55416\n", - " Q62730\n", - " CHEBI:30616\n", + " 57649\n", + " P39177\n", + " C00001\n", + " NaN\n", " NaN\n", " NaN\n", " 0\n", " NaN\n", " \n", " \n", - " 55417\n", - " Q62730\n", - " C00002\n", + " 57650\n", + " P39177\n", + " CHEBI:16240\n", + " NaN\n", " NaN\n", " NaN\n", " 0\n", @@ -5090,40 +4829,40 @@ " \n", " \n", "\n", - "

55418 rows × 6 columns

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57651 rows × 7 columns

\n", "" ], "text/plain": [ - " Uniprot ID molecule ID evidence \\\n", - "0 Q5B2F7 CHEBI:57344 exp \n", - "1 Q9SAH9 CHEBI:58349 exp \n", - "2 Q8IPJ6 CHEBI:57776 exp \n", - "3 A0A1D5PCZ1 C00002 exp \n", - "4 O22765 CHEBI:33384 exp \n", - "... ... ... ... \n", - "55413 P43123 CHEBI:30616 NaN \n", - "55414 Q8RVK9 C00002 NaN \n", - "55415 Q8RVK9 CHEBI:30616 NaN \n", - "55416 Q62730 CHEBI:30616 NaN \n", - "55417 Q62730 C00002 NaN \n", + " Uniprot ID molecule ID evidence RHEA ID \\\n", + "0 P51567 C00002 exp NaN \n", + "1 P08539 CHEBI:37565 exp 19669 \n", + "2 Q84WW2 CHEBI:57955 exp 12556 \n", + "3 P33279 C00002 exp NaN \n", + "4 Q8JGL9 CHEBI:456216 exp 18159 \n", + "... ... ... ... ... \n", + "57646 P09884 C00002 NaN NaN \n", + "57647 P09884 CHEBI:30616 NaN NaN \n", + "57648 P39177 CHEBI:29919 NaN NaN \n", + "57649 P39177 C00001 NaN NaN \n", + "57650 P39177 CHEBI:16240 NaN NaN \n", "\n", " ECFP Binding type \n", - "0 0100000001000000000000000000000001000000000000... 1 NaN \n", - "1 0000000001000000100000100000000000000000000000... 1 NaN \n", - "2 0000000000000000000000000000010001000000000000... 1 NaN \n", + "0 0000000001000000000000000000000000000000000000... 1 NaN \n", + "1 0000001000000000000000000000000000000000000000... 1 NaN \n", + "2 0000000000000100000000000000000000000000000000... 1 NaN \n", "3 0000000001000000000000000000000000000000000000... 1 NaN \n", - "4 0100000000000000000000000000000000000000000000... 1 NaN \n", + "4 0000000001000000000000000000000000000000000000... 1 NaN \n", "... ... ... ... \n", - "55413 NaN 0 NaN \n", - "55414 NaN 0 NaN \n", - "55415 NaN 0 NaN \n", - "55416 NaN 0 NaN \n", - "55417 NaN 0 NaN \n", + "57646 NaN 0 NaN \n", + "57647 NaN 0 NaN \n", + "57648 NaN 0 NaN \n", + "57649 NaN 0 NaN \n", + "57650 NaN 0 NaN \n", "\n", - "[55418 rows x 6 columns]" + "[57651 rows x 7 columns]" ] }, - "execution_count": 95, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -5144,6 +4883,26 @@ "df_UID_MID_train_exp" ] }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.18952277759457295" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(count)/len(df_UID_MID_train_exp.loc[df_UID_MID_train_exp[\"Binding\"] == 0])" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -12274,27 +12033,241 @@ ] }, { - "cell_type": "markdown", + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Adding task-specific enzyme representations (extra token)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (a) Creating input data for training the task-specific ESM1b vectors:" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "df_UID_MID_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\", \"df_UID_MID_train.pkl\" ))\n", + "df_UID_MID_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_substrate_data\", \"df_UID_MID_test.pkl\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Uniprot IDmolecule IDevidenceECFP
18Q921A4C00030exp0000000000000000010000000000000000000000000000...
22Q5B2F7CHEBI:57344exp0100000001000000000000000000000001000000000000...
59Q9SAH9CHEBI:58349exp0000000001000000100000100000000000000000000000...
135Q8IPJ6CHEBI:57776exp0000000000000000000000000000010001000000000000...
160A0A1D5PCZ1C00002exp0000000001000000000000000000000000000000000000...
...............
289967P04152C00677exp0000000000000000001000000000000000000000000000...
289976Q4Q1I5CHEBI:16810exp0000000000000000000000000000000010000000000000...
289995P43123CHEBI:46398exp0000000000000000000000000000000000000000000000...
290031Q8RVK9CHEBI:57384exp0100000001000000000000000000000001000000000000...
290040Q62730CHEBI:57540exp0000000001000000000000100000000000000000000000...
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14778 rows × 4 columns

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" + ], + "text/plain": [ + " Uniprot ID molecule ID evidence \\\n", + "18 Q921A4 C00030 exp \n", + "22 Q5B2F7 CHEBI:57344 exp \n", + "59 Q9SAH9 CHEBI:58349 exp \n", + "135 Q8IPJ6 CHEBI:57776 exp \n", + "160 A0A1D5PCZ1 C00002 exp \n", + "... ... ... ... \n", + "289967 P04152 C00677 exp \n", + "289976 Q4Q1I5 CHEBI:16810 exp \n", + "289995 P43123 CHEBI:46398 exp \n", + "290031 Q8RVK9 CHEBI:57384 exp \n", + "290040 Q62730 CHEBI:57540 exp \n", + "\n", + " ECFP \n", + "18 0000000000000000010000000000000000000000000000... \n", + "22 0100000001000000000000000000000001000000000000... \n", + "59 0000000001000000100000100000000000000000000000... \n", + "135 0000000000000000000000000000010001000000000000... \n", + "160 0000000001000000000000000000000000000000000000... \n", + "... ... \n", + "289967 0000000000000000001000000000000000000000000000... \n", + "289976 0000000000000000000000000000000010000000000000... \n", + "289995 0000000000000000000000000000000000000000000000... \n", + "290031 0100000001000000000000000000000001000000000000... \n", + "290040 0000000001000000000000100000000000000000000000... \n", + "\n", + "[14778 rows x 4 columns]" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_UID_MID_train.loc[df_UID_MID_train[\"evidence\"] == \"exp\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 119, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1197" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## 7. Adding task-specific enzyme representations" + "len(set(list(df_UID_MID_train[\"ECFP\"].loc[df_UID_MID_train[\"evidence\"] == \"exp\"]) +list(df_UID_MID_test[\"ECFP\"].loc[df_UID_MID_test[\"evidence\"] == \"exp\"])))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 120, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1379" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### (a) Creating input data for training the task-specific ESM1b vectors:" + "len(set(list(df_UID_MID_train[\"molecule ID\"].loc[df_UID_MID_train[\"evidence\"] == \"exp\"]) +list(df_UID_MID_test[\"molecule ID\"].loc[df_UID_MID_test[\"evidence\"] == \"exp\"])))" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 122, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "182" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "df_UID_MID_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\", \"df_UID_MID_train.pkl\" ))\n", - "df_UID_MID_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_substrate_data\", \"df_UID_MID_test.pkl\"))" + "1379 - 1197" ] }, { @@ -12331,12 +12304,19 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "UNIPROT_df = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_data\", \"Uniprot_df_with_ESM1b.pkl\"))\n", - "UNIPROT_df.drop(columns = \"ESM1b\", inplace = True)" + "UNIPROT_df = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_data\", \"UNIPROT_df.pkl\"))\n", + "ofile = open(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_data\", \"all_sequences.fasta\"), \"w\")\n", + "for ind in UNIPROT_df.index:\n", + " seq = UNIPROT_df[\"Sequence\"][ind]\n", + " if not pd.isnull(seq):\n", + " seq_end = seq.find(\"#\")\n", + " seq = seq[:seq_end]\n", + " ofile.write(\">\" + str(ind) + \"\\n\" + seq[:1018] + \"\\n\")\n", + "ofile.close()" ] }, { @@ -12563,11 +12543,269 @@ "df_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"df_train_with_ESM1b_ts_GNN.pkl\"))\n", "df_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"splits\", \"df_test_with_ESM1b_ts_GNN.pkl\"))" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. Adding task-specific enzyme representations (mean representations)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Uniprot IDSequenceESM1b_ts_mean
0G3QP07MAAGAGTAGLASGPGVVRDPAASQPRKRPGREGGEGARRSDTMAGG...[-0.72636825, 0.52454716, -0.5797731, -0.24976...
1Q9KNU3MRNTMFTSKEGQTIPQVTFPTRQGDAWVNVTSDELFKGKTVIVFSL...[-0.25921354, 0.2036142, -0.23848991, -0.14593...
2H3HBD2MYPSVALLLWLWGAGVALQVIGLLGCLVEQNDTSDCASTARDRRTS...[-0.8491672, 0.34376088, -1.1393323, 0.3514062...
3A0A3B6C0N2MEYHRVVSLVAVVVVLLRRWPALSSAQAPVSRTITVDSHGGGDFSS...[-0.27494934, 0.5831903, -0.47037718, -0.17670...
4A0A1Z5RRB7MDMPSHTHSQLCESKALVASYTQEARKRNQQHNMASKPGPLSRWPW...[0.13033693, -0.873903, -0.16711213, -0.122451...
............
230809A0A072VJI1MELSAVTLGGKGSSLSSSAVYATAIGKSQIKIDSSALDRLTSPPSS...[0.46406618, 0.14648609, -0.34433103, 0.074277...
230810D7SVZ9MFIESFKVESPNVKYTENEIHSVYDYETTELVHENRNGTYQWVVKP...[0.06923403, -0.5674761, -0.27717096, 0.625875...
230811A0A2C9WL07MAPISILLFSSILLFSASSTGRALSFNYYEKTCPDVELIVTNAVKN...[-0.64768815, 0.81601745, 0.12785931, -0.55637...
230812F4ICB6MGCVNSKQTVSVTPAIDHSGVFRDNVCSGSGRIVVEDLPPVTETKL...[0.054608203, 1.0352598, 0.7369152, -1.6918645...
230813B5LAU7MAAAATCAFFPTGNPPSDSGAKSSGNLGGGSVPGSIDARGLNNVKK...[0.2115751, 0.41601294, -0.66078496, 0.3504067...
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230814 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " Uniprot ID Sequence \\\n", + "0 G3QP07 MAAGAGTAGLASGPGVVRDPAASQPRKRPGREGGEGARRSDTMAGG... \n", + "1 Q9KNU3 MRNTMFTSKEGQTIPQVTFPTRQGDAWVNVTSDELFKGKTVIVFSL... \n", + "2 H3HBD2 MYPSVALLLWLWGAGVALQVIGLLGCLVEQNDTSDCASTARDRRTS... \n", + "3 A0A3B6C0N2 MEYHRVVSLVAVVVVLLRRWPALSSAQAPVSRTITVDSHGGGDFSS... \n", + "4 A0A1Z5RRB7 MDMPSHTHSQLCESKALVASYTQEARKRNQQHNMASKPGPLSRWPW... \n", + "... ... ... \n", + "230809 A0A072VJI1 MELSAVTLGGKGSSLSSSAVYATAIGKSQIKIDSSALDRLTSPPSS... \n", + "230810 D7SVZ9 MFIESFKVESPNVKYTENEIHSVYDYETTELVHENRNGTYQWVVKP... \n", + "230811 A0A2C9WL07 MAPISILLFSSILLFSASSTGRALSFNYYEKTCPDVELIVTNAVKN... \n", + "230812 F4ICB6 MGCVNSKQTVSVTPAIDHSGVFRDNVCSGSGRIVVEDLPPVTETKL... \n", + "230813 B5LAU7 MAAAATCAFFPTGNPPSDSGAKSSGNLGGGSVPGSIDARGLNNVKK... \n", + "\n", + " ESM1b_ts_mean \n", + "0 [-0.72636825, 0.52454716, -0.5797731, -0.24976... \n", + "1 [-0.25921354, 0.2036142, -0.23848991, -0.14593... \n", + "2 [-0.8491672, 0.34376088, -1.1393323, 0.3514062... \n", + "3 [-0.27494934, 0.5831903, -0.47037718, -0.17670... \n", + "4 [0.13033693, -0.873903, -0.16711213, -0.122451... \n", + "... ... \n", + "230809 [0.46406618, 0.14648609, -0.34433103, 0.074277... \n", + "230810 [0.06923403, -0.5674761, -0.27717096, 0.625875... \n", + "230811 [-0.64768815, 0.81601745, 0.12785931, -0.55637... \n", + "230812 [0.054608203, 1.0352598, 0.7369152, -1.6918645... \n", + "230813 [0.2115751, 0.41601294, -0.66078496, 0.3504067... \n", + "\n", + "[230814 rows x 3 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"df_train_with_ESM1b_ts_GNN.pkl\"))\n", + "df_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"splits\", \"df_test_with_ESM1b_ts_GNN.pkl\"))\n", + "\n", + "rep_dict = torch.load(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_data\", \"all_sequences_esm1b_ts_mean.pt\"))\n", + "\n", + "UNIPROT_df[\"ESM1b_ts_mean\"] = \"\"\n", + "\n", + "for ind in UNIPROT_df.index:\n", + " try:\n", + " UNIPROT_df[\"ESM1b_ts_mean\"][ind] = rep_dict[str(ind) +\".pt\"]\n", + " except:\n", + " print(ind)\n", + "UNIPROT_df" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "Uniprot_df = pd.DataFrame(data = {\"Uniprot ID\" : UNIPROT_df[\"Uniprot ID\"],\n", + " \"ESM1b_ts_mean\" : UNIPROT_df[\"ESM1b_ts_mean\"]})\n", + "\n", + "df_train = df_train.merge(Uniprot_df, on = \"Uniprot ID\", how = \"left\")\n", + "df_test = df_test.merge(Uniprot_df, on = \"Uniprot ID\", how = \"left\")\n", + "\n", + "\n", + "df_train.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"df_train_with_ESM1b_ts_GNN.pkl\"))\n", + "df_test.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"splits\", \"df_test_with_ESM1b_ts_GNN.pkl\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 10. Adding ECFP vectors of different dimensions:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "df_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"df_train_with_ESM1b_ts_GNN.pkl\"))\n", + "df_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"splits\", \"df_test_with_ESM1b_ts_GNN.pkl\"))\n", + "\n", + "df_ecfps = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data\", \"df_ecfps.pkl\"))\n", + "df_chebi_to_inchi = pd.read_csv(join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data\", \"chebiID_to_inchi.tsv\"), sep = \"\\t\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "df_ecfps[\"ECFP_512\"] = \"\"\n", + "df_ecfps[\"ECFP_2048\"] = \"\"\n", + "\n", + "for ind in df_ecfps.index:\n", + " met_ID = df_ecfps[\"substrate ID\"][ind]\n", + " is_CHEBI_ID = (met_ID[0:5] == \"CHEBI\")\n", + " \n", + " \n", + " if is_CHEBI_ID:\n", + " try:\n", + " ID = int(met_ID.split(\" \")[0].split(\":\")[-1])\n", + " Inchi = list(df_chebi_to_inchi[\"Inchi\"].loc[df_chebi_to_inchi[\"ChEBI\"] == float(ID)])[0]\n", + " if not pd.isnull(Inchi):\n", + " mol = Chem.inchi.MolFromInchi(Inchi)\n", + " except IndexError:\n", + " mol = None\n", + " \n", + " else:\n", + " try:\n", + " mol = Chem.MolFromMolFile(mol_folder + \"/mol-files/\" + met_ID + '.mol')\n", + " except OSError:\n", + " None\n", + " \n", + " if mol is not None:\n", + " ecfp = AllChem.GetMorganFingerprintAsBitVect(mol, 3, nBits=512).ToBitString()\n", + " df_ecfps[\"ECFP_512\"][ind] = ecfp\n", + " \n", + " ecfp = AllChem.GetMorganFingerprintAsBitVect(mol, 3, nBits=2048).ToBitString()\n", + " df_ecfps[\"ECFP_2048\"][ind] = ecfp" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "df_train.drop(columns = [\"ECFP\"], inplace = True)\n", + "df_train = df_train.merge(df_ecfps, on = \"substrate ID\", how = \"left\")\n", + "\n", + "df_test.drop(columns = [\"ECFP\"], inplace = True)\n", + "df_test = df_test.merge(df_ecfps, on = \"substrate ID\", how = \"left\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "df_train.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"df_train_with_ESM1b_ts_GNN.pkl\"))\n", + "df_test.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"splits\", \"df_test_with_ESM1b_ts_GNN.pkl\"))" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -12581,7 +12819,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.13" } }, "nbformat": 4, diff --git a/notebooks_and_code/1_1 - Creating enzyme-substrate database from GOA database - Additional datasets (for re-traing ESM-1b).ipynb b/notebooks_and_code/1_1 - Creating enzyme-substrate database from GOA database - Additional datasets (for re-traing ESM-1b).ipynb new file mode 100644 index 0000000..2afbd26 --- /dev/null +++ b/notebooks_and_code/1_1 - Creating enzyme-substrate database from GOA database - Additional datasets (for re-traing ESM-1b).ipynb @@ -0,0 +1,8076 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import random\n", + "from os.path import join\n", + "import os\n", + "import re\n", + "import sys\n", + "import time\n", + "from rdkit import Chem\n", + "from rdkit import DataStructs\n", + "from rdkit.Chem import AllChem\n", + "from Bio import SeqIO\n", + "import warnings\n", + "import torch\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "sys.path.append('.\\\\additional_code')\n", + "from data_preprocessing import *\n", + "\n", + "CURRENT_DIR = os.getcwd()\n", + "print(CURRENT_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding negative data points" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "UNIPROT_df = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_data\", \"UNIPROT_df.pkl\"))\n", + "\n", + "df_UID_MID_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\", \"df_UID_MID_train.pkl\"))\n", + "df_UID_MID_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"enzyme_substrate_data\", \"df_UID_MID_test.pkl\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We have 274030 entries with phylogenetic evidence and 18351 entries with experimental evidence\n", + "\n", + " experimental dataset:\n", + "Number of different enzymes: 12156, Number of different substrates: 1379\n", + "\n", + " phylogenetic dataset:\n", + "Number of different enzymes: 198259, Number of different substrates: 661\n" + ] + } + ], + "source": [ + "df_all = pd.concat([df_UID_MID_train, df_UID_MID_test], ignore_index = True)\n", + "\n", + "df_exp = df_all.loc[df_all[\"evidence\"] == \"exp\"]\n", + "df_phylo = df_all.loc[df_all[\"evidence\"] == \"phylo\"]\n", + "\n", + "print(\"We have %s entries with phylogenetic evidence and %s entries with experimental evidence\" % (len(df_phylo), len(df_exp)))\n", + "\n", + "print(\"\\n experimental dataset:\")\n", + "print(\"Number of different enzymes: %s, Number of different substrates: %s\"\n", + " % (len(set(df_exp[\"Uniprot ID\"])), len(set(df_exp[\"molecule ID\"]))) )\n", + "\n", + "print(\"\\n phylogenetic dataset:\")\n", + "print(\"Number of different enzymes: %s, Number of different substrates: %s\"\n", + " % (len(set(df_phylo[\"Uniprot ID\"])), len(set(df_phylo[\"molecule ID\"]))))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (a) Creating negative data points for the training and test set:\n", + "To assign the negative data points, we will choose similar metabolites compared to the substrate of the psoitive datapoints. Therefore, we are first creating a similarity matrix for all metabolites in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df_chebi_to_inchi = pd.read_csv(join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data\", \"chebiID_to_inchi.tsv\"), sep = \"\\t\")\n", + "mol_folder = \"C:\\\\Users\\\\alexk\\\\mol-files\\\\\"\n", + "\n", + "def get_mol(met_ID):\n", + " is_CHEBI_ID = (met_ID[0:5] == \"CHEBI\")\n", + " is_InChI = (met_ID[0:5] == \"InChI\")\n", + " if is_CHEBI_ID:\n", + " try:\n", + " ID = int(met_ID.split(\" \")[0].split(\":\")[-1])\n", + " Inchi = list(df_chebi_to_inchi[\"Inchi\"].loc[df_chebi_to_inchi[\"ChEBI\"] == float(ID)])[0]\n", + " mol = Chem.inchi.MolFromInchi(Inchi)\n", + " except:\n", + " mol = None \n", + " elif is_InChI:\n", + " try:\n", + " mol = Chem.inchi.MolFromInchi(met_ID)\n", + " except:\n", + " mol = None\n", + " \n", + " else:\n", + " try:\n", + " mol = Chem.MolFromMolFile(mol_folder + \"mol-files\\\\\" + met_ID + '.mol')\n", + " except OSError:\n", + " mol = None\n", + " \n", + " return(mol)\n", + "\n", + "def drop_samples_without_mol_file(df):\n", + " droplist = []\n", + " for ind in df.index:\n", + " if get_mol(met_ID = df[\"molecule ID\"][ind]) is None:\n", + " droplist.append(ind)\n", + "\n", + " df.drop(droplist, inplace = True)\n", + " return(df)\n", + "\n", + "def get_metabolites_and_similarities(df):\n", + " df_metabolites = pd.DataFrame(data = {\"ECFP\": df[\"ECFP\"], \"ID\": df[\"molecule ID\"]})\n", + " df_metabolites = df_metabolites.drop_duplicates()\n", + " df_metabolites.reset_index(inplace = True, drop = True)\n", + "\n", + "\n", + " ms = [get_mol(met_ID = df_metabolites[\"ID\"][ind]) for ind in df_metabolites.index]\n", + " fps = [Chem.RDKFingerprint(x) for x in ms]\n", + "\n", + " similarity_matrix = np.zeros((len(ms), len(ms)))\n", + " for i in range(len(ms)):\n", + " for j in range(len(ms)):\n", + " similarity_matrix[i,j] = DataStructs.FingerprintSimilarity(fps[i],fps[j])\n", + " \n", + " return(df_metabolites, similarity_matrix)\n", + "\n", + "\n", + "\n", + "def get_valid_list(met_ID, UID, forbidden_metabolites, df_metabolites, similarity_matrix, lower_bound =0.7, upper_bound =0.9):\n", + " binding_met_IDs = list(df_UID_MID[\"molecule ID\"].loc[df_UID_MID[\"Uniprot ID\"] == UID])\n", + " k = df_metabolites.loc[df_metabolites[\"ID\"] == met_ID].index[0]\n", + "\n", + " similarities = similarity_matrix[k,:]\n", + " selection = (similarities< upper_bound) * (similarities >lower_bound) \n", + " metabolites = list(df_metabolites[\"ID\"].loc[selection])\n", + " \n", + " no_mets = list(set(binding_met_IDs + forbidden_metabolites))\n", + " \n", + " metabolites = [met for met in metabolites if (met not in no_mets)]\n", + " return(metabolites)\n", + "\n", + "\n", + "def create_negative_samples(df, df_metabolites, similarity_matrix):\n", + " start = time.time()\n", + " UID_list = []\n", + " MID_list = []\n", + " Type_list = []\n", + " forbidden_mets = []\n", + "\n", + " for ind in df.index:\n", + " if ind % 100 ==0:\n", + " print(ind)\n", + " print(\"Time: %s [min]\" % np.round(float((time.time()-start)/60),2))\n", + "\n", + " df2 = pd.DataFrame(data = {\"Uniprot ID\": UID_list, \"molecule ID\" : MID_list, \"type\" : Type_list})\n", + " df2[\"Binding\"] = 0\n", + " df = pd.concat([df, df2], ignore_index=True)\n", + "\n", + " UID_list, MID_list, Type_list = [], [], []\n", + "\n", + " forbidden_mets_old = forbidden_mets.copy()\n", + " all_mets = list(set(df[\"molecule ID\"]))\n", + " all_mets = [met for met in all_mets if not met in forbidden_mets_old]\n", + " forbidden_mets = list(set([met for met in all_mets if \n", + " (np.mean(df[\"Binding\"].loc[df[\"molecule ID\"] == met]) < 1/2)]))\n", + " forbidden_mets = forbidden_mets + forbidden_mets_old\n", + " print(len(forbidden_mets))\n", + "\n", + " UID = df[\"Uniprot ID\"][ind]\n", + " Type = df[\"type\"][ind]\n", + " met_ID = df[\"molecule ID\"][ind]\n", + "\n", + " metabolites = get_valid_list(met_ID = met_ID, UID = UID, forbidden_metabolites= forbidden_mets,\n", + " df_metabolites = df_metabolites, similarity_matrix = similarity_matrix,\n", + " lower_bound =0.7, upper_bound =0.95)\n", + " lower_bound = 0.7\n", + " while len(metabolites) < 2:\n", + " lower_bound = lower_bound - 0.2\n", + " metabolites = get_valid_list(met_ID = met_ID, UID = UID, forbidden_metabolites= forbidden_mets,\n", + " df_metabolites = df_metabolites, similarity_matrix = similarity_matrix,\n", + " lower_bound =lower_bound, upper_bound =0.95)\n", + " if lower_bound <0:\n", + " break\n", + " \n", + " new_metabolites = random.sample(metabolites, min(1,len(metabolites)))\n", + "\n", + " for met in new_metabolites:\n", + " UID_list.append(UID), MID_list.append(met), Type_list.append(Type)\n", + "\n", + " df2 = pd.DataFrame(data = {\"Uniprot ID\": UID_list, \"molecule ID\" : MID_list, \"type\" : Type_list})\n", + " df2[\"Binding\"] = 0\n", + "\n", + " df = pd.concat([df, df2], ignore_index = True)\n", + " return(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (a)(i) Creating negative data points for the training set (experimental evidence):" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1266\n", + "0\n", + "Time: 0.0 [min]\n", + "0\n", + "100\n", + "Time: 0.1 [min]\n", + "2\n", + "200\n", + "Time: 0.21 [min]\n", + "5\n", + "300\n", + "Time: 0.33 [min]\n", + "10\n", + "400\n", + "Time: 0.44 [min]\n", + "22\n", + "500\n", + "Time: 0.56 [min]\n", + "33\n", + "600\n", + "Time: 0.66 [min]\n", + "45\n", + "700\n", + "Time: 0.76 [min]\n", + "59\n", + "800\n", + "Time: 0.88 [min]\n", + "66\n", + "900\n", + "Time: 0.98 [min]\n", + "83\n", + "1000\n", + "Time: 1.07 [min]\n", + "95\n", + "1100\n", + "Time: 1.18 [min]\n", + "110\n", + "1200\n", + "Time: 1.29 [min]\n", + 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Uniprot IDmolecule IDevidenceECFPBindingtype
0Q5B2F7CHEBI:57344exp0100000001000000000000000000000001000000000000...1NaN
1Q9SAH9CHEBI:58349exp0000000001000000100000100000000000000000000000...1NaN
2Q8IPJ6CHEBI:57776exp0000000000000000000000000000010001000000000000...1NaN
3A0A1D5PCZ1C00002exp0000000001000000000000000000000000000000000000...1NaN
4O22765CHEBI:33384exp0100000000000000000000000000000000000000000000...1NaN
.....................
29207P04152CHEBI:15901NaNNaN0NaN
29208Q4Q1I5C00007NaNNaN0NaN
29209P43123CHEBI:30616NaNNaN0NaN
29210Q8RVK9C00002NaNNaN0NaN
29211Q62730CHEBI:30616NaNNaN0NaN
\n", + "

29212 rows × 6 columns

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" + ], + "text/plain": [ + " Uniprot ID molecule ID evidence \\\n", + "0 Q5B2F7 CHEBI:57344 exp \n", + "1 Q9SAH9 CHEBI:58349 exp \n", + "2 Q8IPJ6 CHEBI:57776 exp \n", + "3 A0A1D5PCZ1 C00002 exp \n", + "4 O22765 CHEBI:33384 exp \n", + "... ... ... ... \n", + "29207 P04152 CHEBI:15901 NaN \n", + "29208 Q4Q1I5 C00007 NaN \n", + "29209 P43123 CHEBI:30616 NaN \n", + "29210 Q8RVK9 C00002 NaN \n", + "29211 Q62730 CHEBI:30616 NaN \n", + "\n", + " ECFP Binding type \n", + "0 0100000001000000000000000000000001000000000000... 1 NaN \n", + "1 0000000001000000100000100000000000000000000000... 1 NaN \n", + "2 0000000000000000000000000000010001000000000000... 1 NaN \n", + "3 0000000001000000000000000000000000000000000000... 1 NaN \n", + "4 0100000000000000000000000000000000000000000000... 1 NaN \n", + "... ... ... ... \n", + "29207 NaN 0 NaN \n", + "29208 NaN 0 NaN \n", + "29209 NaN 0 NaN \n", + "29210 NaN 0 NaN \n", + "29211 NaN 0 NaN \n", + "\n", + "[29212 rows x 6 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_UID_MID_train_exp = df_UID_MID_train.loc[df_UID_MID_train[\"evidence\"] == \"exp\"]\n", + "\n", + "df_UID_MID_train_exp = drop_samples_without_mol_file(df = df_UID_MID_train_exp)\n", + "#calculating similarity matrix for all metabolites in the set:\n", + "df_metabolites_train, similarity_matrix_train = get_metabolites_and_similarities(df = df_UID_MID_train_exp)\n", + "print(len(df_metabolites_train))\n", + "\n", + "df_UID_MID_train_exp[\"Binding\"] = 1\n", + "df_UID_MID_train_exp.reset_index(inplace = True, drop = True)\n", + "\n", + "df_UID_MID_train_exp = create_negative_samples(df = df_UID_MID_train_exp, df_metabolites = df_metabolites_train,\n", + " similarity_matrix = similarity_matrix_train)\n", + "df_UID_MID_train_exp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (a)(ii) Creating negative data points for the training set (phylogentical evidence):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "df_UID_MID_train_phylo = df_UID_MID_train.loc[df_UID_MID_train[\"evidence\"] == \"phylo\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "655\n", + "0\n", + "Time: 0.0 [min]\n", + "0\n", + "100\n", + "Time: 0.22 [min]\n", + "0\n", + "200\n", + "Time: 0.45 [min]\n", + "1\n", + "300\n", + "Time: 0.68 [min]\n", + "1\n", + "400\n", + "Time: 0.9 [min]\n", + "1\n", + "500\n", + "Time: 1.12 [min]\n", + "1\n", + "600\n", + "Time: 1.34 [min]\n", + "1\n", + "700\n", + "Time: 1.56 [min]\n", + "2\n", + "800\n", + "Time: 1.77 [min]\n", + "3\n", + "900\n", + "Time: 2.0 [min]\n", + "4\n", + "1000\n", + "Time: 2.22 [min]\n", + "5\n", + "1100\n", + "Time: 2.46 [min]\n", + "5\n", + "1200\n", + "Time: 2.69 [min]\n", + "5\n", + "1300\n", + "Time: 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Uniprot IDmolecule IDevidenceECFPBindingtype
0A8XT89CHEBI:58885phylo0000000000000100000000000000000000000000000000...1NaN
1B2GV06CHEBI:57292phylo0100100001000000000000000000000011000000000000...1NaN
2A0A022RBJ3CHEBI:33227phylo1000000000000000000000000000000000000000000000...1NaN
3G3S168CHEBI:59776phylo0100000000000000000000000000000000000000000000...1NaN
4F6I0H0C00002phylo0000000001000000000000000000000000000000000000...1NaN
.....................
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" + ], + "text/plain": [ + " Uniprot ID molecule ID evidence \\\n", + "0 A8XT89 CHEBI:58885 phylo \n", + "1 B2GV06 CHEBI:57292 phylo \n", + "2 A0A022RBJ3 CHEBI:33227 phylo \n", + "3 G3S168 CHEBI:59776 phylo \n", + "4 F6I0H0 C00002 phylo \n", + "... ... ... ... \n", + "424181 A0A0A0LAF9 C00002 NaN \n", + "424182 A9V8I9 C00002 NaN \n", + "424183 A0A2J6JMI2 C00002 NaN \n", + "424184 Q8Y7G6 C00002 NaN \n", + "424185 B9R6X0 C00002 NaN \n", + "\n", + " ECFP Binding type \n", + "0 0000000000000100000000000000000000000000000000... 1 NaN \n", + "1 0100100001000000000000000000000011000000000000... 1 NaN \n", + "2 1000000000000000000000000000000000000000000000... 1 NaN \n", + "3 0100000000000000000000000000000000000000000000... 1 NaN \n", + "4 0000000001000000000000000000000000000000000000... 1 NaN \n", + "... ... ... ... \n", + "424181 NaN 0 NaN \n", + "424182 NaN 0 NaN \n", + "424183 NaN 0 NaN \n", + "424184 NaN 0 NaN \n", + "424185 NaN 0 NaN \n", + "\n", + "[424186 rows x 6 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_UID_MID_train_phylo = drop_samples_without_mol_file(df = df_UID_MID_train_phylo)\n", + "#calculating similarity matrix for all metabolites in the set:\n", + "df_metabolites_train, similarity_matrix_train = get_metabolites_and_similarities(df = df_UID_MID_train_phylo)\n", + "print(len(df_metabolites_train))\n", + "\n", + "df_UID_MID_train_phylo[\"Binding\"] = 1\n", + "df_UID_MID_train_phylo.reset_index(inplace = True, drop = True)\n", + "\n", + "df_UID_MID_train_phylo = create_negative_samples(df = df_UID_MID_train_phylo, df_metabolites = df_metabolites_train,\n", + " similarity_matrix = similarity_matrix_train)\n", + "df_UID_MID_train_phylo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (a)(iii) Creating negative data points for the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df_UID_MID_test = df_UID_MID_test.loc[df_UID_MID_test[\"evidence\"] == \"exp\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "706\n", + "0\n", + "Time: 0.0 [min]\n", + "0\n", + "100\n", + "Time: 0.05 [min]\n", + "6\n", + "200\n", + "Time: 0.1 [min]\n", + "21\n", + "300\n", + "Time: 0.15 [min]\n", + "36\n", + "400\n", + "Time: 0.21 [min]\n", + "54\n", + "500\n", + "Time: 0.26 [min]\n", + "73\n", + "600\n", + "Time: 0.31 [min]\n", + "89\n", + "700\n", + "Time: 0.37 [min]\n", + "104\n", + "800\n", + "Time: 0.42 [min]\n", + "128\n", + "900\n", + "Time: 0.48 [min]\n", + "139\n", + "1000\n", + "Time: 0.53 [min]\n", + "151\n", + "1100\n", + "Time: 0.59 [min]\n", + "165\n", + "1200\n", + "Time: 0.65 [min]\n", + "183\n", + "1300\n", + "Time: 0.71 [min]\n", + "200\n", + "1400\n", + "Time: 0.77 [min]\n", + "216\n", + "1500\n", + "Time: 0.83 [min]\n", + "227\n", + "1600\n", + "Time: 0.88 [min]\n", + "242\n", + "1700\n", + "Time: 0.94 [min]\n", + "257\n", + "1800\n", + "Time: 1.0 [min]\n", + "268\n", + "1900\n", + "Time: 1.05 [min]\n", + "285\n", + "2000\n", + "Time: 1.12 [min]\n", + "303\n", + "2100\n", + "Time: 1.17 [min]\n", + "315\n", + "2200\n", + "Time: 1.23 [min]\n", + "332\n", + "2300\n", + "Time: 1.3 [min]\n", + "351\n", + "2400\n", + "Time: 1.36 [min]\n", + "367\n", + "2500\n", + "Time: 1.42 [min]\n", + "383\n", + "2600\n", + "Time: 1.49 [min]\n", + "396\n", + "2700\n", + "Time: 1.55 [min]\n", + "409\n", + "2800\n", + "Time: 1.63 [min]\n", + "426\n", + "2900\n", + "Time: 1.7 [min]\n", + "444\n", + "3000\n", + "Time: 1.77 [min]\n", + "459\n", + "3100\n", + "Time: 1.84 [min]\n", + "474\n", + "3200\n", + "Time: 1.92 [min]\n", + "496\n", + "3300\n", + "Time: 2.0 [min]\n", + "518\n", + "3400\n", + "Time: 2.08 [min]\n", + "537\n", + "3500\n", + "Time: 2.16 [min]\n", + "552\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Uniprot ID molecule ID evidence \\\n", + "0 P71828 CHEBI:57925 exp \n", + "1 A0A1D8PGI8 CHEBI:16897 exp \n", + "2 Q8NEZ4 C00019 exp \n", + "3 F4K5T2 CHEBI:35235 exp \n", + "4 Q05762 CHEBI:57453 exp \n", + "... ... ... ... \n", + "7009 P53739 CHEBI:57618 NaN \n", + "7010 H9D1R1 CHEBI:71682 NaN \n", + "7011 P00962 CHEBI:58048 NaN \n", + "7012 P48163 C00002 NaN \n", + "7013 Q3TIG7 CHEBI:57618 NaN \n", + "\n", + " ECFP Binding type \n", + "0 0100000001000000000000000000000000000000000000... 1 NaN \n", + "1 0100000000000001000000000000000000000000000100... 1 NaN \n", + "2 0100100001000000000000000000000001000000000000... 1 NaN \n", + "3 0100000000000000000000000000000000000000000000... 1 NaN \n", + "4 0110000000000000001000000000000000000000000000... 1 NaN \n", + "... ... ... ... \n", + "7009 NaN 0 NaN \n", + "7010 NaN 0 NaN \n", + "7011 NaN 0 NaN \n", + "7012 NaN 0 NaN \n", + "7013 NaN 0 NaN \n", + "\n", + "[7014 rows x 6 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_UID_MID_test = drop_samples_without_mol_file(df = df_UID_MID_test)\n", + "#calculating similarity matrix for all metabolites in the set:\n", + "df_metabolites_test, similarity_matrix_test = get_metabolites_and_similarities(df = df_UID_MID_test)\n", + "print(len(df_metabolites_test))\n", + "\n", + "df_UID_MID_test[\"Binding\"] = 1\n", + "df_UID_MID_test.reset_index(inplace = True, drop = True)\n", + "\n", + "df_UID_MID_test = create_negative_samples(df = df_UID_MID_test, df_metabolites = df_metabolites_test,\n", + " similarity_matrix = similarity_matrix_test)\n", + "df_UID_MID_test" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "df_UID_MID_train_phylo.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\",\"df_UID_MID_train_phylo_1_1.pkl\"))\n", + "df_UID_MID_train_exp.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\",\"df_UID_MID_train_exp_1_1.pkl\"))\n", + "df_UID_MID_test.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\",\"df_UID_MID_test_exp_phylo_1_1.pkl\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (b) Mapping ECFPs and ESM-1b-vectors to different splits:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "df_UID_MID_train_phylo = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\",\"df_UID_MID_train_phylo_1_1.pkl\"))\n", + "df_UID_MID_train_exp = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\",\"df_UID_MID_train_exp_1_1.pkl\"))\n", + "df_UID_MID_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\",\"df_UID_MID_test_exp_phylo_1_1.pkl\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "df_UID_MID_train_exp[\"evidence\"] = \"exp\"\n", + "df_UID_MID_train_phylo[\"evidence\"] = \"phylo\"\n", + "df_UID_MID_train = pd.concat([df_UID_MID_train_exp, df_UID_MID_train_phylo], ignore_index = True)\n", + "\n", + "df_UID_MID_test[\"evidence\"] = \"exp\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (b)(i) Mappings ECFPs:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "df_ecfps = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data\", \"df_ecfps.pkl\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "df_UID_MID_train_phylo.drop(columns = [\"ECFP\"], inplace = True)\n", + "df_UID_MID_train_phylo = df_UID_MID_train_phylo.merge(df_ecfps, right_on = \"substrate ID\", left_on = \"molecule ID\", how = \"left\")\n", + "\n", + "df_UID_MID_test.drop(columns = [\"ECFP\"], inplace = True)\n", + "df_UID_MID_test = df_UID_MID_test.merge(df_ecfps, right_on = \"substrate ID\", left_on = \"molecule ID\", how = \"left\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df_UID_MID_train_phylo.drop(columns = [\"ECFP\"], inplace = True)\n", + "df_UID_MID_train_phylo = df_UID_MID_train_phylo.merge(df_ecfps, right_on = \"substrate ID\", left_on = \"molecule ID\", how = \"left\")\n", + "\n", + "df_UID_MID_train_exp.drop(columns = [\"ECFP\"], inplace = True)\n", + "df_UID_MID_train_exp = df_UID_MID_train_exp.merge(df_ecfps, right_on = \"substrate ID\", left_on = \"molecule ID\", how = \"left\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "df_UID_MID_train_phylo[\"outcome\"] = df_UID_MID_train_phylo[\"Binding\"]\n", + "df_UID_MID_train_exp[\"outcome\"] = df_UID_MID_train_exp[\"Binding\"]\n", + "df_UID_MID_test[\"outcome\"] = df_UID_MID_test[\"Binding\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "df_UID_MID_train_phylo = df_UID_MID_train_phylo.merge(UNIPROT_df, how = \"left\", on = \"Uniprot ID\")\n", + "df_UID_MID_train_exp = df_UID_MID_train_exp.merge(UNIPROT_df, how = \"left\", on = \"Uniprot ID\")\n", + "df_UID_MID_test = df_UID_MID_test.merge(UNIPROT_df, how = \"left\", on = \"Uniprot ID\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_UID_MID_train_phylo.to_csv(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\",\"df_UID_MID_train_phylo_1_1.csv\"))\n", + "df_UID_MID_train_exp.to_csv(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\",\"df_UID_MID_train_exp_1_1.csv\"))\n", + "df_UID_MID_test.to_csv(join(CURRENT_DIR, \"..\" ,\"data\",\"enzyme_substrate_data\",\"df_UID_MID_test_exp_phylo_1_1.csv\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (b)(ii) Mappings ESM1b-vectors:" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "'''df_train = df_UID_MID_train\n", + "df_test = df_UID_MID_test\n", + "\n", + "Uniprot_df = pd.DataFrame(data = {\"Uniprot ID\" : UNIPROT_df[\"Uniprot ID\"],\n", + " \"ESM1b\" : UNIPROT_df[\"ESM1b\"]})\n", + "\n", + "df_train = df_train.merge(Uniprot_df, on = \"Uniprot ID\", how = \"left\")\n", + "df_test = df_test.merge(Uniprot_df, on = \"Uniprot ID\", how = \"left\")\n", + "\n", + "df_train.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"df_train.pkl\"))\n", + "df_test.to_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"splits\", \"df_test.pkl\"))''';" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks_and_code/1_2 - Loading Mou et al datasets.ipynb b/notebooks_and_code/1_2a - Loading Mou et al datasets.ipynb similarity index 100% rename from notebooks_and_code/1_2 - Loading Mou et al datasets.ipynb rename to notebooks_and_code/1_2a - Loading Mou et al datasets.ipynb diff --git a/notebooks_and_code/1_1 - Loading Yang et al datasets.ipynb b/notebooks_and_code/1_2b - Loading Yang et al datasets.ipynb similarity index 100% rename from notebooks_and_code/1_1 - Loading Yang et al datasets.ipynb rename to notebooks_and_code/1_2b - Loading Yang et al datasets.ipynb diff --git a/notebooks_and_code/2_0 - Training gradient boosting models.ipynb b/notebooks_and_code/2_0 - Training gradient boosting models.ipynb index c2268e9..5b28e48 100644 --- a/notebooks_and_code/2_0 - Training gradient boosting models.ipynb +++ b/notebooks_and_code/2_0 - Training gradient boosting models.ipynb @@ -18,14 +18,22 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\alexk\\anaconda3\\envs\\ESP\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\alexk\\OneDrive\\Dokumente\\GitHub\\SubFinder\\notebooks_and_code\n" + "C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\n" ] } ], @@ -40,12 +48,11 @@ "from os.path import join\n", "from sklearn.model_selection import KFold\n", "from sklearn.metrics import roc_auc_score\n", - "#from hyperopt import fmin, tpe, hp, Trials, rand\n", + "from hyperopt import fmin, tpe, hp, Trials, rand\n", "import xgboost as xgb\n", "from sklearn.metrics import matthews_corrcoef\n", "\n", "\n", - "\n", "sys.path.append('.\\\\additional_code')\n", "#from data_preprocessing import *\n", "\n", @@ -62,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -85,16 +92,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:56: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + "C:\\Users\\alexk\\anaconda3\\envs\\ESP\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:73: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " result = libops.scalar_compare(x.ravel(), y, op)\n", - "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:56: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + "C:\\Users\\alexk\\anaconda3\\envs\\ESP\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:73: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " result = libops.scalar_compare(x.ravel(), y, op)\n" ] } @@ -110,7 +117,7 @@ "df_test = df_test.loc[df_test[\"ESM1b\"] != \"\"]\n", "df_test = df_test.loc[df_test[\"type\"] != \"engqvist\"]\n", "df_test = df_test.loc[df_test[\"GNN rep\"] != \"\"]\n", - "df_test.reset_index(inplace = True, drop = True)\n" + "df_test.reset_index(inplace = True, drop = True)" ] }, { @@ -123,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -173,11 +180,30 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "44733 11145\n", + "33555 11178\n", + "22014 11541\n", + "10952 11062\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\alexk\\anaconda3\\envs\\ESP\\lib\\site-packages\\numpy\\lib\\npyio.py:501: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " arr = np.asanyarray(arr)\n" + ] + } + ], "source": [ - "'''data_train2 = df_train.copy()\n", + "data_train2 = df_train.copy()\n", "data_train2 = array_column_to_strings(data_train2, column = \"ESM1b\")\n", "\n", "data_train2, df_fold = split_dataframe(df = data_train2, frac=5)\n", @@ -211,12 +237,12 @@ " test_indices[i] = fold_indices[i]\n", " \n", "np.save(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"CV_train_indices.npy\"), train_indices)\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"CV_test_indices.npy\"), test_indices)''';" + "np.save(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"CV_test_indices.npy\"), test_indices)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -247,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -289,19 +315,7 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'hp' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14128/3269672333.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[1;31m#Defining search space for hyperparameter optimization\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m space_gradient_boosting = {\"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 0.5),\n\u001b[0m\u001b[0;32m 29\u001b[0m \u001b[1;34m\"max_depth\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mhp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"max_depth\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;34m\"reg_lambda\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mhp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"reg_lambda\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m: name 'hp' is not defined" - ] - } - ], + "outputs": [], "source": [ "def cross_validation_neg_acc_gradient_boosting(param):\n", " num_round = param[\"num_rounds\"]\n", @@ -358,8 +372,6 @@ "for i in range(1,2000):\n", " best = fmin(fn = cross_validation_neg_acc_gradient_boosting, space = space_gradient_boosting,\n", " algo = rand.suggest, max_evals = i, trials = trials)\n", - " np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"results\", \"cross_validation_binding_ESM1b.npy\"), trials.best_trial)\n", - " np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"results\", \"cross_validation_binding_ESM1b_argmin.npy\"), trials.argmin)\n", " logging.info(i)\n", " logging.info(trials.best_trial[\"result\"][\"loss\"])\n", " logging.info(trials.argmin)''';" @@ -404,14 +416,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[08:02:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[08:05:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[08:09:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[08:12:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[08:15:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "Loss values: [145.78749933523534, 143.24696900455865, 152.99577743836903, 142.04005688827212, 140.47859087124428]\n", - "Accuracies: [0.8696086443294289, 0.8701705855232826, 0.859611805370912, 0.8664304694419841, 0.8676669981017807]\n", - "ROC-AUC scores: [0.9386338140991644, 0.9417085126064844, 0.9334066416003604, 0.9396788677977708, 0.9387971266200663]\n" + "[16:49:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[16:53:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[16:56:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[16:59:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[17:03:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Loss values: [135.64613843194581, 146.31281276919225, 149.789174479733, 159.6153340309748, 137.4009624484746]\n", + "Accuracies: [0.8691790040376851, 0.8684022186437645, 0.8662160991248592, 0.8606942686675104, 0.8758217677136596]\n", + "ROC-AUC scores: [0.9426084967554512, 0.9383771038523977, 0.9347969874349884, 0.9294492918142963, 0.9389172581369728]\n" ] } ], @@ -458,27 +470,6 @@ "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ECFP.npy\"), np.array(ROC_AUC))" ] }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "144.90977870753588" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "np.mean([145.78749933523534, 143.24696900455865, 152.99577743836903, 142.04005688827212, 140.47859087124428])" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -502,8 +493,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[08:19:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "Accuracy on test set: 0.8732035928143712, ROC-AUC score for test set: 0.9372709213592973, MCC: 0.6944703270478896\n" + "[17:06:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Accuracy on test set: 0.8722044728434505, ROC-AUC score for test set: 0.9368380603439415, MCC: 0.6915652351366568\n" ] } ], @@ -521,15 +512,15 @@ "\n", "print(\"Accuracy on test set: %s, ROC-AUC score for test set: %s, MCC: %s\" % (acc_test, roc_auc, mcc))\n", "\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ECFP.npy\"), bst.predict(dtest))\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ECFP.npy\"), test_y)" + "#np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ECFP.npy\"), bst.predict(dtest))\n", + "#np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ECFP.npy\"), test_y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### (b) ESM1b and GNN:" + "### (b) ESM1b and GNN (pre-trained):" ] }, { @@ -541,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -551,7 +542,7 @@ " \n", " for ind in df.index:\n", " emb = df[\"ESM1b\"][ind]\n", - " ecfp = df[\"GNN rep\"][ind]\n", + " ecfp = df[\"GNN rep (pretrained)\"][ind]\n", " \n", " X = X +(np.concatenate([ecfp, emb]), );\n", " y = y + (df[\"Binding\"][ind], );\n", @@ -581,21 +572,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'hp' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3812/3269672333.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[1;31m#Defining search space for hyperparameter optimization\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m space_gradient_boosting = {\"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 0.5),\n\u001b[0m\u001b[0;32m 29\u001b[0m \u001b[1;34m\"max_depth\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mhp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"max_depth\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;34m\"reg_lambda\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mhp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"reg_lambda\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m: name 'hp' is not defined" - ] - } - ], + "outputs": [], "source": [ "def cross_validation_neg_acc_gradient_boosting(param):\n", " num_round = param[\"num_rounds\"]\n", @@ -643,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -652,8 +631,6 @@ "for i in range(1,2000):\n", " best = fmin(fn = cross_validation_neg_acc_gradient_boosting, space = space_gradient_boosting,\n", " algo = rand.suggest, max_evals = i, trials = trials)\n", - " np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"results\", \"cross_validation_binding_ESM1b.npy\"), trials.best_trial)\n", - " np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"results\", \"cross_validation_binding_ESM1b_argmin.npy\"), trials.argmin)\n", " logging.info(i)\n", " logging.info(trials.best_trial[\"result\"][\"loss\"])\n", " logging.info(trials.argmin)''';" @@ -668,18 +645,18 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "param = {'learning_rate': 0.18650490181254992,\n", - " 'max_delta_step': 3.747845574621026,\n", - " 'max_depth': 10,\n", - " 'min_child_weight': 0.3985828341503377,\n", - " 'num_rounds': 366.6289439088624,\n", - " 'reg_alpha': 0.8924081775198611,\n", - " 'reg_lambda': 4.888409483879253, \n", - " 'weight': 0.14249550342115477}" + "param = {'learning_rate': 0.09207371208675638,\n", + " 'max_delta_step': 1.6501026095681381,\n", + " 'max_depth': 12, \n", + " 'min_child_weight': 4.385828776339477,\n", + " 'num_rounds': 361.4040208821599,\n", + " 'reg_alpha': 2.8139614176935313,\n", + " 'reg_lambda': 1.1521733347508363,\n", + " 'weight': 0.14162338902155536}" ] }, { @@ -691,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -707,21 +684,21 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[13:13:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[13:15:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[13:17:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[13:20:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[14:52:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "Loss values: [127.93542431344466, 122.79501284863223, 132.211203999234, 126.27687643490749, 131.02385081797956]\n", - "Accuracies: [0.9080177971488241, 0.9076071922544952, 0.906319241336524, 0.9075287865367582, 0.9075296031817771]\n", - "ROC-AUC scores: [0.9481174271320283, 0.9501233965053054, 0.9437746666563114, 0.9507466012024709, 0.9510292058347031]\n" + "[17:10:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[17:12:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[17:14:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[17:16:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[17:18:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Loss values: [121.97575964696276, 132.66941366275, 143.92507718249186, 155.63028256825766, 139.25640504585414]\n", + "Accuracies: [0.8886496186630776, 0.8872785829307569, 0.8777402304826272, 0.8782317844874344, 0.8850438276113952]\n", + "ROC-AUC scores: [0.9474577315968011, 0.9433819919446351, 0.9373767455135212, 0.9336666463444948, 0.9437762941205631]\n" ] } ], @@ -753,29 +730,9 @@ "print(\"Accuracies: %s\" %accuracy)\n", "print(\"ROC-AUC scores: %s\" %ROC_AUC)\n", "\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_GNN.npy\"), np.array(accuracy))\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"loss_CV_xgboost_ESM1b_GNN.npy\"), np.array(loss))\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_GNN.npy\"), np.array(ROC_AUC))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "128.04847368283959" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean([127.93542431344466, 122.79501284863223, 132.211203999234, 126.27687643490749, 131.02385081797956])" + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_GNN_pretrained.npy\"), np.array(accuracy))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"loss_CV_xgboost_ESM1b_GNN_pretrained.npy\"), np.array(loss))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_GNN_pretrained.npy\"), np.array(ROC_AUC))" ] }, { @@ -788,15 +745,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[14:54:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "Accuracy on test set: 0.9051646706586827, ROC-AUC score for test set: 0.9455840254566843, MCC: 0.7509981408842288\n" + "[17:20:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Accuracy on test set: 0.8944943903707556, ROC-AUC score for test set: 0.9415865958288083, MCC: 0.7327206623426592\n" ] } ], @@ -814,8 +771,8 @@ "\n", "print(\"Accuracy on test set: %s, ROC-AUC score for test set: %s, MCC: %s\" % (acc_test, roc_auc, mcc))\n", "\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_GNN.npy\"), bst.predict(dtest))\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_GNN.npy\"), test_y)" + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_GNN_pretrained.npy\"), bst.predict(dtest))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_GNN_pretrained.npy\"), test_y)" ] }, { @@ -834,7 +791,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -933,8 +890,6 @@ "for i in range(1,2000):\n", " best = fmin(fn = cross_validation_neg_acc_gradient_boosting, space = space_gradient_boosting,\n", " algo = rand.suggest, max_evals = i, trials = trials)\n", - " np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"results\", \"cross_validation_binding_ESM1b.npy\"), trials.best_trial)\n", - " np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"results\", \"cross_validation_binding_ESM1b_argmin.npy\"), trials.argmin)\n", " logging.info(i)\n", " logging.info(trials.best_trial[\"result\"][\"loss\"])\n", " logging.info(trials.argmin)''';" @@ -949,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -981,21 +936,21 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[14:57:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[15:00:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[15:02:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[15:04:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[15:06:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "Loss values: [118.64570140259957, 109.55544934268875, 136.58107225995332, 112.14992450710591, 116.58008809267714]\n", - "Accuracies: [0.9095614274039772, 0.9113877362840018, 0.8988744128334663, 0.9105403011514615, 0.9075296031817771]\n", - "ROC-AUC scores: [0.9531463757576579, 0.9543092505120799, 0.9443616020172131, 0.9560860893869755, 0.9515226897235054]\n" + "[20:05:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[20:08:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[20:10:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[20:12:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[20:15:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Loss values: [108.68044529838687, 113.38285635774665, 116.75072442009302, 125.35861908227079, 119.36638679297242]\n", + "Accuracies: [0.9103633916554509, 0.9104490964394346, 0.9081535395546313, 0.9067980473693726, 0.908235938641344]\n", + "ROC-AUC scores: [0.956404258764795, 0.9524134068104446, 0.9484047612505799, 0.9470567532760683, 0.951539293645603]\n" ] } ], @@ -1032,26 +987,6 @@ "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ts_ECFP.npy\"), np.array(ROC_AUC))" ] }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "118.70244712100494" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean([118.64570140259957, 109.55544934268875, 136.58107225995332, 112.14992450710591, 116.58008809267714])" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1062,15 +997,15 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[15:09:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "Accuracy on test set: 0.9063622754491018, ROC-AUC score for test set: 0.9499652082794869, MCC: 0.7594154904873446\n" + "[20:17:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Accuracy on test set: 0.904599152983134, ROC-AUC score for test set: 0.9494417844088786, MCC: 0.7541937162526161\n" ] } ], @@ -1096,7 +1031,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### (d) ESM1b_ts and GNN:" + "### (d) ESM1b_ts and ECFP (512-dimensional):" ] }, { @@ -1108,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1118,7 +1053,7 @@ " \n", " for ind in df.index:\n", " emb = df[\"ESM1b_ts\"][ind]\n", - " ecfp = df[\"GNN rep\"][ind]\n", + " ecfp = np.array(list(df[\"ECFP_512\"][ind])).astype(int)\n", " \n", " X = X +(np.concatenate([ecfp, emb]), );\n", " y = y + (df[\"Binding\"][ind], );\n", @@ -1128,9 +1063,8 @@ "train_X, train_y = create_input_and_output_data(df = df_train)\n", "test_X, test_y = create_input_and_output_data(df = df_test)\n", "\n", - "\n", - "feature_names = [\"GNN rep_\" + str(i) for i in range(50)]\n", - "feature_names = feature_names + [\"ESM1b_\" + str(i) for i in range(1280)]\n", + "feature_names = [\"ECFP_\" + str(i) for i in range(512)]\n", + "feature_names = feature_names + [\"ESM1b_ts_\" + str(i) for i in range(1280)]\n", "\n", "train_X = np.array(train_X)\n", "test_X = np.array(test_X)\n", @@ -1148,21 +1082,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 28, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'hp' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7960/3269672333.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[1;31m#Defining search space for hyperparameter optimization\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m space_gradient_boosting = {\"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 0.5),\n\u001b[0m\u001b[0;32m 29\u001b[0m \u001b[1;34m\"max_depth\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mhp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"max_depth\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;34m\"reg_lambda\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mhp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"reg_lambda\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m: name 'hp' is not defined" - ] - } - ], + "outputs": [], "source": [ "def cross_validation_neg_acc_gradient_boosting(param):\n", " num_round = param[\"num_rounds\"]\n", @@ -1192,13 +1114,13 @@ "\n", "#Defining search space for hyperparameter optimization\n", "space_gradient_boosting = {\"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 0.5),\n", - " \"max_depth\": hp.choice(\"max_depth\", [9,10,11,12,13]),\n", - " \"reg_lambda\": hp.uniform(\"reg_lambda\", 0, 5),\n", - " \"reg_alpha\": hp.uniform(\"reg_alpha\", 0, 5),\n", - " \"max_delta_step\": hp.uniform(\"max_delta_step\", 0, 5),\n", - " \"min_child_weight\": hp.uniform(\"min_child_weight\", 0.1, 15),\n", - " \"num_rounds\": hp.uniform(\"num_rounds\", 200, 400),\n", - " \"weight\" : hp.uniform(\"weight\", 0.1,0.33)}" + " \"max_depth\": hp.choice(\"max_depth\", [9,10,11,12,13]),\n", + " \"reg_lambda\": hp.uniform(\"reg_lambda\", 0, 5),\n", + " \"reg_alpha\": hp.uniform(\"reg_alpha\", 0, 5),\n", + " \"max_delta_step\": hp.uniform(\"max_delta_step\", 0, 5),\n", + " \"min_child_weight\": hp.uniform(\"min_child_weight\", 0.1, 15),\n", + " \"num_rounds\": hp.uniform(\"num_rounds\", 200, 400),\n", + " \"weight\" : hp.uniform(\"weight\", 0.1,0.33)}" ] }, { @@ -1210,7 +1132,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1219,8 +1141,6 @@ "for i in range(1,2000):\n", " best = fmin(fn = cross_validation_neg_acc_gradient_boosting, space = space_gradient_boosting,\n", " algo = rand.suggest, max_evals = i, trials = trials)\n", - " np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"results\", \"cross_validation_binding_ESM1b.npy\"), trials.best_trial)\n", - " np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"results\", \"cross_validation_binding_ESM1b_argmin.npy\"), trials.argmin)\n", " logging.info(i)\n", " logging.info(trials.best_trial[\"result\"][\"loss\"])\n", " logging.info(trials.argmin)''';" @@ -1235,18 +1155,18 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "param = {'learning_rate': 0.18444025726334898,\n", - " 'max_delta_step': 3.2748796106084117,\n", - " 'max_depth': 13,\n", - " 'min_child_weight': 3.1946753845027738,\n", - " 'num_rounds': 314.1036429221291,\n", - " 'reg_alpha': 0.48821021807600673,\n", - " 'reg_lambda': 2.6236829011598073,\n", - " 'weight': 0.1264521266931227}\n", + "param = {'learning_rate': 0.20031456821679422,\n", + " 'max_delta_step': 3.723458003552047,\n", + " 'max_depth': 10,\n", + " 'min_child_weight': 2.0109208762032678,\n", + " 'num_rounds': 347.78525681188614,\n", + " 'reg_alpha': 2.213525607682663,\n", + " 'reg_lambda': 4.5822546393906025,\n", + " 'weight': 0.16604653557737126}\n", "\n", "num_round = param[\"num_rounds\"]\n", "param[\"tree_method\"] = \"gpu_hist\"\n", @@ -1267,21 +1187,21 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[15:11:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[15:13:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[15:14:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[15:15:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[15:16:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "Loss values: [107.10779217320857, 97.97889476323061, 117.6449558838506, 92.98319474363471, 102.69988687285533]\n", - "Accuracies: [0.9046581312993734, 0.9083448593822038, 0.90064699104848, 0.9066430469441984, 0.9074392117870379]\n", - "ROC-AUC scores: [0.9584373172944172, 0.9558062039027504, 0.9500524479937247, 0.9617684224032785, 0.9569448004040885]\n" + "[20:21:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[20:23:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[20:25:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[20:27:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[20:29:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Loss values: [104.16043379763315, 118.19757372302232, 124.29384769765849, 132.1245948240752, 119.5747769265525]\n", + "Accuracies: [0.8947510094212652, 0.8884415816782967, 0.8868382289229703, 0.8823901645272103, 0.8911614317019723]\n", + "ROC-AUC scores: [0.9559755847455434, 0.9496475323966114, 0.9486398091770489, 0.9454227918700563, 0.9495638094101112]\n" ] } ], @@ -1313,29 +1233,9 @@ "print(\"Accuracies: %s\" %accuracy)\n", "print(\"ROC-AUC scores: %s\" %ROC_AUC)\n", "\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ts_GNN.npy\"), np.array(accuracy))\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"loss_CV_xgboost_ESM1b_ts_GNN.npy\"), np.array(loss))\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ts_GNN.npy\"), np.array(ROC_AUC))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "103.68294488735596" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean([107.10779217320857, 97.97889476323061, 117.6449558838506, 92.98319474363471, 102.69988687285533])" + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ts_ECFP_512.npy\"), np.array(accuracy))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"loss_CV_xgboost_ESM1b_ts_ECFP_512.npy\"), np.array(loss))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ts_ECFP_512.npy\"), np.array(ROC_AUC))" ] }, { @@ -1348,15 +1248,15 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[15:18:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "Accuracy on test set: 0.9028443113772455, ROC-AUC score for test set: 0.9540429057244825, MCC: 0.7544314356676812\n" + "[20:31:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Accuracy on test set: 0.8875102162122, ROC-AUC score for test set: 0.9467608865614003, MCC: 0.7238779924304148\n" ] } ], @@ -1374,20 +1274,27 @@ "\n", "print(\"Accuracy on test set: %s, ROC-AUC score for test set: %s, MCC: %s\" % (acc_test, roc_auc, mcc))\n", "\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_GNN.npy\"), bst.predict(dtest))\n", - "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_GNN.npy\"), test_y)" + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_ECFP_512.npy\"), bst.predict(dtest))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_ECFP_512.npy\"), test_y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 3.Training the best model with training and test set (production mode):" + "### (e) ESM1b_ts and ECFP (2048-dimensional):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (i) Creating numpy arrays with input vectors and output variables" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1397,7 +1304,7 @@ " \n", " for ind in df.index:\n", " emb = df[\"ESM1b_ts\"][ind]\n", - " ecfp = np.array(list(df[\"ECFP\"][ind])).astype(int)\n", + " ecfp = np.array(list(df[\"ECFP_2048\"][ind])).astype(int)\n", " \n", " X = X +(np.concatenate([ecfp, emb]), );\n", " y = y + (df[\"Binding\"][ind], );\n", @@ -1407,8 +1314,7 @@ "train_X, train_y = create_input_and_output_data(df = df_train)\n", "test_X, test_y = create_input_and_output_data(df = df_test)\n", "\n", - "\n", - "feature_names = [\"ECFP_\" + str(i) for i in range(1024)]\n", + "feature_names = [\"ECFP_\" + str(i) for i in range(2048)]\n", "feature_names = feature_names + [\"ESM1b_ts_\" + str(i) for i in range(1280)]\n", "\n", "train_X = np.array(train_X)\n", @@ -1418,51 +1324,1037 @@ "test_y = np.array(test_y)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (ii) Performing hyperparameter optimization" + ] + }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "train_X = np.concatenate([train_X, test_X])\n", - "train_y = np.concatenate([train_y, test_y])" + "def cross_validation_neg_acc_gradient_boosting(param):\n", + " num_round = param[\"num_rounds\"]\n", + " param[\"tree_method\"] = \"gpu_hist\"\n", + " param[\"sampling_method\"] = \"gradient_based\"\n", + " param['objective'] = 'binary:logistic'\n", + " weights = np.array([param[\"weight\"] if binding == 0 else 1.0 for binding in df_train[\"Binding\"]])\n", + " \n", + " del param[\"num_rounds\"]\n", + " del param[\"weight\"]\n", + " \n", + " loss = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(np.array(train_X[train_index]), weight = weights[train_index],\n", + " label = np.array(train_y[train_index]))\n", + " dvalid = xgb.DMatrix(np.array(train_X[test_index]))\n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=1)\n", + " y_valid_pred = np.round(bst.predict(dvalid))\n", + " validation_y = train_y[test_index]\n", + " \n", + " false_positive = 100*(1-np.mean(np.array(validation_y)[y_valid_pred == 1]))\n", + " false_negative = 100*(np.mean(np.array(validation_y)[y_valid_pred == 0]))\n", + " logging.info(\"False positive rate: \" + str(false_positive)+ \"; False negative rate: \" + str(false_negative))\n", + " loss.append(2*(false_negative**2) + false_positive**1.3)\n", + " return(np.mean(loss))\n", + "\n", + "#Defining search space for hyperparameter optimization\n", + "space_gradient_boosting = {\"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 0.5),\n", + " \"max_depth\": hp.choice(\"max_depth\", [9,10,11,12,13]),\n", + " \"reg_lambda\": hp.uniform(\"reg_lambda\", 0, 5),\n", + " \"reg_alpha\": hp.uniform(\"reg_alpha\", 0, 5),\n", + " \"max_delta_step\": hp.uniform(\"max_delta_step\", 0, 5),\n", + " \"min_child_weight\": hp.uniform(\"min_child_weight\", 0.1, 15),\n", + " \"num_rounds\": hp.uniform(\"num_rounds\", 200, 400),\n", + " \"weight\" : hp.uniform(\"weight\", 0.1,0.33)}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Performing a random grid search:" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "param = {'learning_rate': 0.31553117247348733,\n", - " 'max_delta_step': 1.7726044219753656,\n", - " 'max_depth': 10,\n", - " 'min_child_weight': 1.3845040588450772,\n", - " 'num_rounds': 342.68325188584106,\n", - " 'reg_alpha': 0.531395259755843,\n", - " 'reg_lambda': 3.744980563764689,\n", - " 'weight': 0.26187490421514203}\n", + "'''trials = Trials()\n", + "\n", + "for i in range(1,2000):\n", + " best = fmin(fn = cross_validation_neg_acc_gradient_boosting, space = space_gradient_boosting,\n", + " algo = rand.suggest, max_evals = i, trials = trials)\n", + " logging.info(i)\n", + " logging.info(trials.best_trial[\"result\"][\"loss\"])\n", + " logging.info(trials.argmin)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Best set of hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'learning_rate': 0.38572930012069273,\n", + " 'max_delta_step': 2.3691090709866254,\n", + " 'max_depth': 13,\n", + " 'min_child_weight': 0.11946441222742316,\n", + " 'num_rounds': 185.5041665008057,\n", + " 'reg_alpha': 0.4492202436042625,\n", + " 'reg_lambda': 0.8927451484733545,\n", + " 'weight': 0.10881477775175043}\n", "\n", "num_round = param[\"num_rounds\"]\n", "param[\"tree_method\"] = \"gpu_hist\"\n", "param[\"sampling_method\"] = \"gradient_based\"\n", "param['objective'] = 'binary:logistic'\n", - "weights = np.array([param[\"weight\"] if binding == 0 else 1.0 for binding in train_y])\n", + "weights = np.array([param[\"weight\"] if binding == 0 else 1.0 for binding in df_train[\"Binding\"]])\n", "\n", "del param[\"num_rounds\"]\n", "del param[\"weight\"]" ] }, { - "cell_type": "code", - "execution_count": 16, + "cell_type": "markdown", "metadata": {}, - "outputs": [ + "source": [ + "#### (iii) Repeating 5-fold CV for best set of hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[05:46:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[05:49:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[05:53:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[05:56:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[05:59:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Loss values: [105.30552432607526, 115.14142937211616, 113.2335149181909, 133.1620701477483, 122.36331116450947]\n", + "Accuracies: [0.9023777478689996, 0.8997137233852209, 0.9020015596568755, 0.8933285120231423, 0.8975529583637691]\n", + "ROC-AUC scores: [0.9565390334782213, 0.9511082440869931, 0.9489242322179354, 0.9441804521532098, 0.9494393660817402]\n" + ] + } + ], + "source": [ + "loss = []\n", + "accuracy = []\n", + "ROC_AUC = []\n", + "\n", + "for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(np.array(train_X[train_index]), weight = weights[train_index],\n", + " label = np.array(train_y[train_index]))\n", + " dvalid = xgb.DMatrix(np.array(train_X[test_index]))\n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=1)\n", + " y_valid_pred = np.round(bst.predict(dvalid))\n", + " validation_y = train_y[test_index]\n", + "\n", + " #calculate loss:\n", + " false_positive = 100*(1-np.mean(np.array(validation_y)[y_valid_pred == 1]))\n", + " false_negative = 100*(np.mean(np.array(validation_y)[y_valid_pred == 0]))\n", + " logging.info(\"False positive rate: \" + str(false_positive)+ \"; False negative rate: \" + str(false_negative))\n", + " loss.append(2*(false_negative**2) + false_positive**1.3)\n", + " #calculate accuracy:\n", + " accuracy.append(np.mean(y_valid_pred == np.array(validation_y)))\n", + " #calculate ROC-AUC score:\n", + " ROC_AUC.append(roc_auc_score(np.array(validation_y), bst.predict(dvalid)))\n", + " \n", + "print(\"Loss values: %s\" %loss) \n", + "print(\"Accuracies: %s\" %accuracy)\n", + "print(\"ROC-AUC scores: %s\" %ROC_AUC)\n", + "\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ts_ECFP_2048.npy\"), np.array(accuracy))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"loss_CV_xgboost_ESM1b_ts_ECFP_2048.npy\"), np.array(loss))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ts_ECFP_2048.npy\"), np.array(ROC_AUC))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (iv) 3. Training and validating the final model\n", + "Training the model and validating it on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[06:02:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Accuracy on test set: 0.8977635782747604, ROC-AUC score for test set: 0.9484819112091449, MCC: 0.7428505008103589\n" + ] + } + ], + "source": [ + "dtrain = xgb.DMatrix(np.array(train_X), weight = weights, label = np.array(train_y),\n", + " feature_names= feature_names)\n", + "dtest = xgb.DMatrix(np.array(test_X), label = np.array(test_y),\n", + " feature_names= feature_names)\n", + "\n", + "bst = xgb.train(param, dtrain, int(num_round), verbose_eval=1)\n", + "y_test_pred = np.round(bst.predict(dtest))\n", + "acc_test = np.mean(y_test_pred == np.array(test_y))\n", + "roc_auc = roc_auc_score(np.array(test_y), bst.predict(dtest))\n", + "mcc = matthews_corrcoef(np.array(test_y), y_test_pred)\n", + "\n", + "print(\"Accuracy on test set: %s, ROC-AUC score for test set: %s, MCC: %s\" % (acc_test, roc_auc, mcc))\n", + "\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_ECFP_2048.npy\"), bst.predict(dtest))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_ECFP_2048.npy\"), test_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (f) ESM1b_ts and GNN (pretrained):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (i) Creating numpy arrays with input vectors and output variables" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def create_input_and_output_data(df):\n", + " X = ();\n", + " y = ();\n", + " \n", + " for ind in df.index:\n", + " emb = df[\"ESM1b_ts\"][ind]\n", + " ecfp = df[\"GNN rep (pretrained)\"][ind]\n", + " \n", + " X = X +(np.concatenate([ecfp, emb]), );\n", + " y = y + (df[\"Binding\"][ind], );\n", + "\n", + " return(X,y)\n", + "\n", + "train_X, train_y = create_input_and_output_data(df = df_train)\n", + "test_X, test_y = create_input_and_output_data(df = df_test)\n", + "\n", + "\n", + "feature_names = [\"GNN rep_\" + str(i) for i in range(50)]\n", + "feature_names = feature_names + [\"ESM1b_\" + str(i) for i in range(1280)]\n", + "\n", + "train_X = np.array(train_X)\n", + "test_X = np.array(test_X)\n", + "\n", + "train_y = np.array(train_y)\n", + "test_y = np.array(test_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (ii) Performing hyperparameter optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_validation_neg_acc_gradient_boosting(param):\n", + " num_round = param[\"num_rounds\"]\n", + " param[\"tree_method\"] = \"gpu_hist\"\n", + " param[\"sampling_method\"] = \"gradient_based\"\n", + " param['objective'] = 'binary:logistic'\n", + " weights = np.array([param[\"weight\"] if binding == 0 else 1.0 for binding in df_train[\"Binding\"]])\n", + " \n", + " del param[\"num_rounds\"]\n", + " del param[\"weight\"]\n", + " \n", + " loss = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(np.array(train_X[train_index]), weight = weights[train_index],\n", + " label = np.array(train_y[train_index]))\n", + " dvalid = xgb.DMatrix(np.array(train_X[test_index]))\n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=1)\n", + " y_valid_pred = np.round(bst.predict(dvalid))\n", + " validation_y = train_y[test_index]\n", + " \n", + " false_positive = 100*(1-np.mean(np.array(validation_y)[y_valid_pred == 1]))\n", + " false_negative = 100*(np.mean(np.array(validation_y)[y_valid_pred == 0]))\n", + " logging.info(\"False positive rate: \" + str(false_positive)+ \"; False negative rate: \" + str(false_negative))\n", + " loss.append(2*(false_negative**2) + false_positive**1.3)\n", + " return(np.mean(loss))\n", + "\n", + "#Defining search space for hyperparameter optimization\n", + "space_gradient_boosting = {\"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 0.5),\n", + " \"max_depth\": hp.choice(\"max_depth\", [9,10,11,12,13]),\n", + " \"reg_lambda\": hp.uniform(\"reg_lambda\", 0, 5),\n", + " \"reg_alpha\": hp.uniform(\"reg_alpha\", 0, 5),\n", + " \"max_delta_step\": hp.uniform(\"max_delta_step\", 0, 5),\n", + " \"min_child_weight\": hp.uniform(\"min_child_weight\", 0.1, 15),\n", + " \"num_rounds\": hp.uniform(\"num_rounds\", 200, 400),\n", + " \"weight\" : hp.uniform(\"weight\", 0.1,0.33)}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Performing a random grid search:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "'''trials = Trials()\n", + "\n", + "for i in range(1,2000):\n", + " best = fmin(fn = cross_validation_neg_acc_gradient_boosting, space = space_gradient_boosting,\n", + " algo = rand.suggest, max_evals = i, trials = trials)\n", + " logging.info(i)\n", + " logging.info(trials.best_trial[\"result\"][\"loss\"])\n", + " logging.info(trials.argmin)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Best set of hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'learning_rate': 0.19789627044374644,\n", + " 'max_delta_step': 3.815106738298364,\n", + " 'max_depth': 12,\n", + " 'min_child_weight': 0.9568708633806051,\n", + " 'num_rounds': 358.42154962618235,\n", + " 'reg_alpha': 0.3726209284173021,\n", + " 'reg_lambda': 4.442065146895246,\n", + " 'weight': 0.11281944917093198}\n", + "\n", + "num_round = param[\"num_rounds\"]\n", + "param[\"tree_method\"] = \"gpu_hist\"\n", + "param[\"sampling_method\"] = \"gradient_based\"\n", + "param['objective'] = 'binary:logistic'\n", + "weights = np.array([param[\"weight\"] if binding == 0 else 1.0 for binding in df_train[\"Binding\"]])\n", + "\n", + "del param[\"num_rounds\"]\n", + "del param[\"weight\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (iii) Repeating 5-fold CV for best set of hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[06:07:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[06:09:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[06:11:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[06:14:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[06:16:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Loss values: [91.88073293137697, 99.67882761750269, 109.23993476370345, 120.01888413247693, 106.0349108806078]\n", + "Accuracies: [0.917900403768506, 0.9150116299874754, 0.9098864916385062, 0.9069788465015368, 0.9120708546384222]\n", + "ROC-AUC scores: [0.9604579328670103, 0.9560894222080177, 0.9530202250038822, 0.9515046007045915, 0.9566354023173045]\n" + ] + } + ], + "source": [ + "loss = []\n", + "accuracy = []\n", + "ROC_AUC = []\n", + "\n", + "for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(np.array(train_X[train_index]), weight = weights[train_index],\n", + " label = np.array(train_y[train_index]))\n", + " dvalid = xgb.DMatrix(np.array(train_X[test_index]))\n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=1)\n", + " y_valid_pred = np.round(bst.predict(dvalid))\n", + " validation_y = train_y[test_index]\n", + "\n", + " #calculate loss:\n", + " false_positive = 100*(1-np.mean(np.array(validation_y)[y_valid_pred == 1]))\n", + " false_negative = 100*(np.mean(np.array(validation_y)[y_valid_pred == 0]))\n", + " logging.info(\"False positive rate: \" + str(false_positive)+ \"; False negative rate: \" + str(false_negative))\n", + " loss.append(2*(false_negative**2) + false_positive**1.3)\n", + " #calculate accuracy:\n", + " accuracy.append(np.mean(y_valid_pred == np.array(validation_y)))\n", + " #calculate ROC-AUC score:\n", + " ROC_AUC.append(roc_auc_score(np.array(validation_y), bst.predict(dvalid)))\n", + " \n", + "print(\"Loss values: %s\" %loss) \n", + "print(\"Accuracies: %s\" %accuracy)\n", + "print(\"ROC-AUC scores: %s\" %ROC_AUC)\n", + "\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ts_GNN_pretrained.npy\"), np.array(accuracy))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"loss_CV_xgboost_ESM1b_ts_GNN_pretrained.npy\"), np.array(loss))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ts_GNN_pretrained.npy\"), np.array(ROC_AUC))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[06:18:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Accuracy on test set: 0.9181217029496991, ROC-AUC score for test set: 0.9562220354082519, MCC: 0.7889261156069193\n" + ] + } + ], + "source": [ + "dtrain = xgb.DMatrix(np.array(train_X), weight = weights, label = np.array(train_y),\n", + " feature_names= feature_names)\n", + "dtest = xgb.DMatrix(np.array(test_X), label = np.array(test_y),\n", + " feature_names= feature_names)\n", + "\n", + "bst = xgb.train(param, dtrain, int(num_round), verbose_eval=1)\n", + "y_test_pred = np.round(bst.predict(dtest))\n", + "acc_test = np.mean(y_test_pred == np.array(test_y))\n", + "roc_auc = roc_auc_score(np.array(test_y), bst.predict(dtest))\n", + "mcc = matthews_corrcoef(np.array(test_y), y_test_pred)\n", + "\n", + "print(\"Accuracy on test set: %s, ROC-AUC score for test set: %s, MCC: %s\" % (acc_test, roc_auc, mcc))\n", + "\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_GNN_pretrained.npy\"), bst.predict(dtest))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_GNN_pretrained.npy\"), test_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (g) ESM1b_ts (mean representaion) and GNN (pretrained):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (i) Creating numpy arrays with input vectors and output variables" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "def create_input_and_output_data(df):\n", + " X = ();\n", + " y = ();\n", + " \n", + " for ind in df.index:\n", + " emb = df[\"ESM1b_ts_mean\"][ind]\n", + " ecfp = df[\"GNN rep (pretrained)\"][ind]\n", + " \n", + " X = X +(np.concatenate([ecfp, emb]), );\n", + " y = y + (df[\"Binding\"][ind], );\n", + "\n", + " return(X,y)\n", + "\n", + "train_X, train_y = create_input_and_output_data(df = df_train)\n", + "test_X, test_y = create_input_and_output_data(df = df_test)\n", + "\n", + "\n", + "feature_names = [\"GNN rep_\" + str(i) for i in range(50)]\n", + "feature_names = feature_names + [\"ESM1b_\" + str(i) for i in range(1280)]\n", + "\n", + "train_X = np.array(train_X)\n", + "test_X = np.array(test_X)\n", + "\n", + "train_y = np.array(train_y)\n", + "test_y = np.array(test_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (ii) Performing hyperparameter optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_validation_neg_acc_gradient_boosting(param):\n", + " num_round = param[\"num_rounds\"]\n", + " param[\"tree_method\"] = \"gpu_hist\"\n", + " param[\"sampling_method\"] = \"gradient_based\"\n", + " param['objective'] = 'binary:logistic'\n", + " weights = np.array([param[\"weight\"] if binding == 0 else 1.0 for binding in df_train[\"Binding\"]])\n", + " \n", + " del param[\"num_rounds\"]\n", + " del param[\"weight\"]\n", + " \n", + " loss = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(np.array(train_X[train_index]), weight = weights[train_index],\n", + " label = np.array(train_y[train_index]))\n", + " dvalid = xgb.DMatrix(np.array(train_X[test_index]))\n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=1)\n", + " y_valid_pred = np.round(bst.predict(dvalid))\n", + " validation_y = train_y[test_index]\n", + " \n", + " false_positive = 100*(1-np.mean(np.array(validation_y)[y_valid_pred == 1]))\n", + " false_negative = 100*(np.mean(np.array(validation_y)[y_valid_pred == 0]))\n", + " logging.info(\"False positive rate: \" + str(false_positive)+ \"; False negative rate: \" + str(false_negative))\n", + " loss.append(2*(false_negative**2) + false_positive**1.3)\n", + " return(np.mean(loss))\n", + "\n", + "#Defining search space for hyperparameter optimization\n", + "space_gradient_boosting = {\"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 0.5),\n", + " \"max_depth\": hp.choice(\"max_depth\", [9,10,11,12,13]),\n", + " \"reg_lambda\": hp.uniform(\"reg_lambda\", 0, 5),\n", + " \"reg_alpha\": hp.uniform(\"reg_alpha\", 0, 5),\n", + " \"max_delta_step\": hp.uniform(\"max_delta_step\", 0, 5),\n", + " \"min_child_weight\": hp.uniform(\"min_child_weight\", 0.1, 15),\n", + " \"num_rounds\": hp.uniform(\"num_rounds\", 200, 400),\n", + " \"weight\" : hp.uniform(\"weight\", 0.1,0.33)}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Performing a random grid search:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "'''trials = Trials()\n", + "\n", + "for i in range(1,2000):\n", + " best = fmin(fn = cross_validation_neg_acc_gradient_boosting, space = space_gradient_boosting,\n", + " algo = rand.suggest, max_evals = i, trials = trials)\n", + " logging.info(i)\n", + " logging.info(trials.best_trial[\"result\"][\"loss\"])\n", + " logging.info(trials.argmin)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Best set of hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'learning_rate': 0.1156069031228949,\n", + " 'max_delta_step': 2.6918966267028415,\n", + " 'max_depth': 12,\n", + " 'min_child_weight': 1.9758135134127655,\n", + " 'num_rounds': 127.079967978755,\n", + " 'reg_alpha': 2.3233749696436714,\n", + " 'reg_lambda': 2.163811098458429,\n", + " 'weight': 0.28341863683389795}\n", + "\n", + "num_round = param[\"num_rounds\"]\n", + "param[\"tree_method\"] = \"gpu_hist\"\n", + "param[\"sampling_method\"] = \"gradient_based\"\n", + "param['objective'] = 'binary:logistic'\n", + "weights = np.array([param[\"weight\"] if binding == 0 else 1.0 for binding in df_train[\"Binding\"]])\n", + "\n", + "del param[\"num_rounds\"]\n", + "del param[\"weight\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (iii) Repeating 5-fold CV for best set of hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[06:22:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[06:23:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[06:24:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[06:25:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[06:26:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Loss values: [84.09770237588249, 93.2762385821601, 105.77465665299039, 112.21969637517623, 101.68673959966944]\n", + "Accuracies: [0.916733961417676, 0.9144748613347647, 0.9059873494497878, 0.9043572590851564, 0.910427319211103]\n", + "ROC-AUC scores: [0.9636427269607835, 0.9587725902992912, 0.9556605491176462, 0.9545229260971622, 0.9585052255048616]\n" + ] + } + ], + "source": [ + "loss = []\n", + "accuracy = []\n", + "ROC_AUC = []\n", + "\n", + "for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(np.array(train_X[train_index]), weight = weights[train_index],\n", + " label = np.array(train_y[train_index]))\n", + " dvalid = xgb.DMatrix(np.array(train_X[test_index]))\n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=1)\n", + " y_valid_pred = np.round(bst.predict(dvalid))\n", + " validation_y = train_y[test_index]\n", + "\n", + " #calculate loss:\n", + " false_positive = 100*(1-np.mean(np.array(validation_y)[y_valid_pred == 1]))\n", + " false_negative = 100*(np.mean(np.array(validation_y)[y_valid_pred == 0]))\n", + " logging.info(\"False positive rate: \" + str(false_positive)+ \"; False negative rate: \" + str(false_negative))\n", + " loss.append(2*(false_negative**2) + false_positive**1.3)\n", + " #calculate accuracy:\n", + " accuracy.append(np.mean(y_valid_pred == np.array(validation_y)))\n", + " #calculate ROC-AUC score:\n", + " ROC_AUC.append(roc_auc_score(np.array(validation_y), bst.predict(dvalid)))\n", + " \n", + "print(\"Loss values: %s\" %loss) \n", + "print(\"Accuracies: %s\" %accuracy)\n", + "print(\"ROC-AUC scores: %s\" %ROC_AUC)\n", + "\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ts_mean_GNN_pretrained.npy\"), np.array(accuracy))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"loss_CV_xgboost_ESM1b_ts_mean_GNN_pretrained.npy\"), np.array(loss))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ts_mean_GNN_pretrained.npy\"), np.array(ROC_AUC))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[06:27:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Accuracy on test set: 0.9101716323649602, ROC-AUC score for test set: 0.956069174706691, MCC: 0.7725739053747791\n" + ] + } + ], + "source": [ + "dtrain = xgb.DMatrix(np.array(train_X), weight = weights, label = np.array(train_y),\n", + " feature_names= feature_names)\n", + "dtest = xgb.DMatrix(np.array(test_X), label = np.array(test_y),\n", + " feature_names= feature_names)\n", + "\n", + "bst = xgb.train(param, dtrain, int(num_round), verbose_eval=1)\n", + "y_test_pred = np.round(bst.predict(dtest))\n", + "acc_test = np.mean(y_test_pred == np.array(test_y))\n", + "roc_auc = roc_auc_score(np.array(test_y), bst.predict(dtest))\n", + "mcc = matthews_corrcoef(np.array(test_y), y_test_pred)\n", + "\n", + "print(\"Accuracy on test set: %s, ROC-AUC score for test set: %s, MCC: %s\" % (acc_test, roc_auc, mcc))\n", + "\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_mean_GNN_pretrained.npy\"), bst.predict(dtest))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_mean_GNN_pretrained.npy\"), test_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (h) ESM1b_ts and GNN (not pre-trained):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (i) Creating numpy arrays with input vectors and output variables" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "def create_input_and_output_data(df):\n", + " X = ();\n", + " y = ();\n", + " \n", + " for ind in df.index:\n", + " emb = df[\"ESM1b_ts\"][ind]\n", + " ecfp = df[\"GNN rep\"][ind]\n", + " \n", + " X = X +(np.concatenate([ecfp, emb]), );\n", + " y = y + (df[\"Binding\"][ind], );\n", + "\n", + " return(X,y)\n", + "\n", + "train_X, train_y = create_input_and_output_data(df = df_train)\n", + "test_X, test_y = create_input_and_output_data(df = df_test)\n", + "\n", + "\n", + "feature_names = [\"GNN rep_\" + str(i) for i in range(50)]\n", + "feature_names = feature_names + [\"ESM1b_\" + str(i) for i in range(1280)]\n", + "\n", + "train_X = np.array(train_X)\n", + "test_X = np.array(test_X)\n", + "\n", + "train_y = np.array(train_y)\n", + "test_y = np.array(test_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (ii) Performing hyperparameter optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_validation_neg_acc_gradient_boosting(param):\n", + " num_round = param[\"num_rounds\"]\n", + " param[\"tree_method\"] = \"gpu_hist\"\n", + " param[\"sampling_method\"] = \"gradient_based\"\n", + " param['objective'] = 'binary:logistic'\n", + " weights = np.array([param[\"weight\"] if binding == 0 else 1.0 for binding in df_train[\"Binding\"]])\n", + " \n", + " del param[\"num_rounds\"]\n", + " del param[\"weight\"]\n", + " \n", + " loss = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(np.array(train_X[train_index]), weight = weights[train_index],\n", + " label = np.array(train_y[train_index]))\n", + " dvalid = xgb.DMatrix(np.array(train_X[test_index]))\n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=1)\n", + " y_valid_pred = np.round(bst.predict(dvalid))\n", + " validation_y = train_y[test_index]\n", + " \n", + " false_positive = 100*(1-np.mean(np.array(validation_y)[y_valid_pred == 1]))\n", + " false_negative = 100*(np.mean(np.array(validation_y)[y_valid_pred == 0]))\n", + " logging.info(\"False positive rate: \" + str(false_positive)+ \"; False negative rate: \" + str(false_negative))\n", + " loss.append(2*(false_negative**2) + false_positive**1.3)\n", + " return(np.mean(loss))\n", + "\n", + "#Defining search space for hyperparameter optimization\n", + "space_gradient_boosting = {\"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 0.5),\n", + " \"max_depth\": hp.choice(\"max_depth\", [9,10,11,12,13]),\n", + " \"reg_lambda\": hp.uniform(\"reg_lambda\", 0, 5),\n", + " \"reg_alpha\": hp.uniform(\"reg_alpha\", 0, 5),\n", + " \"max_delta_step\": hp.uniform(\"max_delta_step\", 0, 5),\n", + " \"min_child_weight\": hp.uniform(\"min_child_weight\", 0.1, 15),\n", + " \"num_rounds\": hp.uniform(\"num_rounds\", 200, 400),\n", + " \"weight\" : hp.uniform(\"weight\", 0.1,0.33)}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Performing a random grid search:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "'''trials = Trials()\n", + "\n", + "for i in range(1,2000):\n", + " best = fmin(fn = cross_validation_neg_acc_gradient_boosting, space = space_gradient_boosting,\n", + " algo = rand.suggest, max_evals = i, trials = trials)\n", + " logging.info(i)\n", + " logging.info(trials.best_trial[\"result\"][\"loss\"])\n", + " logging.info(trials.argmin)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Best set of hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'learning_rate': 0.18444025726334898,\n", + " 'max_delta_step': 3.2748796106084117,\n", + " 'max_depth': 13,\n", + " 'min_child_weight': 3.1946753845027738,\n", + " 'num_rounds': 314.1036429221291,\n", + " 'reg_alpha': 0.48821021807600673,\n", + " 'reg_lambda': 2.6236829011598073,\n", + " 'weight': 0.1264521266931227}\n", + "\n", + "num_round = param[\"num_rounds\"]\n", + "param[\"tree_method\"] = \"gpu_hist\"\n", + "param[\"sampling_method\"] = \"gradient_based\"\n", + "param['objective'] = 'binary:logistic'\n", + "weights = np.array([param[\"weight\"] if binding == 0 else 1.0 for binding in df_train[\"Binding\"]])\n", + "\n", + "del param[\"num_rounds\"]\n", + "del param[\"weight\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (iii) Repeating 5-fold CV for best set of hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[06:28:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[06:30:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[06:31:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[06:32:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "[06:34:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Loss values: [95.85892125174448, 101.18538008374776, 107.3774676337244, 113.8545092756927, 103.56995296940914]\n", + "Accuracies: [0.9084791386271871, 0.9079441760601181, 0.9039944545533316, 0.9011028747062014, 0.910336011687363]\n", + "ROC-AUC scores: [0.9591609388450508, 0.9585739135344192, 0.9547930983970586, 0.9530322066116591, 0.9565295911826541]\n" + ] + } + ], + "source": [ + "loss = []\n", + "accuracy = []\n", + "ROC_AUC = []\n", + "\n", + "for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(np.array(train_X[train_index]), weight = weights[train_index],\n", + " label = np.array(train_y[train_index]))\n", + " dvalid = xgb.DMatrix(np.array(train_X[test_index]))\n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=1)\n", + " y_valid_pred = np.round(bst.predict(dvalid))\n", + " validation_y = train_y[test_index]\n", + "\n", + " #calculate loss:\n", + " false_positive = 100*(1-np.mean(np.array(validation_y)[y_valid_pred == 1]))\n", + " false_negative = 100*(np.mean(np.array(validation_y)[y_valid_pred == 0]))\n", + " logging.info(\"False positive rate: \" + str(false_positive)+ \"; False negative rate: \" + str(false_negative))\n", + " loss.append(2*(false_negative**2) + false_positive**1.3)\n", + " #calculate accuracy:\n", + " accuracy.append(np.mean(y_valid_pred == np.array(validation_y)))\n", + " #calculate ROC-AUC score:\n", + " ROC_AUC.append(roc_auc_score(np.array(validation_y), bst.predict(dvalid)))\n", + " \n", + "print(\"Loss values: %s\" %loss) \n", + "print(\"Accuracies: %s\" %accuracy)\n", + "print(\"ROC-AUC scores: %s\" %ROC_AUC)\n", + "\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ts_GNN.npy\"), np.array(accuracy))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"loss_CV_xgboost_ESM1b_ts_GNN.npy\"), np.array(loss))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ts_GNN.npy\"), np.array(ROC_AUC))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (iv) 3. Training and validating the final model\n", + "Training the model and validating it on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[06:35:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Accuracy on test set: 0.9063823463853183, ROC-AUC score for test set: 0.9546895380125349, MCC: 0.763088327957358\n" + ] + } + ], + "source": [ + "dtrain = xgb.DMatrix(np.array(train_X), weight = weights, label = np.array(train_y),\n", + " feature_names= feature_names)\n", + "dtest = xgb.DMatrix(np.array(test_X), label = np.array(test_y),\n", + " feature_names= feature_names)\n", + "\n", + "bst = xgb.train(param, dtrain, int(num_round), verbose_eval=1)\n", + "y_test_pred = np.round(bst.predict(dtest))\n", + "acc_test = np.mean(y_test_pred == np.array(test_y))\n", + "roc_auc = roc_auc_score(np.array(test_y), bst.predict(dtest))\n", + "mcc = matthews_corrcoef(np.array(test_y), y_test_pred)\n", + "\n", + "print(\"Accuracy on test set: %s, ROC-AUC score for test set: %s, MCC: %s\" % (acc_test, roc_auc, mcc))\n", + "\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_GNN.npy\"), bst.predict(dtest))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_GNN.npy\"), test_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.Training the best model with training and test set (production mode):" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "def create_input_and_output_data(df):\n", + " X = ();\n", + " y = ();\n", + " \n", + " for ind in df.index:\n", + " emb = df[\"ESM1b_ts\"][ind]\n", + " ecfp = np.array(list(df[\"ECFP\"][ind])).astype(int)\n", + " \n", + " X = X +(np.concatenate([ecfp, emb]), );\n", + " y = y + (df[\"Binding\"][ind], );\n", + "\n", + " return(X,y)\n", + "\n", + "train_X, train_y = create_input_and_output_data(df = df_train)\n", + "test_X, test_y = create_input_and_output_data(df = df_test)\n", + "\n", + "\n", + "feature_names = [\"ECFP_\" + str(i) for i in range(1024)]\n", + "feature_names = feature_names + [\"ESM1b_ts_\" + str(i) for i in range(1280)]\n", + "\n", + "train_X = np.array(train_X)\n", + "test_X = np.array(test_X)\n", + "\n", + "train_y = np.array(train_y)\n", + "test_y = np.array(test_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "train_X = np.concatenate([train_X, test_X])\n", + "train_y = np.concatenate([train_y, test_y])" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'learning_rate': 0.31553117247348733,\n", + " 'max_delta_step': 1.7726044219753656,\n", + " 'max_depth': 10,\n", + " 'min_child_weight': 1.3845040588450772,\n", + " 'num_rounds': 342.68325188584106,\n", + " 'reg_alpha': 0.531395259755843,\n", + " 'reg_lambda': 3.744980563764689,\n", + " 'weight': 0.26187490421514203}\n", + "\n", + "num_round = param[\"num_rounds\"]\n", + "param[\"tree_method\"] = \"gpu_hist\"\n", + "param[\"sampling_method\"] = \"gradient_based\"\n", + "param['objective'] = 'binary:logistic'\n", + "weights = np.array([param[\"weight\"] if binding == 0 else 1.0 for binding in train_y])\n", + "\n", + "del param[\"num_rounds\"]\n", + "del param[\"weight\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on test set: 0.9953592814371257, ROC-AUC score for test set: 0.9999793621872437, MCC: 0.9881988254638187\n" + "[06:38:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "Accuracy on test set: 0.995690615944721, ROC-AUC score for test set: 0.999973562453436, MCC: 0.9890341165154867\n" ] } ], @@ -1483,14 +2375,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on train set: 0.9949893978563337, ROC-AUC score for train set: 0.9999647197351464, MCC: 0.9872677880401014\n" + "Accuracy on train set: 0.9952117916840937, ROC-AUC score for train set: 0.9999666505894647, MCC: 0.9878260302574451\n" ] } ], @@ -1505,7 +2397,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -1516,7 +2408,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1530,7 +2422,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.8.13" } }, "nbformat": 4, diff --git a/notebooks_and_code/2_4 - Training additional machine learning models.ipynb b/notebooks_and_code/2_4 - Training additional machine learning models.ipynb new file mode 100644 index 0000000..af40f65 --- /dev/null +++ b/notebooks_and_code/2_4 - Training additional machine learning models.ipynb @@ -0,0 +1,658 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training additional machine learning models for the task of enzyme-substrate pair prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Loading and preprocessing data for model training and evaluation\n", + "### 2. Training and validation machine learning models" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\alexk\\anaconda3\\envs\\ESP\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import random\n", + "import pickle\n", + "import sys\n", + "import os\n", + "import logging\n", + "from os.path import join\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.metrics import roc_auc_score\n", + "#from hyperopt import fmin, tpe, hp, Trials, rand\n", + "import xgboost as xgb\n", + "from sklearn.metrics import matthews_corrcoef\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.utils.class_weight import compute_class_weight\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "\n", + "sys.path.append('.\\\\additional_code')\n", + "#from data_preprocessing import *\n", + "\n", + "CURRENT_DIR = os.getcwd()\n", + "print(CURRENT_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Loading and preprocessing data for model training and evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def array_column_to_strings(df, column):\n", + " df[column] = [str(list(df[column][ind])) for ind in df.index]\n", + " return(df)\n", + "\n", + "def string_column_to_array(df, column):\n", + " df[column] = [np.array(eval(df[column][ind])) for ind in df.index]\n", + " return(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (a) Loading data: \n", + "Only keeping data points from the GO Annotation database with experimental evidence" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\alexk\\anaconda3\\envs\\ESP\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:73: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " result = libops.scalar_compare(x.ravel(), y, op)\n", + "C:\\Users\\alexk\\anaconda3\\envs\\ESP\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:73: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " result = libops.scalar_compare(x.ravel(), y, op)\n" + ] + } + ], + "source": [ + "df_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"df_train_with_ESM1b_ts_GNN.pkl\"))\n", + "df_train = df_train.loc[df_train[\"ESM1b\"] != \"\"]\n", + "df_train = df_train.loc[df_train[\"type\"] != \"engqvist\"]\n", + "df_train = df_train.loc[df_train[\"GNN rep\"] != \"\"]\n", + "df_train.reset_index(inplace = True, drop = True)\n", + "\n", + "df_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"df_test_with_ESM1b_ts_GNN.pkl\"))\n", + "df_test = df_test.loc[df_test[\"ESM1b\"] != \"\"]\n", + "df_test = df_test.loc[df_test[\"type\"] != \"engqvist\"]\n", + "df_test = df_test.loc[df_test[\"GNN rep\"] != \"\"]\n", + "df_test.reset_index(inplace = True, drop = True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (b) Splitting training set into 5-folds for hyperparameter optimization:\n", + "The 5 folds are created in such a way that the same enzyme does not occure in two different folds" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def split_dataframe(df, frac):\n", + " df1 = pd.DataFrame(columns = list(df.columns))\n", + " df2 = pd.DataFrame(columns = list(df.columns))\n", + " try:\n", + " df.drop(columns = [\"level_0\"], inplace = True)\n", + " except: \n", + " pass\n", + " df.reset_index(inplace = True)\n", + " \n", + " train_indices = []\n", + " test_indices = []\n", + " ind = 0\n", + " while len(train_indices) +len(test_indices) < len(df):\n", + " if ind not in train_indices and ind not in test_indices:\n", + " if ind % frac != 0:\n", + " n_old = len(train_indices)\n", + " train_indices.append(ind)\n", + " train_indices = list(set(train_indices))\n", + "\n", + " while n_old != len(train_indices):\n", + " n_old = len(train_indices)\n", + "\n", + " training_seqs= list(set(df[\"ESM1b\"].loc[train_indices]))\n", + "\n", + " train_indices = train_indices + (list(df.loc[df[\"ESM1b\"].isin(training_seqs)].index))\n", + " train_indices = list(set(train_indices))\n", + " \n", + " else:\n", + " n_old = len(test_indices)\n", + " test_indices.append(ind)\n", + " test_indices = list(set(test_indices))\n", + "\n", + " while n_old != len(test_indices):\n", + " n_old = len(test_indices)\n", + "\n", + " testing_seqs= list(set(df[\"ESM1b\"].loc[test_indices]))\n", + "\n", + " test_indices = test_indices + (list(df.loc[df[\"ESM1b\"].isin(testing_seqs)].index))\n", + " test_indices = list(set(test_indices))\n", + " \n", + " ind +=1\n", + " return(df.loc[train_indices], df.loc[test_indices])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "'''data_train2 = df_train.copy()\n", + "data_train2 = array_column_to_strings(data_train2, column = \"ESM1b\")\n", + "\n", + "data_train2, df_fold = split_dataframe(df = data_train2, frac=5)\n", + "indices_fold1 = list(df_fold[\"index\"])\n", + "print(len(data_train2), len(indices_fold1))#\n", + "\n", + "data_train2, df_fold = split_dataframe(df = data_train2, frac=4)\n", + "indices_fold2 = list(df_fold[\"index\"])\n", + "print(len(data_train2), len(indices_fold2))\n", + "\n", + "data_train2, df_fold = split_dataframe(df = data_train2, frac=3)\n", + "indices_fold3 = list(df_fold[\"index\"])\n", + "print(len(data_train2), len(indices_fold3))\n", + "\n", + "data_train2, df_fold = split_dataframe(df = data_train2, frac=2)\n", + "indices_fold4 = list(df_fold[\"index\"])\n", + "indices_fold5 = list(data_train2[\"index\"])\n", + "print(len(data_train2), len(indices_fold4))\n", + "\n", + "\n", + "fold_indices = [indices_fold1, indices_fold2, indices_fold3, indices_fold4, indices_fold5]\n", + "\n", + "train_indices = [[], [], [], [], []]\n", + "test_indices = [[], [], [], [], []]\n", + "\n", + "for i in range(5):\n", + " for j in range(5):\n", + " if i != j:\n", + " train_indices[i] = train_indices[i] + fold_indices[j]\n", + " \n", + " test_indices[i] = fold_indices[i]\n", + " \n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"CV_train_indices.npy\"), train_indices)\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"CV_test_indices.npy\"), test_indices)''';" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "train_indices = list(np.load(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"CV_train_indices.npy\"), allow_pickle=True))\n", + "test_indices = list(np.load(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"CV_test_indices.npy\"), allow_pickle=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating numpy arrays with input vectors and output variables" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def create_input_and_output_data(df):\n", + " X = ();\n", + " y = ();\n", + " \n", + " for ind in df.index:\n", + " emb = df[\"ESM1b_ts\"][ind]\n", + " ecfp = np.array(list(df[\"ECFP\"][ind])).astype(int)\n", + " \n", + " X = X +(np.concatenate([ecfp, emb]), );\n", + " y = y + (df[\"Binding\"][ind], );\n", + "\n", + " return(X,y)\n", + "\n", + "train_X, train_y = create_input_and_output_data(df = df_train)\n", + "test_X, test_y = create_input_and_output_data(df = df_test)\n", + "\n", + "\n", + "feature_names = [\"ECFP_\" + str(i) for i in range(1024)]\n", + "feature_names = feature_names + [\"ESM1b_ts_\" + str(i) for i in range(1280)]\n", + "\n", + "train_X = np.array(train_X)\n", + "test_X = np.array(test_X)\n", + "\n", + "train_y = np.array(train_y)\n", + "test_y = np.array(test_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "classes = np.unique(train_y)\n", + "weights = compute_class_weight(class_weight='balanced', classes=classes, y=train_y)\n", + "class_weights = dict(zip(classes, weights))\n", + "\n", + "# normalize the features\n", + "scaler = StandardScaler()\n", + "scaler.fit(train_X)\n", + "train_X = scaler.transform(train_X)\n", + "test_X = scaler.transform(test_X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and validation machine learning models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (a) Logistic Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (ii) Performing hyperparameter optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_validation_neg_acc_logistic_regression(param):\n", + " param['solver'] = 'liblinear'\n", + " param['class_weight'] = class_weights\n", + " \n", + " loss = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i] \n", + " \n", + " clf = LogisticRegression(max_iter=20, penalty = param[\"penalty\"],\n", + " C = param[\"C\"], solver = param['solver'],\n", + " class_weight= param[\"class_weight\"]\n", + " ).fit(train_X[train_index], train_y[train_index])\n", + " y_valid_pred = np.round(clf.predict(train_X[test_index]))\n", + " validation_y = train_y[test_index]\n", + " \n", + " false_positive = 100*(1-np.mean(np.array(validation_y)[y_valid_pred == 1]))\n", + " false_negative = 100*(np.mean(np.array(validation_y)[y_valid_pred == 0]))\n", + " logging.info(\"False positive rate: \" + str(false_positive)+ \"; False negative rate: \" + str(false_negative))\n", + " loss.append(2*(false_negative**2) + false_positive**1.3)\n", + " return(np.mean(loss))\n", + "\n", + "#Defining search space for hyperparameter optimization\n", + "space_logistic_regression = {'C': hp.choice('C', [1, 10, 100, 1000]),\n", + " 'penalty': hp.choice('penalty', ['l1', 'l2'])}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Performing a random grid search:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "'''trials = Trials()\n", + "\n", + "for i in range(1,10):\n", + " best = fmin(fn = cross_validation_neg_acc_logistic_regression, space = space_logistic_regression,\n", + " algo = rand.suggest, max_evals = i, trials = trials)\n", + " print(i)\n", + " print(trials.best_trial[\"result\"][\"loss\"])\n", + " print(trials.argmin)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Best set of hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "param = {\"C\" : 1,\n", + " \"penalty\" : \"l2\"}\n", + "\n", + "param['solver'] = 'liblinear'\n", + "param['class_weight'] = class_weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (iii) Repeating 5-fold CV for best set of hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss values: [1481.1517290256324, 1527.9477878588439, 1610.891134117497, 1481.9223786505895, 1493.7427890128495]\n", + "Accuracies: [0.555252882956506, 0.5611802674043338, 0.542320304883453, 0.5568644818423384, 0.5641326945674772]\n", + "ROC-AUC scores: [0.5268393811621369, 0.5262103625271073, 0.5149750024593845, 0.5375482138817793, 0.5385738017600883]\n" + ] + } + ], + "source": [ + "loss = []\n", + "accuracy = []\n", + "ROC_AUC = []\n", + "\n", + "for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " clf = LogisticRegression(max_iter=250, penalty = param[\"penalty\"],\n", + " C = param[\"C\"], solver = param['solver'],\n", + " class_weight= param[\"class_weight\"]\n", + " ).fit(train_X[train_index], train_y[train_index])\n", + " y_valid_pred = np.round(clf.predict(train_X[test_index]))\n", + " validation_y = train_y[test_index]\n", + "\n", + " #calculate loss:\n", + " false_positive = 100*(1-np.mean(np.array(validation_y)[y_valid_pred == 1]))\n", + " false_negative = 100*(np.mean(np.array(validation_y)[y_valid_pred == 0]))\n", + " logging.info(\"False positive rate: \" + str(false_positive)+ \"; False negative rate: \" + str(false_negative))\n", + " loss.append(2*(false_negative**2) + false_positive**1.3)\n", + " #calculate accuracy:\n", + " .append(np.mean(y_valid_pred == np.array(validation_y)))\n", + " #calculate ROC-AUC score:\n", + " ROC_AUC.append(roc_auc_score(np.array(validation_y), clf.predict_proba(train_X[test_index])[:, 1]))\n", + " \n", + "print(\"Loss values: %s\" %loss) \n", + "print(\"Accuracies: %s\" %accuracy)\n", + "print(\"ROC-AUC scores: %s\" %ROC_AUC)\n", + "\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_LR.npy\"), np.array(accuracy))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"loss_CV_LR.npy\"), np.array(loss))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_LR.npy\"), np.array(ROC_AUC))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (iv) 3. Training and validating the final model\n", + "Training the model and validating it on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on test set: 0.6304640718562874, ROC-AUC score for test set: 0.620806910231481, MCC: 0.13965916973823772\n" + ] + } + ], + "source": [ + "clf = LogisticRegression(max_iter=250, penalty = param[\"penalty\"],\n", + " C = param[\"C\"], solver = param['solver'],\n", + " class_weight= param[\"class_weight\"]\n", + " ).fit(train_X, train_y)\n", + "y_test_pred = np.round(clf.predict(test_X))\n", + "acc_test = np.mean(y_test_pred == np.array(test_y))\n", + "roc_auc = roc_auc_score(np.array(test_y), clf.predict_proba(test_X)[:, 1])\n", + "mcc = matthews_corrcoef(np.array(test_y), y_test_pred)\n", + "\n", + "print(\"Accuracy on test set: %s, ROC-AUC score for test set: %s, MCC: %s\" % (acc_test, roc_auc, mcc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (b) Random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_validation_neg_acc_random_forest(param):\n", + " param['solver'] = 'liblinear'\n", + " param['class_weight'] = class_weights\n", + " \n", + " loss = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i] \n", + " \n", + " clf = RandomForestClassifier(n_estimators = param[\"n_estimators\"],\n", + " class_weight= param[\"class_weight\"]\n", + " ).fit(train_X[train_index], train_y[train_index])\n", + " y_valid_pred = np.round(clf.predict(train_X[test_index]))\n", + " validation_y = train_y[test_index]\n", + " \n", + " false_positive = 100*(1-np.mean(np.array(validation_y)[y_valid_pred == 1]))\n", + " false_negative = 100*(np.mean(np.array(validation_y)[y_valid_pred == 0]))\n", + " logging.info(\"False positive rate: \" + str(false_positive)+ \"; False negative rate: \" + str(false_negative))\n", + " loss.append(2*(false_negative**2) + false_positive**1.3)\n", + " return(np.mean(loss))\n", + "\n", + "#Defining search space for hyperparameter optimization\n", + "space_random_forest = {'n_estimators': hp.choice('n_estimators', [100, 200, 300])}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Performing a random grid search:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "'''trials = Trials()\n", + "\n", + "for i in range(1,10):\n", + " best = fmin(fn = cross_validation_neg_acc_random_forest, space = space_random_forest,\n", + " algo = rand.suggest, max_evals = i, trials = trials)\n", + " print(i)\n", + " print(trials.best_trial[\"result\"][\"loss\"])\n", + " print(trials.argmin)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Best set of hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "param = {\"n_estimators\" : 100}\n", + "param['class_weight'] = class_weights" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss values: [320.3317972277872, 297.5914956403973, 312.6594901020858, 278.87419374147123, 302.0886608020148]\n", + "Accuracies: [0.8865885771361118, 0.8891655140617796, 0.8870867677036249, 0.8945084145261293, 0.8898128898128899]\n", + "ROC-AUC scores: [0.9484840687096031, 0.9475283641094073, 0.9427751776258135, 0.9515115956023957, 0.9481888507479239]\n" + ] + } + ], + "source": [ + "loss = []\n", + "accuracy = []\n", + "ROC_AUC = []\n", + "\n", + "for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " clf = RandomForestClassifier(n_estimators = param[\"n_estimators\"],\n", + " class_weight= param[\"class_weight\"]\n", + " ).fit(train_X[train_index], train_y[train_index])\n", + " y_valid_pred = np.round(clf.predict(train_X[test_index]))\n", + " validation_y = train_y[test_index]\n", + "\n", + " #calculate loss:\n", + " false_positive = 100*(1-np.mean(np.array(validation_y)[y_valid_pred == 1]))\n", + " false_negative = 100*(np.mean(np.array(validation_y)[y_valid_pred == 0]))\n", + " logging.info(\"False positive rate: \" + str(false_positive)+ \"; False negative rate: \" + str(false_negative))\n", + " loss.append(2*(false_negative**2) + false_positive**1.3)\n", + " #calculate accuracy:\n", + " accuracy.append(np.mean(y_valid_pred == np.array(validation_y)))\n", + " #calculate ROC-AUC score:\n", + " ROC_AUC.append(roc_auc_score(np.array(validation_y), clf.predict_proba(train_X[test_index])[:, 1]))\n", + " \n", + "print(\"Loss values: %s\" %loss) \n", + "print(\"Accuracies: %s\" %accuracy)\n", + "print(\"ROC-AUC scores: %s\" %ROC_AUC)\n", + "\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_random_forest.npy\"), np.array(accuracy))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"loss_CV_random_forest.npy\"), np.array(loss))\n", + "np.save(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_random_forest.npy\"), np.array(ROC_AUC))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (iv) 3. Training and validating the final model\n", + "Training the model and validating it on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on test set: 0.8771706586826348, ROC-AUC score for test set: 0.9445799929837202, MCC: 0.6703608017079266\n" + ] + } + ], + "source": [ + "clf = RandomForestClassifier(n_estimators = param[\"n_estimators\"],\n", + " class_weight= param[\"class_weight\"]\n", + " ).fit(train_X, train_y)\n", + "y_test_pred = np.round(clf.predict(test_X))\n", + "acc_test = np.mean(y_test_pred == np.array(test_y))\n", + "roc_auc = roc_auc_score(np.array(test_y), clf.predict_proba(test_X)[:, 1])\n", + "mcc = matthews_corrcoef(np.array(test_y), y_test_pred)\n", + "\n", + "print(\"Accuracy on test set: %s, ROC-AUC score for test set: %s, MCC: %s\" % (acc_test, roc_auc, mcc))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks_and_code/3 Plots and figures.ipynb b/notebooks_and_code/3 Plots and figures.ipynb index 2114a41..67be591 100644 --- a/notebooks_and_code/3 Plots and figures.ipynb +++ b/notebooks_and_code/3 Plots and figures.ipynb @@ -5,11 +5,19 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\alexk\\anaconda3\\envs\\ESP\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\alexk\\OneDrive\\Dokumente\\GitHub\\SubFinder\\notebooks_and_code\n" + "C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\n" ] } ], @@ -18,7 +26,7 @@ "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "from sklearn import metrics\n", - "from sklearn.metrics import roc_auc_score\n", + "from sklearn.metrics import roc_auc_score, matthews_corrcoef\n", "import os\n", "from os.path import join\n", "import pandas as pd\n", @@ -38,36 +46,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### ROC-AUC score: Comparison of model with ESM-1b and ESM-1b_ts" + "### ROC-AUC score: Comparison of model with ESM-1b_ts vectors and ECFP vectors of different dimensions (512/1024/2048)" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "y_test_pred_esm1b_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ECFP.npy\"))\n", - "test_y_esm1b_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ECFP.npy\"))\n", - "\n", "y_test_pred_esm1b_ts_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_ECFP.npy\"))\n", "test_y_esm1b_ts_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_ECFP.npy\"))\n", "\n", - "y_test_pred_esm1b_gnn = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_GNN.npy\"))\n", - "test_y_esm1b_gnn = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_GNN.npy\"))\n", + "y_test_pred_esm1b_ts_ecfp_512 = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_ECFP_512.npy\"))\n", + "test_y_esm1b_ts_ecfp_512 = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_ECFP_512.npy\"))\n", "\n", - "y_test_pred_esm1b_ts_gnn = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_GNN.npy\"))\n", - "test_y_esm1b_ts_gnn = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_GNN.npy\"))" + "y_test_pred_esm1b_ts_ecfp_2048 = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_ECFP_2048.npy\"))\n", + "test_y_esm1b_ts_ecfp_2048 = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_ECFP_2048.npy\"))\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -80,31 +86,30 @@ "fig, ax = plt.subplots(figsize= (8,8))\n", "plt.rcParams.update({'font.size': 28})\n", "\n", - "fpr_esm1b_ecfp, tpr_esm1b_ecfp, threshold = metrics.roc_curve(test_y_esm1b_ecfp, y_test_pred_esm1b_ecfp)\n", - "roc_auc_esm1b_ecfp = metrics.auc(fpr_esm1b_ecfp, tpr_esm1b_ecfp)\n", "\n", "fpr_esm1b_ts_ecfp, tpr_esm1b_ts_ecfp, threshold = metrics.roc_curve(test_y_esm1b_ts_ecfp, y_test_pred_esm1b_ts_ecfp)\n", "roc_auc_esm1b_ts_ecfp = metrics.auc(fpr_esm1b_ts_ecfp, tpr_esm1b_ts_ecfp)\n", "\n", "\n", - "fpr_esm1b_gnn, tpr_esm1b_gnn, threshold = metrics.roc_curve(test_y_esm1b_gnn, y_test_pred_esm1b_gnn)\n", - "roc_auc_esm1b_gnn = metrics.auc(fpr_esm1b_gnn, tpr_esm1b_gnn)\n", + "fpr_esm1b_ts_ecfp_2048, tpr_esm1b_ts_ecfp_2048, threshold = metrics.roc_curve(test_y_esm1b_ts_ecfp_2048, y_test_pred_esm1b_ts_ecfp_2048)\n", + "roc_auc_esm1b_ts_ecfp_2048 = metrics.auc(fpr_esm1b_ts_ecfp_2048, tpr_esm1b_ts_ecfp_2048)\n", "\n", - "fpr_esm1b_ts_gnn, tpr_esm1b_ts_gnn, threshold = metrics.roc_curve(test_y_esm1b_ts_gnn, y_test_pred_esm1b_ts_gnn)\n", - "roc_auc_esm1b_ts_gnn = metrics.auc(fpr_esm1b_ts_gnn, tpr_esm1b_ts_gnn)\n", + "fpr_esm1b_ts_ecfp_512, tpr_esm1b_ts_ecfp_512, threshold = metrics.roc_curve(test_y_esm1b_ts_ecfp_512, y_test_pred_esm1b_ts_ecfp_512)\n", + "roc_auc_esm1b_ts_ecfp_512 = metrics.auc(fpr_esm1b_ts_ecfp_512, tpr_esm1b_ts_ecfp_512)\n", "\n", "#plt.title('Receiver Operating Characteristic')\n", - "plt.plot(fpr_esm1b_ecfp, tpr_esm1b_ecfp, 'magenta', label = 'AUC (ESM-1b/ECFP) = %0.2f' % roc_auc_esm1b_ecfp, linewidth=2.0)\n", - "plt.plot(fpr_esm1b_ts_ecfp, tpr_esm1b_ts_ecfp, 'r', label = 'AUC (ESM-$1b_{ts}$/ECFP) = %0.2f' % roc_auc_esm1b_ts_ecfp, linewidth=2.0)\n", "\n", - "plt.plot(fpr_esm1b_ts_gnn, tpr_esm1b_ts_gnn, 'blue', label = 'AUC (ESM-$1b_{ts}$/GNN) = %0.2f' % roc_auc_esm1b_ts_gnn, linewidth=2.0)\n", - "plt.plot(fpr_esm1b_gnn, tpr_esm1b_gnn, 'black', label = 'AUC (ESM-1b/GNN) = %0.2f' % roc_auc_esm1b_gnn, linewidth=2.0)\n", + "plt.plot(fpr_esm1b_ts_ecfp_512, tpr_esm1b_ts_ecfp_512, 'black', label = 'AUC (512-dimensional ECFPs) \\n = %0.4f' % roc_auc_esm1b_ts_ecfp_512, linewidth=2.0)\n", + "plt.plot(fpr_esm1b_ts_ecfp_2048, tpr_esm1b_ts_ecfp_2048, 'blue', label = 'AUC (2048-dimensional ECFPs) \\n = %0.4f' % roc_auc_esm1b_ts_ecfp_2048, linewidth=2.0)\n", + "plt.plot(fpr_esm1b_ts_ecfp, tpr_esm1b_ts_ecfp, 'red', label = 'AUC (1024-dimensional ECFPs)\\n = %0.4f' % roc_auc_esm1b_ts_ecfp, linewidth=2.0)\n", + "\n", "\n", "\n", "ax.locator_params(axis=\"y\", nbins=5)\n", "ax.locator_params(axis=\"x\", nbins=5)\n", "\n", - "plt.legend(loc = 'lower right', fontsize =20)\n", + "leg = plt.legend(loc = 'lower right', fontsize =20, frameon=True)\n", + "leg.get_frame().set_linewidth(3.0)\n", "plt.plot([0, 1], [0, 1],'--')\n", "eps = 0.01\n", "plt.xlim([0-eps, 1+eps])\n", @@ -114,6 +119,32 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ROC-AUC score: Comparison of model with ESM-1b and ESM-1b_ts" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_esm1b_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ECFP.npy\"))\n", + "test_y_esm1b_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ECFP.npy\"))\n", + "\n", + "y_test_pred_esm1b_ts_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_ECFP.npy\"))\n", + "test_y_esm1b_ts_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_ECFP.npy\"))\n", + "\n", + "y_test_pred_esm1b_gnn = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_GNN_pretrained.npy\"))\n", + "test_y_esm1b_gnn = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_GNN_pretrained.npy\"))\n", + "\n", + "y_test_pred_esm1b_ts_gnn = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_GNN_pretrained.npy\"))\n", + "test_y_esm1b_ts_gnn = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_GNN_pretrained.npy\"))" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -121,7 +152,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -148,11 +179,11 @@ "roc_auc_esm1b_ts_gnn = metrics.auc(fpr_esm1b_ts_gnn, tpr_esm1b_ts_gnn)\n", "\n", "#plt.title('Receiver Operating Characteristic')\n", - "plt.plot(fpr_esm1b_ecfp, tpr_esm1b_ecfp, 'black', label = 'AUC (ESM-1b/ECFP) = %0.3f' % roc_auc_esm1b_ecfp, linewidth=3.0)\n", - "plt.plot(fpr_esm1b_ts_ecfp, tpr_esm1b_ts_ecfp, 'green', label = 'AUC (ESM-$1b_{ts}$/ECFP) = %0.3f' % roc_auc_esm1b_ts_ecfp, linewidth=3.0)\n", + "plt.plot(fpr_esm1b_ecfp, tpr_esm1b_ecfp, 'magenta', label = 'AUC (ESM-1b + ECFP) = %0.2f' % roc_auc_esm1b_ecfp, linewidth=2.0)\n", + "plt.plot(fpr_esm1b_gnn, tpr_esm1b_gnn, 'black', label = 'AUC (ESM-1b + GNN) = %0.2f' % roc_auc_esm1b_gnn, linewidth=2.0)\n", + "plt.plot(fpr_esm1b_ts_ecfp, tpr_esm1b_ts_ecfp, 'red', label = 'AUC (ESM-$1b_{ts}$ + ECFP) = %0.2f' % roc_auc_esm1b_ts_ecfp, linewidth=2.0)\n", + "plt.plot(fpr_esm1b_ts_gnn, tpr_esm1b_ts_gnn, 'blue', label = 'AUC (ESM-$1b_{ts}$ + GNN) = %0.2f' % roc_auc_esm1b_ts_gnn, linewidth=2.0)\n", "\n", - "#plt.plot(fpr_esm1b_ts_gnn, tpr_esm1b_ts_gnn, 'blue', label = 'AUC (ESM-$1b_{ts}$/GNN) = %0.2f' % roc_auc_esm1b_ts_gnn, linewidth=2.0)\n", - "#plt.plot(fpr_esm1b_gnn, tpr_esm1b_gnn, 'black', label = 'AUC (ESM-1b/GNN) = %0.2f' % roc_auc_esm1b_gnn, linewidth=2.0)\n", "\n", "\n", "ax.locator_params(axis=\"y\", nbins=5)\n", @@ -165,7 +196,7 @@ "plt.ylim([0-eps, 1+eps])\n", "plt.ylabel('True Positive Rate')\n", "plt.xlabel('False Positive Rate')\n", - "plt.show()#" + "plt.show()" ] }, { @@ -175,7 +206,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -202,11 +233,11 @@ "roc_auc_esm1b_ts_gnn = metrics.auc(fpr_esm1b_ts_gnn, tpr_esm1b_ts_gnn)\n", "\n", "#plt.title('Receiver Operating Characteristic')\n", - "plt.plot(fpr_esm1b_ecfp, tpr_esm1b_ecfp, 'black', label = 'AUC (ESM-1b/ECFP) = %0.3f' % roc_auc_esm1b_ecfp, linewidth=3.0)\n", - "plt.plot(fpr_esm1b_ts_ecfp, tpr_esm1b_ts_ecfp, 'green', label = 'AUC (ESM-$1b_{ts}$/ECFP) = %0.3f' % roc_auc_esm1b_ts_ecfp, linewidth=3.0)\n", + "plt.plot(fpr_esm1b_ecfp, tpr_esm1b_ecfp, 'magenta', label = 'AUC (ESM-1b/ECFP) = %0.3f' % roc_auc_esm1b_ecfp, linewidth=3.0)\n", + "plt.plot(fpr_esm1b_ts_ecfp, tpr_esm1b_ts_ecfp, 'red', label = 'AUC (ESM-$1b_{ts}$/ECFP) = %0.3f' % roc_auc_esm1b_ts_ecfp, linewidth=3.0)\n", "\n", - "#plt.plot(fpr_esm1b_ts_gnn, tpr_esm1b_ts_gnn, 'blue', label = 'AUC (ESM-$1b_{ts}$/GNN) = %0.2f' % roc_auc_esm1b_ts_gnn, linewidth=2.0)\n", - "#plt.plot(fpr_esm1b_gnn, tpr_esm1b_gnn, 'black', label = 'AUC (ESM-1b/GNN) = %0.2f' % roc_auc_esm1b_gnn, linewidth=2.0)\n", + "plt.plot(fpr_esm1b_ts_gnn, tpr_esm1b_ts_gnn, 'blue', label = 'AUC (ESM-$1b_{ts}$/GNN) = %0.3f' % roc_auc_esm1b_ts_gnn, linewidth=3.0)\n", + "plt.plot(fpr_esm1b_gnn, tpr_esm1b_gnn, 'black', label = 'AUC (ESM-1b/GNN) = %0.3f' % roc_auc_esm1b_gnn, linewidth=3.0)\n", "\n", "\n", "\n", @@ -226,7 +257,7 @@ "\n", "\n", "\n", - "plt.show()#" + "plt.show()" ] }, { @@ -238,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -247,7 +278,7 @@ "df_test = df_test.loc[df_test[\"type\"] != \"engqvist\"]\n", "df_test = df_test.loc[df_test[\"GNN rep\"] != \"\"]\n", "df_test.reset_index(inplace = True, drop = True)\n", - "df_test[\"pred\"] = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_ECFP.npy\"))\n", + "df_test[\"pred\"] = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_GNN_pretrained.npy\"))\n", "\n", "\n", "df_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"df_train_with_ESM1b_ts_GNN.pkl\"))\n", @@ -258,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -340,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -363,7 +394,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -422,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -445,7 +476,7 @@ }, { "data": { - "image/png": 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jR7Vs2TINHz5c06dP17p169TQ0GB9+mGapq688kq99NJL+uY3v2nru+vq6rRz505rnJCQoMzMzPPOd7lcuvfee62xaZraunWrrTWdz5tvvqmGhgZJUnp6ui677LKAvBcAAAAAACfpsc9Aqqur9cYbb2jdunXavXu3pLN3TiQlJWnevHm6//77NW7cuB6pw7eZZnp6ukJDQztck5aWpqSkJJWWlkqSDh48qKamJvXv379HapSkf/zjH/r73/8uSYqJidGdd96pkydP9tj7AAAAAABwKlvDitbWVm3evFnr1q3T1q1b1dLSIsk7pAgPD9cdd9yhBQsW6JZbbjnrUwy7HThwwGs8ceLETq2bOHGitaOipaVFhw4d0lVXXWV7fZLU1NSk7Oxsa3z33XdrwIABhBUAAAAAgD7JlrBi9+7dWrdunTZu3KgTJ05I8g4oDMPQ9ddfr/vvv1933323oqOj7Xhtp+Tn51vXMTExSkhI6NS6UaNGeY2PHTvWY2HFH//4R1VVVUmSxo4dq2uuuaZH3gMAAAAAwMWgy2HF8ePHtX79eq1fv15Hjx6VdPZnHiNHjtT8+fO1YMGCXum/UFNT43X0aEpKSqfX+s4tKSmxra72CgsL9eGHH0o63S/jvvvu65H3AAAAAABwsehSWDF9+nT95S9/sZpjthcbG6tvfOMbWrBggaZOnWpLkV11pufEGYMGDer02piYGIWFhVn9LsrKymytTZJOnTql9evXq62tTZI0e/bsTu/8AAAAAAAgWHUprPjzn//sNQ4NDdXMmTO1YMEC3X777T3aiNIfNTU1XuOBAwd2eq1hGIqLi1NlZaWk0w1D7fbee+/p+PHjkqTk5GTdcssttr8DAAAAAICLTZc/AzEMQ6ZpKjIyUllZWUpKStKuXbu0a9cuO+uzvPTSS36vaWpq8hr7G6KEh4db121tbWppaZHL5fK7jnMpKyuzGngahqH77rtPYWE9djgLAAAAAAAXjW7/dnzy5Em9+eabdtTSoa6EFR6Px2vsb9DgGx40NTXZElaYpqnf/e531mkpX/va1zRmzJhuPxcAAAAAgGDQrXNDDcOwq47z8u2J4Y8z/SbO8Hfngu/85ubmLtfS3s6dO5WXlydJioqK0te//nVbngsAAAAAQDDo8s6K7oQIgeIbNviGFxfiO79fv37drqm2tlZ/+MMfrPFdd92lqKiobj8XAAAAAIBg0aWw4osvvrC7jh7RvueEJOuzi87yDSvsaBy6ceNGnTx5UpI0ZswYTZkypdvPBAAAAAAgmHQprBg+fLjddfQI33DBt+HmhbTveRESEtLtfhU5OTnat2+fpNO7PubNm9et511IS0uLWltbrX+OM/8aFhZmW6NQAAAAAADsFtTHT8TFxXmN/Tl+1DRNr6NP/Tn29Hx+//vfW9czZ85UUlJSt5/ZkW3btmnz5s3WeMmSJZKkOXPmKCsrq0ffDQAAAABAVwV1WJGcnOw1rqqq6vTauro6r89A7AgWGhoarOs//elP+tOf/uTX+ry8PC1evNgajx49Wt/73vfOOz8zM1PTp0+Xx+PRkiVL9OKLLyo8PJwjUgEAAAAAjhbUv7XGxsYqIiJCbrdbklRcXNzptUVFRV5ju3dBtLW1dXvdhZqculwur889wsPDFRER0aX3AgAAAAAQKN06uvRikJqaal3X19eroqKiU+sKCgq8xqNHj7a1LgAAAAAAcG5BvbNCkiZMmKDc3FxrvHfvXmVmZl5w3ZlGmNLpHQpjx47tdi0vv/yyX/MrKyu1bNkyazxmzBh9//vf73YdAAAAAAA4WdDvrJgwYYJXj4ZPPvlEp06d6nDNkSNHVFZWZo3HjRtny7GlAAAAAADgwoJ+Z0VMTIymTp2qv/zlL5KkiooKbdu2Tbfeeus557e0tGjjxo3W2DAMzZ49+7zP9939MHjwYL3wwgv2FA8AAAAAQB8U9DsrJGnWrFkKDw+3xu+++662b99+VpPL+vp6vfrqqyopKbHuXX311Ro2bFjAagUAAAAAoK8L+p0VkhQXF6eFCxdq5cqVMk1Tpmlq06ZN+uijj5SWlqbIyEiVl5frwIEDamlpsdYlJydr3rx5vVg5AAAAAAB9T58IKyRp/PjxeuCBB5Sdna3m5mZJUnl5ucrLy885PyUlRYsWLeKoTwAAAAAAAqzPhBWSNHnyZI0YMULvvPOOcnNzz9loMzY2VlOnTtXs2bO9GnMCAAAAAIDA6HO/jSclJWnx4sVqaGhQQUGBqqur5fF4FBMTo/j4eKWmpiokpPOtPOLj47Vq1aoeqbUnnw0AAAAAgFP1ubDijKioKE2YMKG3ywAAAAAAAD76xGkgAAAAAADg4kFYAQAAAAAAHIWwAgAAAAAAOAphBQAAAAAAcBTCCgAAAAAA4CiEFQAAAAAAwFEIKwAAAAAAgKMQVgAAAAAAAEchrAAAAAAAAI5CWAEAAAAAAByFsAIAAAAAADgKYQUAAAAAAHAUwgoAAAAAAOAohBUAAAAAAMBRCCsAAAAAAICjEFYAAAAAAABHIawAAAAAAACOQlgBAAAAAAAchbACAAAAAAA4CmEFAAAAAABwFMIKAAAAAADgKIQVAAAAAADAUQgrAAAAAACAoxBWAAAAAAAARyGsAAAAAAAAjkJYAQAAAAAAHIWwAgAAAAAAOAphBQAAAAAAcBTCCgAAAAAA4CiEFQAAAAAAwFEIKwAAAAAAgKMQVgAAAAAAAEchrAAAAAAAAI5CWAEAAAAAAByFsAIAAAAAADgKYQUAAAAAAHAUwgoAAAAAAOAohBUAAAAAAMBRCCsAAAAAAICjEFYAAAAAAABHIawAAAAAAACOQlgBAAAAAAAcJay3CwCCmWE819slBJxpPtvbJQAAAAC4yLGzAgAAAAAAOAphBQAAAAAAcBTCCgAAAAAA4CiEFQAAAAAAwFEIKwAAAAAAgKMQVgAAAAAAAEchrAAAAAAAAI5CWAEAAAAAAByFsAIAAAAAADgKYQUAAAAAAHAUwgoAAAAAAOAohBUAAAAAAMBRCCsAAAAAAICjEFYAAAAAAABHIawAAAAAAACOQlgBAAAAAAAchbACAAAAAAA4SlhvF9BbGhsblZ+fr5qaGrndbsXGxiohIUEjR45USEjgMhy3260vv/xSpaWlamxsVGtrqyIjIxUXF6eRI0cqOjo6YLUAAAAAAOAEfS6sKCsr09tvv62DBw+qtbX1rJ/HxsYqPT1ds2bNUlhYz/z1FBYW6rPPPtPhw4dVVFQk0zTPO3fEiBHKyMjQNddcI8MweqQeAAAAAACcpE+FFbt27dKGDRvU1NR03jm1tbXavHmzcnJytGjRIsXHx9taw8svv6zDhw93en5hYaFef/117dy5U9/61rcUGxtraz0AAAAAADhNn+lZkZubq7Vr13oFFYmJiZo2bZoyMzM1adIkuVwu62fFxcVasWKF3G63rXXU19efdW/QoEG6+uqrlZGRoVtvvVVTp05VYmKi15wjR45o+fLlamhosLUeAAAAAACcpk/srKitrdWaNWuszy0Mw9DcuXOVkZHh1Z+ivr5eq1evVl5eniSppKRE2dnZWrhwoe01xcTEaMqUKbr++ut1ySWXnPVz0zS1b98+/e53v9PJkye96nn00UdtrwcAAAAAAKfoEzsrtmzZIo/HY42zsrI0Y8aMsxppRkdH64knnlBycrJ1b8+ePSoqKrKtlujoaN1111164YUX9PWvf/2cQYV0OlCZNGmSvvvd76pfv37W/X379qmwsNC2egAAAAAAcJqgDyvq6uq0c+dOa5yQkKDMzMzzzne5XLr33nutsWma2rp1q231PP7445oxY4bXJycdGTZsmDIyMrzu7d2717Z6AAAAAABwmqAPK/bv3+916kd6erpCQ0M7XJOWlqakpCRrfPDgwQ6bcvrjQu8+l2uuucZrzM4KAAAAAEAwC/qw4sCBA17jiRMndmpd+3ktLS06dOiQrXX5w7fZ5rmadAIAAAAAECyCPqzIz8+3rmNiYpSQkNCpdaNGjfIaHzt2zNa6/NG+34bUtd0ZAAAAAABcLII6rKipqfE6ejQlJaXTa33nlpSU2FaXv44fP+41jouL66VKAAAAAADoeUEdVpSWlnqNBw0a1Om1MTExCgv798muZWVlttXlr08//dRrnJaW1kuVAAAAAADQ84I6rKipqfEaDxw4sNNrDcPw2sFQXV1tW13+KCsr0+7du61xSEiIrrrqql6pBQAAAACAQAjqsML3BI/+/fv7tT48PNy6bmtrU0tLiy11dVZbW5vWr1/vdZrJlClTFB8fH9A6AAAAAAAIpKAOK3wbU7pcLr/Wt/8MRDo7/OhpW7Zs8WrsGR0drTvvvDOgNQAAAAAAEGhBHVa035EgnR0+XIjv/Obm5m7X1Fn79+/Xli1brLFhGJo/f76io6MDVgMAAAAAAL0hqMMK37DBN7y4EN/5/fr163ZNnZGfn681a9bINE3r3pw5czRhwoSAvB8AAAAAgN4U1GFF+54TkvzuOeEbVvjb86Ir/vWvf2nlypVetd5www2aM2dOj78bAAAAAAAnCOqwwjdc8LfnRPueFyEhIX73vPBXRUWFXn75ZZ08edK6d/XVV+vee+/t0fcCAAAAAOAk/jVxuMi0P3pU8u/4UdM0vY4+9efY066orq7W8uXLVVdXZ937yle+om9961sKCelaptTS0qLW1lYrdDnzr2FhYT0evAAAAAAA0FVBHVYkJyd7jauqqjq9tq6uzuszkKSkJNvq8tXQ0KBXXnnFq77U1FQtWrRIoaGhXX7utm3btHnzZmu8ZMkSSaf7X2RlZXW9YAAAAAAAelBQhxWxsbGKiIiQ2+2WJBUXF3d6bVFRkde4p8IKt9utV155RSUlJda9YcOG6dvf/na3G3pmZmZq+vTp8ng8WrJkiV588UWFh4f7fSoKAAAAAACBFNQ9K6TTOxTOqK+vV0VFRafWFRQUeI1Hjx5ta13S6aNQV6xY4RWMJCcn6zvf+Y4iIiK6/XyXy6WIiAir0Wh4eLgiIiL4BAQAAAAA4GhBH1b4Hve5d+/eTq3bt2+fde1yuTR27Fhb6zp16pRWrVql/Px8615CQoKefPJJRUVF2fouAAAAAAAuJn0irGj/2cMnn3yiU6dOdbjmyJEjKisrs8bjxo2z9djS/8/efcdHVeb7A//MpPcE0oEUOoEAoSQBAgGUjgoionipKuhaWF2xLNeCq6xtXXVVvCIXRKOgLEWpiwQILaRQEggtgTTSe520Ob8/8su5cyYzk5m0mSSf9+uVF/MMzznznTNnzpzzPU9RKpXYtm0brl69Kj7n4uKCP//5z80GBSUiIiIiIiLqabr94AWOjo4ICwvDyZMnATROD3rkyBHMmzdPY/26ujrs3LlTLMtkMsydO1fr+gsKCrBhwwax3Lt3b2zatElnTD///DNiY2PFsoODA/785z/D1dVVn7dERERERERE1K11+5YVADBnzhxx3AYA+P3333Hs2DEolUpJvfLycnzxxReSwS7HjRsHHx+fdotl7969iIqKEsu2trZYt25dh842QkRERERERNSVdPuWFQDg7OyMp556Cl999RUEQYAgCNi9ezeioqIwdOhQ2NnZIS8vDwkJCairqxOX8/LywhNPPNGusRw5ckRSrq6ubrElhiabN29ur5CIiIiIiIiITEqPSFYAQGBgIFauXImIiAjU1tYCAPLy8pCXl6exfr9+/fDMM8+0y6wcujQlT4iIiIiIiIioUY9JVgBAaGgo/Pz8sHfvXiQmJmocaNPJyQlhYWGYO3euZGBOIiIiIiIiIuocPe5q3NPTE88++ywqKiqQkpKC4uJiKBQKODo6wtXVFQMHDoRcrv9QHq6urvif//kfvesbUpeIiIiIiIioJ+pxyYom9vb2GDVqlLHDICIiIiIiIiI1PWI2ECIiIiIiIiLqOpisICIiIiIiIiKTwmQFEREREREREZkUJiuIiIiIiIiIyKQwWUFEREREREREJoXJCiIiIiIiIiIyKUxWEBEREREREZFJYbKCiIiIiIiIiEwKkxVEREREREREZFKYrCAiIiIiIiIik8JkBRERERERERGZFCYriIiIiIiIiMikMFlBRERERERERCaFyQoiIiIiIiIiMilMVhARERERERGRSWGygoiIiIiIiIhMCpMVRERERERERGRSmKwgIiIiIiIiIpPCZAURERERERERmRQmK4iIiIiIiIjIpDBZQUREREREREQmhckKIiIiIiIiIjIpTFYQERERERERkUlhsoKIiIiIiIiITAqTFURERERERERkUpisICIiIiIiIiKTwmQFEREREREREZkUJiuIiIiIiIiIyKQwWUFEREREREREJoXJCiIiIiIiIiIyKUxWEBEREREREZFJYbKCiIiIiIiIiEwKkxVEREREREREZFKYrCAiIiIiIiIik8JkBRERERERERGZFCYriIiIiIiIiMikMFlBRERERERERCaFyQoiIiIiIiIiMilMVhARERERERGRSWGygoiIiIiIiIhMCpMVRERERERERGRSmKwgIiIiIiIiIpPCZAURERERERERmRQmK4iIiIiIiIjIpDBZQUREREREREQmhckKIiIiIiIiIjIpTFYQERERERERkUlhsoKIiIiIiIiITAqTFURERERERERkUpisICIiIiIiIiKTwmQFEREREREREZkUJiuIiIiIiIiIyKQwWUFEREREREREJoXJCiIiIiIiIiIyKUxWEBEREREREZFJYbKCiIiIiIiIiEwKkxVEREREREREZFKYrCAiIiIiIiIik8JkBRERERERERGZFCYriIiIiIiIiMikmBs7AGOprKxEcnIySkpKUF1dDScnJ7i5uaF///6Qyzs/h6NQKJCcnIzi4mJUVlbCwcEBvXr1wqBBg2Bu3mM/JiIiIiIiIuqBetxVcG5uLvbs2YOrV6+ivr6+2f87OTlh8uTJmDNnTqckCUpLS7F3715cvHgRNTU1zf7f1tYWoaGhePDBB2FjY9Ph8RAREREREREZW49KVkRHR+Onn37SmBRoU7BrhQAAIABJREFUUlpaigMHDuDKlSt45pln4Orq2mHxJCUlYevWraioqNBap6qqCpGRkUhISMAzzzyDfv36dVg8RERERERERKagx4xZkZiYiO3bt0sSFe7u7pgyZQpmz56NsWPHwsLCQvy/jIwMfPnll6iuru6QeNLT07F582ZJosLZ2RmTJk3CnDlzEBoaCltbW/H/CgoK8MUXX6C4uLhD4iEiIiIiIiIyFT2iZUVpaSm+++47CIIAAJDJZFi0aBHuu+8+yfgU5eXl+Pbbb3Hr1i0AQHZ2NiIiIvDUU0+1azy1tbX4+uuvUVtbKz43Y8YMLFiwQNL1RKFQ4IcffkBcXBwAoKysDN9++y1ee+21do2HiIiIiIiIyJT0iJYVBw8ehEKhEMsPPPAAZsyY0WwgTQcHB7z44ovw8vISn4uLi0N6enq7xnPixAlJC4lJkybhkUceaTZGhrW1NZ588kkMHTpUfO7OnTu4fPlyu8ZDREREREREZEq6fbKirKwMZ8+eFctubm6YPXu21voWFhZ47LHHxLIgCDh06FC7xdPQ0ID//Oc/YtnGxgaLFi3SWl8ul+OJJ56ATCYTnzt48GC7xUNERERERERkarp9suLy5cuSWT8mT54MMzMzncsMHToUnp6eYvnq1as6B+U0xM2bNyXjVAQHB8POzk7nMu7u7hg2bJhYTk9PR35+frvEQ0RERERERGRqun2yIiEhQVIeM2aMXsup1qurq0NSUlK7xHPlypVWxTN27Fid6yEiIiIiIiLqLrp9siI5OVl87OjoCDc3N72WGzBggKR8+/btdo9HLpfDz8/PqPEQERERERERmZpunawoKSmRTD3ar18/vZdVr5udnd3meJRKJXJzc8Wyu7s7rK2t9VrW09NTMrVqe8RDREREREREZIq6dbIiJydHUu7Vq5feyzo6Okpm51BNMrRWYWEh6urqWhWPTCaDi4uLWM7Pz0dDQ0ObYyIiIiIiIiIyNd06WVFSUiIpq17st0Qmk8HZ2Vksq041aox41OsrlUqUlZW1OSYiIiIiIiIiU9OtkxXqM3hYWVkZtLxqFw2lUilpFWHseDStj4iIiIiIiKg76NbJCoVCISmrjvmgD9VuIEDbkwPqy7c1HvX3R0RERERERNQddOtkRX19vaSsfrHfEvX6tbW1bYpHvWWGofGoJzfa2tKDiIiIiIiIyBR162SFejJAPXnREvX6lpaWbYpHPdlgaDzqyQlDW2YQERERERERdQXdOlmhPsaDoS0R1JMJho4xoU59+bbGo++0p0RERERERERdSbdOVqgnBwwdc0J1TAi5XN7mlgztGY+m9RERERERERF1B4YNmtDFqE49Chg2/aggCJKpRg2dZrS941GvL5fL4ejoqLN+XV0d6uvrUV1dDaBx6lSFQgEzMzN2IekkFhZKY4fQ6Zr2t9boadurLdsK4PYyRE/bVgC3l6G4vQzD7aU/HusNw33LMNxehuH2Mkxbj19NrK2tIZPJDF5OJgiC0C4RmKDS0lK8+uqrYjkgIADr1q1r1bLDhw/Hiy++2KZ4lEolXnzxRbH7h4eHB9599129lhUEAS+88IK4rKenJzZu3Khzmd9//x0HDhxoU8xERERERERErfXZZ5/BxsbG4OW6dcsKJycn2NjYiBmhjIwMvZdNT0+XlD09Pdscj1wuh4eHBzIzMwEA+fn5UCgUeo09kZ2dLRnjQp94Zs+ejfvvvx9KpRJlZWWwsrKCTCZjywoiIiIiIiLqFK0da7FbJysAYODAgUhMTAQAlJeXIz8/H25ubi0ul5KSIikPGjSo3eJpSlYolUrcvXsXw4YNa3G5O3fuGByPhYWFmJSws7NrRbREREREREREna9bD7AJAKNGjZKU4+Pj9Vru4sWL4mMLCwsEBAR0SDyqr6OLetzq6yEiIiIiIiLqLnpEssLc/P8akJw5cwYNDQ06l7lx4wZyc3PF8ogRI9pt5o0hQ4bAwcFBLMfGxqKqqkrnMnl5ebh+/bpY9vHx0at1CBEREREREVFX1O2TFY6OjggLCxPL+fn5OHLkiNb6dXV12Llzp1iWyWSYO3eu1voFBQVYu3at+PfXv/5VZzxmZmaYOXOmWK6ursbu3bu11lcqlYiIiIDqOKjz5s3T+RpEREREREREXVm3T1YAwJw5cySDevz+++84duwYlErp9DPl5eX44osvkJ2dLT43btw4+Pj4tGs8U6dOlUyFevbsWezevRv19fWSegqFAlu3bsWNGzfE5/r374/Ro0e3azxEREREREREpqRbT12qKjExEV999ZWkhYK7uzuGDh0KOzs75OXlISEhQTLjhpeXF1577TWd06wUFBRgw4YNYrl3797YtGlTi/Gkp6fj448/Rm1trfics7MzRowYAQcHBxQXFyMhIUHSRcTR0RF//etfJYkOIiIiIiIiou6m288G0iQwMBArV65ERESEmCDIy8tDXl6exvr9+vXDM88806r5YPXh4+ODZ555Blu3bkVlZSUAoKSkBGfOnNFY39XVFWvXrmWigoiIiIiIiLq9HpOsAIDQ0FD4+flh7969SExM1DjQppOTE8LCwjB37lzJwJwdYfjw4Xj77bexZ88eXLx4UdLKoomtrS1CQ0Px4IMPdljihIiIiIiIiMiU9JhuIOoqKiqQkpKC4uJiKBQKODo6wtXVFQMHDoRc3vlDeSgUCty+fRvFxcWorKyEg4MDevfujYEDB8LCwqLT4yEiIiIiIiIylh6brCAiIiIiIiIi09QjZgMhIiIiIiIioq6DyQoiIiIiIiIiMilMVhARERERERGRSWGygoiIiIiIiIhMCpMVRERERERERGRSmKwgIiIiIiIiIpPCZAURERERERERmRQmK4iIiIiIiIjIpJgbOwAiTYqKihAdHY2EhAQUFRWhsrISDg4OcHV1RVBQEIKDg+Hg4GDsMI1OoVAgIyMDaWlp4r+5ublQKpUAgN69e2PTpk1GjtI01NXVISUlBbdu3UJ6ejqys7NRXl6O+vp62NjYwMXFBf7+/hg9ejQCAgIgk8mMHbLRNDQ0IDMzE3fu3MG9e/eQnZ2N4uJiVFRUoK6uDpaWlrCzs0Pfvn0xePBgBAcHw9HR0dhhm7StW7ciJiZG8tz7778PV1dXI0VkPAUFBdiwYYPBy3l4eODdd9/tgIi6FqVSiRs3buDSpUu4c+cOysrKUFVVBVtbWzg5OcHd3R2DBw/G4MGD4e3tbexwO9XatWvbtHxP/s2sq6vD5cuXkZCQgLS0NJSVlaGmpgZWVlZwdHSEj48PRowYgTFjxsDS0tLY4RpVbW0toqOjce3aNWRkZKC8vBwymQxOTk7o06cPxo4di9GjR8PCwsLYoba7zjjv5DVA2ymVSuTk5CA9PR3p6elIS0tDZmYmFAqFWOfll1/GkCFDWv0adXV1uHTpEuLi4pCVlYWSkhJYWlrC2dkZQ4YMQUhICPz8/Nr8XpisIJMTGRmJPXv2oK6uTvJ8cXExiouLcfv2bRw8eBBLly7FuHHjjBSl8b311lvIy8uDIAjGDsWklZWV4ZdffkFiYqLkIK2qoqICFRUVyMjIQFRUFLy9vbF8+XL4+/t3crSm4cCBAzh06JDW/1coFFAoFCgsLMSVK1ewd+9ezJgxA/Pnz4e5OX9W1F25cqVZooKoNVJSUvDTTz8hMzOz2f+VlZWhrKwMGRkZiI+PBwB8/fXXMDMz6+wwuyw7Oztjh2AUV69eRUREBIqKipr9X3V1Naqrq5Gbm4vY2Fjs3bsXS5cuxahRo4wQqfHFx8fj559/Rnl5ebP/y8vLQ15eHi5dugQvLy+sWLGiW51HdMZ5J68B2u63337DsWPHUFtb22GvkZGRgf/93/9FVlaW5Pm6ujpUVlbi3r17iIyMxOTJk7F48WJYWVm1+rV4VkkmZf/+/c0uktzd3eHs7IySkhLk5eUBACorK7FlyxbU1NRg0qRJxgjV6HJzc40dQpdQVFSE2NjYZs87OTnBxcUFVlZWKCsrQ05OjvgDnJWVhY8++ghPP/00xowZ09khG536iYi5uTlcXV1hb28Pc3NzVFdXIycnBzU1NQCA+vp6HD58GOnp6Xj22We75d2k1qqsrERERISxwzBpgwYN0muf6dWrVydEY7qioqLw008/Sb6flpaWcHd3h52dHerq6lBQUICysjIjRmlcAQEBBtUvLCyU/JaGhIS0d0gmLyYmBtu2bRPvjAOAhYUFvL29YWNjg6qqKmRnZ4sXjyUlJdi8eTOWL1+OiRMnGitsozhy5Aj27t0rec7Ozg6enp6Qy+XIzc0Vv3/Z2dn45JNPsG7dOgwePNgY4ba7jj7v5DVA+ygqKurwRMUnn3wiuQHY9D2oq6uTHC9Onz6N/Px8vPjii61OnDNZQSbj4sWLkoOUl5cXVq1aBV9fX/G5tLQ0bNu2DdnZ2QCAH3/8Ed7e3t0qc20oKysr9O3bF/369YOvry/i4uJw7do1Y4dlkvz9/TFhwgQMHz68WfP70tJSHDp0CKdOnYIgCFAqldi6dSu8vb3h6elppIiNw8LCAgEBARg5ciQGDhyIPn36QC6XDnGkVCqRmJiIPXv2ICcnBwBw7do1HDx4EAsWLDBG2Cbpl19+QWlpKYDGC6mkpCQjR2R6Vq5c2SO7wxji9OnTkkSFp6cnHnroIQQGBjZL9BQXFyMhIQFnz57tcd3Z1q1bZ1D9r7/+WrwAMzc3R2hoaEeEZbKKiorw448/iokKCwsLPPzwwwgLC5N09aitrUVUVBT27duHuro6CIKAn376CYMGDYKbm5uxwu9UCQkJ2Ldvn1i2t7fH448/jjFjxoi/j4IgICkpCRERESgsLER9fT02b96MN998s1slWzvivJPXAO1PJpPB3d0dvr6+8PHxgVKpxJ49e9q0zurqanz55ZdiosLc3ByLFy9GWFiY2LK2oqICv/32G06dOgUAuHHjBnbv3o0lS5a06jWZrCCT0NDQgH//+99i2cXFBevXr2/WJNPX1xfr16/Hu+++i5KSEiiVSuzevRvr16/v7JCNbvXq1fDx8YGHh4fkQvLWrVtGjMr0yOVyjBo1CvPnz4ePj4/Wek5OTnj88cfh4eGBXbt2AWhsMbB///4294PuaubNm9dinabtOmjQIHzwwQfiCX9kZCTmzp3b4/s0A0BiYiKio6MBAIGBgRgzZgyTFWSwnJwc7Nq1S0xUBAYGYu3atVpbo7i4uCA8PBzh4eGdGWaXU1paisTERLE8atQo2NvbGzGizhcVFSW2kAMazys0tSa0tLTE/fffD2dnZ2zZsgVAY3Pv06dP4+GHH+60eI1FqVTi119/Fb+DVlZW+Mtf/tJsTBiZTIbhw4fj1Vdfxfvvvy+OJ7Nv3z6sXr3aGKG3q4467+Q1QPuaOHEiwsLC0K9fP0n3i5s3b7Z53UePHkVJSYlYXrVqVbPuOPb29li6dCkAiAmLkydPYurUqfDw8DD4NTkbCJmEmJgYFBQUiOXFixdr7TtqZ2eHxYsXi+Xk5OQeeYEeEhICLy+vZne8ScrHxwd/+tOfdCYqVE2fPl0yIFBiYmKHNqfr6mxtbTFnzhyxXFNTg7t37xoxItNQXV2NH3/8EQBgbW0t/nATGWrHjh1ik1oPDw+diQrS3/nz5yVdH8LCwowYjXHcvn1bfOzl5dVit8dx48bBy8tLLCcnJ3dYbKbkxo0bYhcEAJg5c6bOwWudnZ2xcOFCsRwTE9Mtuu521Hlnd70GKCsrw969e5uN69DRBg8ejIEDB7ZpnAhNampqEBkZKZZHjBihc9yQRYsWiQOhKpVKHDlypFWvy6scMglNg4EBjQf50aNH66wfFBQEZ2dnjcsTtZXqwGFN/cBJO9VmmgDEbg892a+//irefVi4cGG3agJMnefOnTtISUkRy4888ggTFe3k3Llz4uNevXph6NChRozGOFTHN+nbt69ey/Tp00d8XFFR0e4xmaLr169Lyvp0Fxo3bpzYLF4QBJ6n6tBdrwFqa2tx5MgRbNy4Ee+99x6OHTsmaZXQ1Vy9elXSEmvatGk661tZWWHChAli+fLly2hoaDD4dZmsIKOrra2V/BAMHz68xUFYzMzMJANpJSQkdFh81POoZ/S1zSJCjdR/fGxsbIwUiWm4du0azp49CwAYOHAgm+NTq0VFRYmPe/XqhREjRhgxmu4jOTlZcqd74sSJPbKVorW1tfhYffYFbVTr2dratntMpkj1hoW1tbVeY+w0DX7b5NKlSx0SW1fXU64BMjIysHv3brz++uv47LPPcP78+S53bqm6nS0sLPRK8AYGBoqPq6qqWtUaq+cdmcnkZGdno76+XiwPGDBAr+VU6zXNw0zUHgoLCyVlzuetm2o/SLlc3i7zandVCoUCP/zwA4DGgaeWLVvW4wY5pPajOmhdYGBgj7yg7ghNyUSgcZyBnjarRZP+/fuLj1NSUiTnYprU1dXhzp07YnnQoEEdFpspqa6uFh+rJnhaopq4z8rKatVd5e6uO18D2NnZITg4WNIdQxAEXL9+Hdu3b8crr7yCLVu2ICEhoUvsG+np6eJjX19fvaaq9/f3lySfVNehLw6wSUbXNKpvE9VMtC7q9bKzszFw4MB2i4t6JkEQcPHiRbHs5OTEmQp0yMnJweHDh8XyxIkTe3RyZ/fu3SguLgbQOFBpT5tJpjX27NmD7OxsFBcXo66uDra2tnBxccGAAQMQFBTUbab9M5T6NKRNF5ZFRUU4e/YsEhISUFhYiNraWtjZ2cHb2xvDhg3DpEmTtPb3psaEomqz8aFDh6J3795GjMh4wsPDERUVBaVSifLycuzbtw+PPPKI1vp79+5FeXk5gMYm3i01A+8uVBMUhtwNV01y1NfXIy8vTzLmB3XvawAbGxs8+eSTqK2txeXLlxETE4OkpCQxMVFXV4e4uDjExcXB3t4eY8eORUhIiN4Jm86kVCol47boO1CmhYUFXFxcxNZJ6p+3PpisIKNTv4utb99u9ZOLgoICkztQUdcTExOD/Px8sRwcHMw74yoEQYBCoUBOTg6uXLmCyMhIsQ9j//79JQNf9TTXr1/H6dOnATT2/541a5aRI+oa1Psbl5WVoaysDGlpaYiMjISfnx+WL18u6SvfE2RmZkrKnp6eOHnyJPbs2SPpNwwAJSUlKCkpQVJSEg4ePIhFixZhypQpnRlulxEXFyfZfpMmTTJiNMbl7e2NJ554AhEREVAqlTh27Biys7PFgaZtbGxQXV2Nu3fv4vjx4+JsRtbW1nj66ad7zFg8Li4u4mOFQoGCgoIWb2LU1dVJLu6AxvNUJiukesI1gKWlJYKDgxEcHIyKigrEx8cjJiYGKSkp4gwzFRUVOHXqFE6dOgU3NzcEBwcjJCSkVbNndISSkhJJCxjV70RLevXqJSYr1D9vfTBZQUanmnkG9O8Dqd4vXv3kjchQxcXF4rSlQPOZLnqqf/zjHzpH27a3t8f06dMxa9YsvZoFdkeq3T/kcjmWLVvWYr9bamRnZwc3NzdYW1ujpqYG+fn5koH7UlNT8fe//x1r1qzByJEjjRhp51IfvDAuLg7Hjh0Ty46OjnB3d4cgCMjOzkZVVRWAxn0xIiICRUVFWLBgQafG3BWodgGxs7NrcTC/7i4sLAy9evXCrl27kJOTg6tXr+Lq1asa68rlcowYMQILFy7UORtGdzNo0CDJLAgXLlxocYrvuLi4Zt1q1M93qeddA9jb24tTSxcVFSEmJgaxsbGS5HR+fj4OHjyIgwcPws/PDyEhIRg3bhwcHR2NFrd6iyJDxqtR/axaM05HzzyrJJOifoDRd6Rz9XpdbaAaMi21tbX45ptvJP0en3jiCTanboG1tTWmTJmCCRMm9NhEBdDYlaHpjsF9993Xo8ft0IePjw8mTZqEESNGaLxDmZaWhiNHjohdsurq6rBlyxa8+uqr6NevX2eHaxRNyYcmTYmK3r17Y+nSpRg+fLjY6quhoQExMTHYuXOn+Ft4+PBh+Pr6IigoqHMDN2E5OTmSMRdCQkI4uwqAgIAArFu3Dr/88ovOgSCHDh2KadOm9ahEBdA4RaODg4PYBebo0aMYO3as1m5+paWl2LNnT7Pnu8oFdWfqydcAvXr1wuzZszF79mxkZWXhwoULiIuLkwzompqaitTUVPz6668YNmwYQkJCMHr06HaflrQlrf2cgMaWJdrWo4+ee2ZJJkN9UBl9BxBTv2vZFQanIdPU0NCALVu2IDU1VXxu6tSpOueP7kn8/PwkU7BVVlYiNzcXNTU1UCgUOHToEI4ePYp58+Zh7ty5Pa7bzM2bN8VZG9zc3PDggw8aOSLT5urqig0bNuis4+vri7Vr1yIyMlJs7VRbW4udO3di/fr1nRGm0WmancHBwQHr169v1gTXzMwMEyZMgKenJz7++GPx93DPnj0YNWoUB+b8/86cOSMp9+QuIE1qamqwZ88ecewKoHFwYC8vL9ja2kKhUCA7Oxu1tbVISkpCUlISBg0ahNWrV/eYbiCWlpaYN28edu7cCaBxm33yySd4/PHHERQUJPl+JSUlISIiQjLeTBOepzbHa4BG3t7eWLhwIRYuXIiUlBTExMQgPj5eTJAplUpcu3YN165dg5WVFZYuXarXFLrtRX37GtJyVPUzbc3nxGQFGZ16drC+vl6ShdNG/UROn2WI1CmVSmzbtk0yJdPYsWOxZMkSI0ZlWhYtWtTsOaVSiRs3bmD//v1ITU1FQ0MDfvvtN1RUVPSobVdbW4sdO3aI/U6XLVvGY1E7mj59OjIzM8Wm+8nJyUhNTe0RLVc03TlbuHChzr7C/v7+mDp1Ko4fPw4AyMvLw82bNzFs2LAOi7OraGhowIULF8Syr68v+vbta8SIjK+mpgb//Oc/cffuXQCN51EPPfQQpkyZIjmO1dfXIzY2Fr/++isqKytx+/ZtfPLJJ3jttdfg5ORkrPA71bRp05CWlobz588DAMrLy/Htt9/Czs4OXl5ekMvlyM3NRWlpqWSZEydOiOWeMtWrIXgN0NyAAQMwYMAAPProo7h+/TrOnj0rGfi9qbtkZ1LfvvpOdaxetzWfE1PtZHTqB6ra2lq9llOvZ8h0UkRA4wX3999/j9jYWPG5MWPG4Mknn+SdyBbI5XIEBATgtddew5gxY8TnIyMjJVOZdnd79uwRm2yGhYVhyJAhRo6o+1EfN0Z1Os/uTP03zdzcHMHBwS0up95aoCd9H3VJSEiQ3O0OCwszYjSmYffu3WKiwtzcHOvWrcP999/f7ILC3NwcEyZMwGuvvSZecBcWFuKnn37q9JiNacWKFXjooYckTeArKyuRnJyMW7duiYkKKysrLFu2DKNGjZIsrz7OAvEaQJuGhgbcuHEDsbGx4sC2xtTaz0m9bms+J7asIKOzt7eXlEtLS5s9p4lq9lrTeoh0USqV2LFjB6Kjo8XnRo8ejaeeeooDIxpALpdj+fLluHnzpjjex8mTJ3vERXtWVhZOnjwJoHGKW00tUKjt3Nzc0Lt3b3FMkJycHCNH1DnUf9O8vb316ifs5eUFCwsL8W6Wav/nnkx1YE1LS0uMHz/eiNEYX2lpqaRbzKRJk1qcTcHDwwOzZ88Wx2O4cuUK8vPz4ebm1qGxmgqZTIa5c+di0qRJOHPmDG7cuIGcnBxUVVXBysoKvXv3xsiRIxEWFgYXFxdxdqgmPXWKXF14DfB/BEHAnTt3mnUBUWVra4ugoCCMHTu2U2PT9DnpS7Vuaz4nJivI6NQHKCosLNRrijr16W+0DXREpE6pVOKHH34Qm3MCjYmKNWvWMFHRCjY2Nhg+fDhiYmIAACkpKUaOqHOUl5eL3T9KS0vx0ksvGbS86rgNNjY2+Oyzz9o1vu7E0dFRPOarz5LRXalPcajvSZ5cLoednR1KSkoAQDJocE9VWloqaZEzZsyYHn+X+/r16+IYFQD0Hoh19OjRYrJCEATcunWrxyQrmjg5OWHevHktzgiiOg6WtbU1z1M14DVA4zTVsbGxiI2N1Ti1p7m5OUaMGIHg4GCMHDnSKIMC29nZwdHRUWydVlRUpPeyqnVb8zkxWUFGpz6qdHp6ul7T06Wnp4uPzc3N4e7u3u6xUffTlKg4d+6c+BwTFW2nOtBaT7mYpM7T1j6vXZGbm5ukhYT6NIi6qG4vznYBnDt3TnJhzoE1G6fqVqXvYJnq9TQNJEmNVLtg+fv7s3upBj31GiA/P19MUGRlZTX7f5lMhgEDBiAkJARjx441iZnpvLy8xO+76vbXpbCwUJIwV0/C64PJCjK6Xr16wdXVVWyqeuvWLb2Wu337tvh4wIAB/BGgFjFR0XFU50rvKYOImZmZGXQCUV9fL5m2y9bWVpw5paff5dWlrq4OeXl5YtmYc813JrlcjiFDhuDq1asAoPeAapWVlZJpT3vKAIi6qB7z3d3dMXjwYCNGYxrUp5pu7VgBPSV5aKjk5GTJd7YzZ27oSnrSNUBZWRni4uIQExMjjhWjzsvLC8HBwQgJCTG5bkODBw8WE3AFBQUoKipqMcmp/nm25tjLZAWZhKCgIHEO+Vu3brX4BSgqKpJ8AVQH+CPSRBAEJio6kOr3sac0CR44cCA+/fRTveufO3cO33//vVjesGEDXF1dOyK0buXSpUuSC6SW+tV3J2PGjBGTFcXFxcjKymp2J1JdUlKS2D0JAPr379+hMZq6W7duSZJdbFXRyNnZWVJOS0tDv379WlxOtWsDAJ2z0/RkBw8eFB/b2dl1+hgDXUl3vgaorq7GxYsXERsbi5s3b0paeDVxdnbGuHHjEBISAh8fHyNEqZ+goCD8/vvvYjk6Ohpz587VuYzquHB+fn6tmu7Y9NNQ1CNMnDhRvMMoCILkIK/JgQMHxJMxKysrjBs3rsNjpK5LU6IiKCiIiYp2Eh0djezsbLGsPgI6UWuVlZVh7969YtnKygrDhw83YkSda8yYMZIWLzgWAAAgAElEQVSxKg4cOKCzfkNDA44cOSKWLSwsMGLEiA6LrytQPe7L5XJMmDDBiNGYjsGDB4vnXQBw/PhxNDQ06FxGEATxohJobKrOVirNnTp1SjKDg/oMIiTVXa8BSkpK8Morr2DHjh3NxoixtrZGaGgo1q1bh7///e9YvHixSScqAKBPnz6SacNPnDihc0yk27dvS7pCtTZRzGQFmQRvb2+EhISI5TNnzjQbRblJVFSUZFTvGTNmdItRgKljCIKAiIgIyT4zZswYPP3000xUaJCUlIRffvlF4yBP6pRKJaKiovDDDz+Izzk4OGDy5MkdGSJ1YSkpKYiIiNBrRo979+7h008/lQzOdf/99/eo472NjQ3mz58vluPj47WeyDc0NOD7779HZmam+NzUqVPh4ODQ4XGaKoVCgfj4eLE8YsQIdov5/5ycnBAYGCiWs7KysHXrVq3dQRoaGrBz505cv35dfC4oKKjHfB8bGhoQHR2tc+yYhoYGHD16FD///LP43JAhQzBlypTOCLHL6q7XAPX19ZL9xczMDIGBgXjqqafw8ccfY9WqVQgICOgSXViaPPzww+LjsrIyfPfdd5LurU3y8vKwbds2Mank7u7e6mQFu4GQyVi0aBGSk5PFfms//vgjEhISMH78eDg5OaG0tBSxsbFISEgQl/Hz88OsWbOMFbJRHTx4EIcOHWr2vOqdkcLCQjz33HPN6oSGhmLZsmUdGp+piI+Pl/zoyWQyVFVV4csvv9R7HTNmzEBAQEBHhGdyamtrcfz4cRw/fhy+vr4YNGgQ+vTpA0dHR1hbW6Ourg5lZWXIyMjApUuXJNMimpubY+XKlSYxEBSZpvr6ekRFRSEqKgp9+/bFkCFD0LdvX3H/qqmpQV5eHpKSkpCYmCjpzjBs2LAWm5x2R+Hh4bh27RoSExMBAL/99hsSExMxYcIEeHp6QqlUIjMzE1FRUZLuDj4+PnjwwQeNFbZJiImJkVx8swuI1COPPILk5GRxjJP4+HikpKRg4sSJ8Pf3h62tLRQKBdLT03H+/HnJ/mVnZ9ejpmtWKpXYtm0bdu3ahcDAQPTv3x/u7u6wsLBAeXk50tPTER8fL9lG/fr1w9q1ayUtWLqyjjzv7M7XAP7+/ggJCcH48eM7JbFSWFiIt956q9nzqr+nAPD5559r3De/+uorreseMmQIpk6dKk7bnpSUhL/97W8IDw9H3759UVdXh+TkZERFRYljmZmbm2PFihWtvkHIZAWZDEdHR7zwwgv4/PPPxTtpCQkJkgOTqr59++K5557rsYM7KZVKvUaH11Snpaae3Yn6XSJBEHDjxg2D1jF+/Pj2DKnLSEtLQ1paml51XVxcsGLFCgwbNqyDo6LuIjMzU9IKQJcJEybg8ccfbzYoYE8gl8uxZs0abN68WWxafvfuXa0DtAGN43qsXbu2x/4+NlHtAuLo6ChpSUCAh4cHXnjhBXzzzTcoLS0F0Nh0XdMFqSpnZ2c8++yzPXLMnaqqKly4cAEXLlzQWS8wMBCrVq3qVsn7jjzv7I7XAI6Ojnjvvfc6fRwvQRD0+pxaey2wZMkSVFZWIjY2FkDj4M+7d+/WWNfS0hKrVq1q01hTPe9Xn0yap6cn3nrrLezduxfR0dEamxbZ2dkhPDwc8+bN65EnrkQdydfXFzNmzMD169dx7969Zpl4dd7e3ggNDUV4eDisra07KUrqqlxdXRESEoK7d+9K7kBqIpfLERgYiOnTp2Po0KGdFKFpsrS0xLp163DmzBkcPXpU67ZzcXHBzJkzER4e3uO7uWVlZUkSOqGhoT1+m2jSv39/vP322zhy5AjOnz+P8vJyrXUdHBwwadIkzJo1q8fM+tRELpdj9OjRuH37ttZ++jKZDP3798fMmTMxevToTo6w6+tu1wCWlpbdcsBxuVyOp556CiNGjMDBgwc1/h7J5XIEBATg0UcfhYeHR5teTya0dCZKZCS1tbW4desWCgsLUVVVBXt7e7i5uWHQoEE84SDqBDU1NcjMzERBQQHKyspQW1sLc3Nz2NjYoFevXujXrx/7f1OrVVRUIDMzE+Xl5eJ0mxYWFrC1tYW7uzt8fX1N+q6ZMWVkZODevXvi3XAHBwf4+vrC29u72zQ5p86nVCqRlZWFjIwMVFZWoqamBlZWVrC3t0e/fv3g5eXVpfrXdwRBEHDv3j3k5eWhrKwMVVVVsLOzg5OTE/z9/fmb2E54DdB1pKeni79HFhYWcHZ2xqBBg9ptmnEmK4iIiIiIiIjIpPTs9CgRERERERERmRwmK4iIiIiIiIjIpDBZQUREREREREQmhckKIiIiIiIiIjIpTFYQERERERERkUlhsoKIiIiIiIiITAqTFURERERERERkUpisICIiIiIiIiKTwmQFEREREREREZkUJiuIiIiIiIiIyKQwWUFEREREREREJoXJCiIiIiIiIiIyKUxWEBGZiNTUVMhkMvFv+/btxg6J1Jw8eRLLly/H4MGD4ejoKPm8/vznPxs7vG7lnXfekWzfjrJy5UrxNfz8/DrsdYg6iur35J133jF2OD2an5+f+FmsXLmyQ16D5wrUkzBZQUQ6qf8oNv0dO3bMoPVs375dsvy+ffs6KGKi9ldfX4+VK1di2rRp+OGHH3D79m2Ul5cbOywiIiKibsvc2AEQUde0YcMGzJgxw9hhEHWK999/H99//32z583MzDQ+ps61fft2pKamAmi8s9lRdzSJiNpDSUkJPvvsM7G8YMECjB492ogREZkmJiuIqFViY2Oxb98+LFiwwNihEHWoyspK/OMf/xDL/fv3x+bNmxEWFgZbW1sjRkZNtm/fjlOnTgEAwsPDmawgIpNWUlKCjRs3imU/Pz8mK4g0YDcQImq1N998E0ql0thhEHWo48ePS7p8bN26FTNnzmSiooO98847EARB/Oso27dvF1+jqXUGEVFrpKamiscTjiVB1HZMVhCRQczN/69B1tWrV7Fz504jRkPU8S5fviw+trKywuTJk40YDREREVHPwGQFERlk8eLFsLOzE8tvv/026uvrjRgRUcfKz88XH7u6unJsCiIiIqJOwGQFERnE3d0d69atE8vJycls6kjdWkVFhfhYtWUREREREXUcnnURkcHWr1+Pr7/+GiUlJQCAd999F8uWLYOVlZWRI2sfNTU1OHXqFNLS0pCfnw9nZ2eMHz8e48eP17lcbm4uTp8+jdTUVNTX18PLywtTp06Fr69vu8SVl5eHs2fP4t69e6iqqoKXlxeCg4MxZMiQNq87Pz8f586dQ3Z2NoqKiuDo6AhPT0+EhYXB09OzHaJvdOvWLVy5cgXZ2dmoqKiAn58fli5d2m7rb3L79m3ExcUhLy8P1dXVcHV1ha+vL8LCwmBjY2PQujpyvAR9paWlITY2FpmZmaivr0efPn0QFhaGfv36tWm9VVVVOHPmDNLT01FQUAAbGxu4u7tj/PjxGDhwYKvXW1xcjPj4eNy+fRulpaVoaGiAra0tPDw80L9/f4wcOZJjfuhBqVQiLi4ON2/eRH5+Purr6+Hm5oYhQ4YgJCSk01r5KBQKXL58GUlJSSgqKoJCoYCNjQ169eoFPz8/jBgxAm5ubgats7S0FImJibh16xaKiopQW1sLZ2dneHh4ICQkBH379m3X93DlyhVcvXoV9+7dg1wux5AhQzB9+nRJS0F1dXV1OHv2LK5evYqysjK4uLggMDAQEydOhFze9vt99fX1OH36NO7evYu8vDz06tULAwcOxJQpUzo1MVpWVoYzZ87g3r17KCgoEL+roaGh8PPza9U6BUHAtWvXkJCQgNzcXFRWVsLS0hKOjo7w8fHB0KFD0b9///Z9IwSgMcF+6tQppKeno6SkBL1798bIkSMxfvx4tgykrkcgItLh7t27AgDxb926dYIgCMJ7770nef7zzz/XuZ5t27ZJ6u/du1dr3RUrVoj1fH199Y5V/TXu3r2rta6vr69Yb8WKFYIgCEJZWZnw8ssvCy4uLpL1NP2NHDlSOHfuXLN1paamCosXLxbMzMw0Ljd//nwhPT29xfjVt/W2bdvE9S9cuFCwsLDQuP6xY8cKUVFRem8nVfv37xdCQkIEmUymcd0AhNDQUOHo0aN6re/tt9+WLNtk3759wtixY5ut28nJqVVxa1JfXy988803woABA7S+FxsbG+Hxxx8XUlJSdK5L2/La/pr2odY6ceKEZH0nTpwQBEEQrly5Itx///0aPx+ZTCZMmzZNSEhIMPj1bt68KTz66KOCtbW11vc0aNAg4bvvvhMaGhr0Xm98fLwwb948wdzcXOf2MjMzE4KDg4XNmzdrXZe2famJ+vddnz9Nx5OWjjfXr1+XrOOVV17Re3s0efXVVyXruHr1qs76ubm5wosvvij07t1b63txcnISXn31VaGwsNDgePSVnZ0trF27VnB0dGxx2w4aNEhYv369UF1drXV9SUlJwltvvSWMHTtWkMvlOtc3YsQIYfv27Xrvf9r2l59//lkYOXKkxtdwcHAQPvjgA0GpVErWVVdXJ3z44YeCq6urxuV8fHyEffv26RVXeHi4uFx4eLggCI3Hqr/97W+Ch4eHxvX37t1b2LRpk1BXV6fXa6gu+/bbb+u1jCAIQlRUlHDffffp/L4OHz5c2LlzZ7NtpI1CoRA2bdok+Pj4tLjPuLm5CStXrhSSk5P1jlkb1XMSKysroaqqSmf99evXS2IZPHhwi68xf/58sX5wcLDGOprOLVSp7g/6/ql/ptrOFYqLi4U1a9YIdnZ2Gtfj5eUlfPvtty2+TyJTwmQFEemkLVlRXl4uuLm5ic97eHgIlZWVWtdj6smKzMxMYejQoS2eNFhZWQlHjhwR1xMVFSU4Ozu3uFy/fv10xiMImk9AIiMjhV69erW4fplMJmzatEnvbVVcXCzMnDnToBOmZ555psWTZ00XDC+88ILWdbZXsiInJ0djMkTbn6WlpXiCp4mhJ5Mdkaz4+eefBRsbmxZf28LCQtixY4fer/X111+3mExQ/ZswYYKQn5/f4nq/+uqrFi9A1f+GDBmidX2mkqwQBEGYMGGCWMfDw0Pvi0hBaLww9fb2FpfXdpHTZNeuXYK9vb3e78nLy0uIjY3VOx59nTt3TmviVtdfdna21nU6OTkZvL558+YJpaWlLcZr6LFH9W/lypXiesrKyvS6oJTJZMJ3333XYlzqyYrS0lK9L1hDQkKEkpKSFl9DdRl9khUKhUJYtmyZQZ/DQw89pPM3XhAaj8OjRo0y+DP++eefW4y5JdHR0ZJ1tpRgDwoKahZHWlqa1vp1dXWCg4ODWPeNN97QWM9YyYrLly9LXlvX37PPPtvi9iQyFewGQkStYm9vjzfeeAMvv/wygMYuEF988QVef/11I0dmuJqaGsybNw83btwAAIwZMwaTJk2Cs7MzcnJycOjQIdy7d0+su3TpUty6dQt5eXmYN28eysvLYWFhgSlTpmDUqFGwtbVFcnIyDhw4II53kJGRgVWrVuHEiRN6x5WRkYFXXnkFRUVFAIBx48ZhwoQJcHJyQkZGBg4fPoy8vDwAgCAI+Otf/wo7Ozu8+OKLOtebn5+P8PBwXL9+XXzO0tISkyZNQmBgIJydnVFRUYErV67g1KlT4gCq33zzDaqqqvD999/r/R4++ugj/Otf/wIAODg44P7778fAgQNhZmaG1NRUnD9/Xu91aVNYWIiJEyfizp074nMymQwTJ07EuHHjYG9vj/T0dBw+fBgFBQUAgNraWqxatQrV1dV49tlnm61TtamsUqmUdAXR1Iy2vZvWXrp0CW+88QZqamogl8sxefJkBAUFwdbWFnfu3MHhw4dRWloKoLGp+sqVK2Fvb4+FCxfqXO8///lP8TvbxNXVFbNnz4avry8qKioQFxeHc+fOie/5/PnzCAsLQ3R0NJydnTWu98SJE3juueckz/n7+2PSpEno27cvrK2tUVFRgezsbCQmJuLq1attnvZYLpeL272hoUHyf9o+j9Y2rV+1apW4r+bm5uLw4cN44IEH9Fr2P//5D7KysiTr0ubLL7/Eiy++KNnf/Pz8EBYWhr59+8Lc3ByZmZk4fvw4MjIyAADZ2dmYOnUqoqOjMWLEiNa8vWYKCgrwwAMPoLi4WHzOxcUF4eHhGDBgABwcHFBdXY3CwkIkJSXh0qVLqK6uNug1Bg8ejICAAPj5+cHBwQGCICA/Px+XL19GTEyMuA0OHjyI5cuXY9++fQat/8MPPxSPPe7u7pg1axZ8fHxQU1OD8+fP4+zZs2Ld7du3Y8qUKVixYgUeeeQRnDp1CgAwZMgQTJ06Fe7u7iguLsbRo0dx+/ZtAI3H3Oeeew5hYWEGdcV76qmnxPV7eHhg9uzZ6NevH0pLS3Hu3DnEx8eLdS9cuIBZs2YhKioKlpaWBr1/bRQKBWbOnInTp0+Lz8nlcoSEhCAoKAi9e/eGQqFAUlISIiMjxc91//79mD9/Po4dO6b1+/XYY4/hypUrYtnKygphYWEICAiAi4sLGhoaUFpaips3b+LixYsoLCxsl/cENP4+Ojs7i91T//jjD8ycOVNj3cLCQsksT03++OMPrF69WuMyFy5ckExhff/997cqTjMzM43HLblcDplMpnGZlrocZWVl4b//+79x7949yGQyjB8/HiEhIXBxcUFBQQH++OMP3Lp1S6y/efNmTJw4Ef/1X//VqvdA1KmMmCghoi5AW8sKQRCE6upqoU+fPuL/ubi4aL0LZMotK5q6V/Tt21c4efJks7rV1dXCf/3Xf0nW/frrr4t3ZqZMmaKxGeu9e/eaNT8+duyY1pjUt7WVlZXOuGpra4W33nqr2TLXr1/X+hpKpVKYNWuW5O7gs88+K+Tm5mqsf+fOHWHatGmS1/j++++1rl/97mZT15jnnntO493RmpoarevS18KFCyWvGRAQIMTHxzerp1AohNdff73Z9rp8+bLO9bd2fzSEesuKps9++PDhwpUrV5rVLy8vF55++mnJMq6urkJeXp7W14iNjZV0JZLJZMKbb76p8TOIjY0VBg8eLFn/kiVLtK5bdR+xtbUVfvnlF53vNy8vT/j222+FxYsXa63TUssKVZqa2utLn8+3tLRUsLW1FestXLhQ7/UvXrxYXM7a2lrrMfLUqVOSrmR+fn7CgQMHNDa/r6+vF7799ltJy5uhQ4e2y/dJEARh48aNkm2/YcMGnd07qqqqhN9++02YM2eO1mOJIAjCrFmzhC1btghZWVk6X//OnTvCAw88IIlh586dOpdR318sLCwEmUwmbNy4UVAoFM3q//vf/xYsLS3F+j4+PsJHH30kAI0tvn799ddmy9TX1wuvvfaa5HWeeOIJnXGp7ptN32uZTCa88847Qm1tbbP6kZGRkpY4AIS33npL52uo1m2pZcVTTz0lqf/II49o/a3Mzc0VlixZIqm/ceNGjXVPnTolqTd37lyd+0JDQ4Nw/vx54emnnxb279+vM2Z9LViwQHz9oKAgrfV27drV7DcKgPD4449rXeadd94R69nY2GjcpwSh5ZYVTbR15dCH+rJN+3FQUJBw6dKlZvUbGhqEjz/+WNKd0NfX16AufkTGwmQFEemkK1khCILwzTffSP7/zTff1LgeU05WABCcnZ2FO3fuaK2vUCgEPz8/sX5Tc/eQkBCtJy2C0NhHW7VpvCEnL00nzTdu3ND5vtXHD3nggQe01lXfRl988YXOdQtCY1JkypQp4jL+/v5CfX29xrrqFwwAhL/85S8tvkZrRUZGSl7L399f5wmyIDS/EJs+fbrO+sZIVjS9VkvvRf3C44UXXtBaV/UzBCB8/PHHOtd97949oW/fvpJlNI2NUltbKznh/9vf/qbfm26BKSUrBEGQNJu3sLDQq2tMUVGReIEKQFi6dKnGevX19UL//v0liQddiacmhw8fllyAbN26tcVl9DF58mRxnffdd1+7rNNQDQ0NkjECQkNDddbXdOz57LPPdC7zxhtvSOrL5XLBwsJCuHDhgs7lVLsF2djY6OweoanZ/wcffKBz/UlJSZIuB5aWljq71+ibrFA/zrz88ss642iydOlScRkHBwehuLi4WZ0333xT8rtVXl6u17rb05dffinGIJPJtH5HVRO9q1atEh+7u7trHZsjLCxMrDdjxgytMRgjWQFACAwMbHGbr169WrJM09hIRKaMU5cSUZusXr1aMqL3Z599Jja170ref/99+Pv7a/1/KysrrFy5UiwrlUrIZDJs3bpV5ywow4YNw+TJk8Wyod0eNm7c2GIT49dffx2jRo0SywcPHkR6enqzeoIg4KOPPhLLc+bMwQsvvNBiDBYWFvjmm2/EJqp3797FkSNH9Irfz88P77//vl51W+OLL76QlL/++mu4u7vrXGbDhg0ICgoSy5GRkUhMTOyQ+Nri888/b/G9/POf/4SXl5dY3rFjB6qqqprVu3LlCqKiosRyaGgo/vKXv+hct7e3d7Pt+/nnnzerV1BQIGnOPG7cOJ3r7apUu2/U1dUhIiKixWV++ukn1NTUaFyHql9//VXSjWn79u16za4xe/ZsPProo2J58+bNLS6jj9zcXPGxsT5PuVyOt99+WyxHR0cb1G0gODhYMs22JmvXrpWUlUolXnrpJQQHB+tcbs2aNeLj6upqSdeHlowZMwbr16/XWWfYsGGS915bW4utW7fq/RrafPDBB+LjwMBAfPjhh3ot9/nnn4uzppSXl2vc91X3maFDh8Le3r6N0RpOtWuGIAg4fvy4xnp//PGH+Pj5558XZ1XKy8vT+FtQUVGBCxcuaHwdU7F9+/YWt7l6F8D26IZJ1NGYrCCiNrGwsMA777wjlsvLyyUnRF2BtbW1JBGhTUhIiKQ8efJkDB8+vMXlQkNDxce3b99GXV2dXnHZ2triySefbLGemZkZnn/+ebGsVCqxf//+ZvViY2Ml41S0NLaFqmHDhiEwMFAs6zv2xurVqztsStva2lpJ0mTYsGGYPXt2i8uZmZk1O2n77bff2j2+tvD19cWDDz7YYj17e3tJH+vS0lKNJ+jq+8NLL72ktX+0qoULF0qmMD106FCz/Vd96sdLly61uN6uaOrUqZKE5rZt21pcZvv27eJjHx8f3HfffRrr7dixQ3wcEhLS7Fijy5IlS8THFy9eRFlZmd7LaqN60WPMz1P1mANAcsHYEvVEhCa+vr7w8PCQPPfMM8+0uJzqMR0AkpKS9I7r+eef12va0zVr1kimWd6zZ4/er6FJbm4ujh49Kpb/9Kc/6T2Gi6urq2Tf1XT8V91nbt68qTFp2tGGDBkimfJWNSnRJDU1FXfv3gUA9O7dG0FBQZL3pmmZU6dOSY572r7HxjJx4kSMGTOmxXrDhw9H7969xbLq+QCRqWKygoja7IknnkBAQIBY/uqrryQDypm6MWPGwNbWtsV6qidBABAWFqbX+lWXEwRB74uJadOm6X13Sv3CVtNJfdOgbkDjAJT6xt9k0KBB4mNNg5NpMnXqVINewxCXLl2CQqEQyw899JDeyy5YsEBywXDu3Ll2ja2t5s+fr1cyAdDvs1e9g2ZmZob58+frHYvqoJ3V1dXNPntHR0cMHTpULL/77rvYtWtXmwfQNDUymUyS1Lxy5YrO78G1a9cQFxcnlleuXKnxM21oaJAM9qjaEksfqt9LpVKJhIQEg5bXRLVlwX/+8x/89a9/RWVlZZvX26SiogI//PADVq9ejXHjxqFPnz5wcHCAhYUFzM3NxT/1RFhmZqber6HvdlQ9Pnt7e+tsYadpGQDioI760Pe75+DgIDl+JiQkGDyIqSrVllVA2/YzTfu96j5TUlKCxYsXiwNTdybVRMKxY8ea/b/qc/fddx9kMpmkpYSmZIXqc7169ZK0zDMF4eHhetf18/MTH6sOoEtkqpisIKI2k8vlePfdd8WyQqHAe++9Z8SIDNOnTx+96qmfOLd2uaYZQlqi2rWjJe7u7pLuAJru9KneIRUEAc7OzpILg5b+VO/s6dscW/Uitr3dvHlTUjbkBNLe3l5y8t00E4ypMOSzDwwMlCReNH32qttq8ODBeiXnmqjfsdO0rV566SXxsUKhwGOPPQZfX1/86U9/wi+//GKUi5aOsGLFCknCQVfrCtX/U090qEpJSZEkMD/99FODvpejR4+WrK89Zlh4/vnnYWFhIZb//ve/w8vLC4899hi2bNmC69evS2Ys0Vd9fT0++eQTeHt7Y/ny5di2bRvi4+ORlZWFiooK1NfXo6GhQfKnypCLq9Ycnzv6mO7t7a1X954mqp9tfX29OBNJa6i3kBk1apRB+9mnn34qLqtpH1uwYIHkQvjQoUPw9fXFjBkz8MknnyA6Ohq1tbWtjl9fqomHtLQ0JCcnS/5fNfHQVFd1maioqGatx1SXmT59ul4tYzqTvvstIN139d1viYzJtL5tRNRlPfzww5KLmu+++05samnq9L1wU78r2trl9D3JV2+ebEh9TSf16mOJqF8UtPSnGnfTtJkt0TbVZXtQf4+enp4GLa9a39TuMBny2dvY2MDR0VEsa3ovqs+1ZTtpW/+aNWskXZGAxrvgmzdvxpIlS9C3b1/4+/tj1apV2L9/f6dctHQEX19fTJ8+XSxHRERofC/19fX48ccfxbJ6FxJV6t9LpVJp8HdTlb7fTV2GDx+Obdu2SbpwlZeXY9euXVizZg0CAgLg6uqKhQsXYsuWLXp9f+rr67F06VKsX79eMgWkIVRbUrWkNcdnUz6mA207TnX08d/Kygr79u2Dt7e35DX++OMPrF+/Xpx2e9q0adi0aZNkjJb2pD6ehGqiQRAEREZGNqvr4eEhTvtbWVkpaYmWm5uLq1eval2/KVBPnumiuu+2JuFI1NmYrCCidiGTySStKerq6rBx40YjRtT1GXICol5f0x0TQ5oqt0TfJv6qd2fbm/oFj6HbS7WLTWsvnjpKe3/2qu+vLcUlUlIAACAASURBVNtJfV2q/vWvf+HIkSOYMmWKxu4Oqamp2L59OxYsWAB/f39s2bLFoDhMheoYIYWFhThw4ECzOocPH5YMOKhtYE2gfb+XgP7fzZY88cQTuHTpEh5//HGN484UFRVh3759WLNmDfr06YP169frHKfg008/xa+//iqWrayssHz5ckRERODy5cvIz89HVVUVlEolhMbZ6ppdTHX1i6u2fK+Btt0Jb8/9TNvnMGrUKCQmJuLVV1+Fq6trs/9XKBQ4efIkNmzYgIEDB2LRokVIS0trt7iAxuSq6lhSqt0+Ll++LCZtBgwYIEkgausKot4txBSTFUTdGZMVRNRu5syZg0mTJonlH3/80eSa13clhvYRV62vaawL1buGffr0kVwQGPqXmpra6vfVXhwcHCRlQ7eX6om/+rqMrb0/e9X315btpL4udbNmzcKpU6eQlpaGbdu24cknn0RAQECz5EVWVhbWrFmj8yLeVD388MNwcnISy5q6gqg+5+joiEWLFmldn/rd/C1btrTpu6nPYMH6GjZsGH766Sfk5uZi7969ePnllzFhwoRmScjq6mp88sknmDhxosa77rW1tdi0aZNY9vT0xMWLF/H9999j6dKlGDVqFFxdXWFjYyPZV7pbM/W2fK8Bzd9tfanuZ3K5HDU1NW3az7Tp1asXPvzwQ2RlZeHEiRN45513MGvWLEnrL6Ax4bFnzx6MHj1a7zGQ9KU+GGhTAk9TFxBNZW3JCl9fXwwYMKBdYyUi3ZisIKJ2pTpNZUNDA9566y2D19HaZord7cQ2Ly/PoPqqd3JdXFya/b/qna6srCyDmlSbIvX3mJOTY9DyqvU1bS9jMuSzVygUkjEPNL0X1efasp20rV9dv379sHLlSnz33Xe4du0acnNzsWPHjmaj6G/fvl3SXaIrsLa2xmOPPSaWjxw5ItlGhYWFOHjwoFhesmSJzu4F6negU1JS2jHa9uHk5IQFCxbgH//4B86dO4fS0lIcPHgQy5cvh6WlpVjvypUrGqfEPX36tCSJ8cEHH0gGZdZG9ZjWHbTlmA607Tilup8plcoO76ZpYWGBqVOn4u2338aRI0dQVFSE6OjoZq0uSkpK8NhjjzXrztQWqomH4uJixMfHA4BkpiT1ZEV4eLiYhIuJiRGPqboSHETU8ZisIKJ2FR4ejhkzZojl3bt3G3zXRLXpqyHTn3WlGUj0Ych2y8/PR3Z2tljWdCGg+pwgCM1Gh+9qhgwZIikbMsViRUWFZLC6jhwItDUM+ewTExMlTf81ffaq2+rWrVsGfa8uXrwoKbdmW7m5uWHZsmX4448/8N1330n+ryt2B1HtCqI+PoX6OBYttR4ZMGCApJuF6qw9psrGxub/tXffUVFc///4n0sv0gQRRCkqojGCgoBItcQaS0w0RMXYaxKNUZNofNtJMLElRpIolnzUiCYRE0VjBQUEEVRQUBBFigVEQIr0+f3hj/nubJ2FXVj19Thnz9kLd+7cmblzd+bOvXcwYsQI7Nu3DwkJCZwn/gcOHBB7a4XoZLjDhw/ntR7ht6m8DvLz88XmjpDlxo0b7HctLS3OpMCKEq0XWrqcaWpqwtPTEyEhIcjMzOS8lvbOnTtK/T0KCAjgvJb1zJkzqKmpwaVLlwC87FkiPPcM8LLXSuMrg+vr63HhwgXcvn2b8wYaaqwgpOVRYwUhROmEe1cwDINvvvlGoeWFnx4VFRXx7jHxqt98i4qKiuLdbfiff/7hhBsvuoSJPtU+ePBg0zOnBlxdXaGnp8eGjx07xnvZY8eOcW7w+/fvr9S8NdeJEyd49yric+yFt6++vl7iPAvSREREsN/19fXF3j6hqBkzZsDNzY0NC9+QNZXwsARlPqGVxsPDg3PzJzzsQ/h79+7d4eXlJTMtfX19TpyEhAS17F0hTe/evTFnzhw2XFVVJdY4ITpfAt8eAocPH25+BtUM33OvrKwMUVFRbNjZ2Rn6+vpNXq/ojXZr1v+mpqacYUGAcuqBRkZGRpxXqZ49exZxcXFsI62rqyvatm0rtpzoUBDhXhUCgUDsN7Q5RIdStUS9RciriBorCCFK5+7ujjFjxrDhEydOcGbXlke0B0Dj0xBZUlJSEBsbq1hG1VxFRQXCwsLkxmtoaMD27dvZsIaGBmf/N/L29oadnR0bPnDgAGeW81eNtrY25wlteno6Tp06JXe5hoYGbNmyhfM3SfurNWVnZ+Pff/+VG6+8vBy7d+9mwyYmJhIvqEW3b8uWLbwaQ/755x9OD5R3331XKZOmCo/7VsabQYTn0WipN7sI95hIS0vDlStXcOPGDU6vGL5zckycOJH93tDQgBUrVigvoy1AdBy/6DEVneeEz5w3qampCjVAviq2b9/O69zbuXMnpwfUuHHjmrVeW1tbTqNldHQ0r/pSVeSVmeYSbniIi4vjNOpK6yEhq7HC2dlZodfOyiN6TqjbG6kIURfUWEEIUYl169Zx3kUufEMlj+iTyK1bt8qMX1lZ+UpO1MfHqlWrODeLkmzcuJFzgzRy5EjY2tqKxdPS0sLy5cvZcF1dHd577z2Fh8/ExMSgurpaoWVU5bPPPuOE58+fj8LCQpnLfPvtt+wYZuDlBWrja+vUyaJFi+Ruy5IlSzjHb8qUKRLnR3B2doa/vz8bjo+Px6ZNm2Sm/ejRI7HXkS5cuFAs3uPHj5GTkyMzLWE1NTWIj49nw/b29ryXlUY4jYyMDKW8vlOeoKAgTlfzvXv3cnpVaGpqIigoiFdaU6dO5TQkhoeHY+PGjQrlp7S0VGnDJhISEhSKL9qrTXhbAHDezgDIH/pTXFyMSZMmvZZPm5OSkuSee3fu3OG8TUtbWxszZsxo9rpXrVrFCQcFBSEtLU2hNFJTUyXWS0lJSQodL9Eyo4x6QJhwo211dTV+/fVXNiytscLT05NtRLh9+zZOnz4tMT1lMDY25vTuSExMVGr6hLwuqLGCEKISvXr1wocffsiG6+rqeC9rb28PPz8/Nnz69Gl8+eWXEtO4desWAgICkJyczJno7XWgq6uLkpISDBo0SOIQl7q6Oqxdu5bTAKGrq4uQkBCpac6YMYMzp8jdu3fh6uqKAwcOyLzQLC4uxu7du+Hr6wtfX1+xMemtJSAgAO+99x4bvn//PgYMGCBxzoeamhp88803nGFJurq6+OGHH1okr4rQ1dXF/fv3MWjQIKSmpor9v6KiAvPmzeNcgJubm2PlypVS09y0aROnV8SyZcuwevVq1NbWisVNTk7GwIEDkZuby/7tww8/5Lztp9Ht27fRuXNnTJgwARERETLLRmFhIQIDAzmNG819YgxwGzhra2sxffp0pKenK+01npK0b98eI0aMYMN//PEHp2v9sGHDYG1tzSstbW1t7Nmzh9P48eWXX2LSpEm4d++ezGUTEhLw+eefw87OTmmTlfbr1w/e3t7YuXOnzAaz6upqrFu3DocOHWL/5u3tjfbt23PieXt7cyZV3LRpE3bs2CGxh8HVq1fh5+eH1NRUhV/1qe4a5yZZtmwZ1q9fL/E37eLFixg8eDBn0tyvv/4aVlZWzV7/kCFDOPOtPH36FP369cP27dtlTrhcUVGB8PBwjBgxAs7OzsjPzxeL88UXX8DR0RHBwcHIyMiQmY+jR49i6dKlbFhfXx/Dhg1rwhZJ5+XlJXH+K319fYn1GPCyQV+4UVe4LlPFfBX9+vVjv//1118IDQ1FUVGR0tdDyKtMS34UQghpmjVr1uDIkSMKNVQ0Wr9+Pfz9/dmL2Y0bN+Lw4cMYNmwYLC0t2aeIly9fRkNDA6ytrbFgwQKF58dQZytWrMDWrVuRm5sLf39/eHh4oF+/fjA2NkZ+fj4iIyPFZosPCQlBjx49pKapqamJ8PBwBAQEICUlBcDLGecnT56MRYsWwd/fH/b29mjTpg3KyspQUFCA69evIz09XW2fcu7cuRM3btxgb+pu3boFV1dX+Pj4wM3NDW3atEFubi4iIyPFbry2bNkCFxeX1si2TMHBwVi+fDlSU1PRu3dv+Pn5wdXVFfr6+rh//z4iIyM58wBoaGhg586dMrspu7m5ISQkBIsXLwbwcojVmjVrsGPHDgwfPhy2traoqKhAYmIiYmNjOTeSTk5O+OWXX6SmXV9fjyNHjuDIkSPQ09ODs7MzevXqxb6OsrS0FGlpaYiOjubcFNna2nJuWppq1KhRsLKyYt/K8ffff+Pvv/+GpqYmZ14TOzs73Lp1q9nrazRt2jS2e7novAzCN4V8DBgwADt27MDcuXPZRpaDBw/i0KFD6NOnD9zd3WFhYYGGhgaUlJTg7t27SEpKUtnNTVxcHOLi4jB37lx0794dvXv3hrW1NYyMjPDixQvcu3cPFy5c4EwYqaWlJTbECnh5k/7NN99g0aJFAF4OdVmwYAG2bduGwYMHw8LCAsXFxYiPj+c8Yd62bRtmzpypku1rDf369YOlpSWOHDmClStX4ueff8bw4cNhY2OD58+f4/Lly2JP2D09PZU6LCg0NBQ5OTnsEIeysjJ8+umnWLFiBXx9fdGtWzcYGxujoqICT58+RWpqKlJTU3kN07h//z5WrFiBFStWoFOnTnB1dYW9vT1MTU1RV1eH/Px8xMTE4O7du5zlgoODxV5t2lza2trw8/PDyZMnOX/38fHh1AmiBg8eLDaviI6ODucBirLMnj0bkZGRAF7WofPnz8f8+fOhq6vLabhcvnw556EEIW8UhhBCZLh//z4DgP0sXLhQoeVnzJjBWb7xc/ToUbnLbty4UeKyoh8bGxsmKSmJ2bNnD+fv9+/fl5q2nZ0dG+/jjz/mtS2i+2LPnj28luObL0npnzt3jjEzM5O7DwQCAbNhwwZe+WEYhikvL2cmTpzIa/+KfrS0tJjnz59LTHfVqlWcuC3l0aNHjKurK+9t0NbWZsLCwnil/fHHH7PL2dnZqST/Fy5c4OTvwoULzMGDBxk9PT1ex2Pfvn2817Vjxw5GS0uL977q168fU1hYyDvvfD+Ojo7M3bt3paaraFm6cOECY2pqKnOdko5fc45vbW0tY2lpKbYeCwsLprq6WqG0Gp0+fZpp165dk/bpsmXLmrROUU1Zt5GREXPixAmZ6U6ZMoVXWgKBgAkODhbLy6pVq6Sm3dS6x9/fn13G39+f93J88yWafmlpKePr68trP3h4eDDFxcVKy0uj2tpa5vPPP2cEAoHCx1kgEDA3b96UuZ18PxoaGszq1avl5repNm3aJLbOkJAQmcvcvHlTbBk/Pz/e61T02mLRokVy95PoMW3qtQjDNL28E9JaaBgIIUSl/ve//3Fey6eIpUuX4u+//xabiKuRnp4egoKCcP36dbi6ujYnm2pr4MCBSEpKwqhRozhPWoS5uroiKipKoScvhoaGOHDgAC5fvoyxY8fKnWVeR0cH/v7+2LRpE/Ly8sQmB2ttVlZWSEhIQGhoqNTyArwsM4GBgUhPT1f4yXdL++ijjxAXF4eAgAAIBAKx/wsEAgQEBCApKQlTpkzhne68efNw69YtjB8/Xua56ejoiJ07dyImJobThV+Uu7s79u3bh/Hjx8PS0lLu+rt27YqQkBCkpKTIPFaKCggIQHp6OjZs2ICBAwfC2tq6WW9P4ENLSwuTJ08W+/ukSZOaPCztnXfeQVZWFkJCQnjtH1tbW8yYMQNnz57Fd99916R1ijp+/DjmzJnDa/3m5uZYsGABMjIyOMNiJNm3bx9+/vlnqUMaNDQ0MGDAAJw7dw5ff/11k/Ku7oyNjXH+/HmsWrVKak8oc3NzbNiwATExMTA1NVV6HrS0tLB582bcvHkTQUFBcns1aGpqwsPDA2vXrsW9e/fE5iABXs4ttXz5cri5uUn9rWqkq6uL9957D4mJiWLzaCiTpKEb8oZz9OzZEx06dFBomebYsmULYmJiMHv2bPTu3RtmZmZKmcSYkNeFgGF4vhuNEEJaCcMwuHbtGpKTk1FYWAgjIyN06tQJ/v7+KrmQU1dPnjxBbGws8vPzUVlZCWtra3h6esLJyanZaVdXVyMhIQH37t1DUVERqqqq0KZNG7Rr1w7dunVDz549VX7jp0x37txBUlISCgoK8OLFC1hYWMDOzg6+vr5quR1RUVEYMGAAG75w4QICAgLYcHZ2Nq5cuYL8/HzU19ejQ4cO8PHxkTiRqiIqKytx6dIlPHjwAEVFRdDX14elpSX69u2Lbt26NSnNe/fu4fbt23jw4AFKS0tRX1+PNm3awMbGBi4uLnB0dGxWnt80OTk5uHLlCgoLC1FcXAwtLS0YGxvDwcEBPXr0aHYZkKewsBA3b97E/fv3UVRUhOrqahgYGMDCwgJvv/02evXqpfDNVW1tLRISEpCSkoKSkhKYmpqy9ZnojeKrLiAgANHR0QAAf39/zutIa2trcenSJdy/fx8FBQUwMzODo6Mj/P395d7wK1N9fT2SkpJw584d9nXhhoaGMDc3h6OjI3r27KnQMI2KigqkpqYiKysLT548QWVlJXR0dGBmZsYOKVK3Bm9CiHqixgpCCCGklclrrCCEvJpkNVYQQgiRjYaBEEIIIYQQQgghRK1QYwUhhBBCCCGEEELUCjVWEEIIIYQQQgghRK1QYwUhhBBCCCGEEELUCjVWEEIIIYQQQgghRK1QYwUhhBBCCCGEEELUCr26lBBCCCGEEEIIIWqFelYQQgghhBBCCCFErVBjBSGEEEIIIYQQQtQKNVYQQgghhBBCCCFErVBjBSGEEEIIIYQQQtQKNVYQQgghhBBCCCFErVBjBSEqIBAI2M/q1auVnn5UVBRnHVFRUUpfx+vC3t6e3U9Tp05t7ewo3eu+feT/yc7O5pz3e/fube0sKWzq1Kls/u3t7WXGpbKtHHv37uWUm+zs7NbOUotRpLwR9bd69WpOWW5Nr0N9TMirgBorCCGEEEIIIYQQola0WjsDhBAij3DvlICAAAQEBLRaXsjrj8rbq6WkpARbt25lw2PHjkXv3r1bMUdEmSIiInD9+nUAgKmpKRYtWtTKOVJvUVFRnN6WqujdScjriM4d9USNFYQQtbdmzRpOmG4eiSpReXu1lJSUcI6Zvb09NVa8RiIiIrBv3z4AgJ2dHTVWyBEVFcU5H+iGixB+6NxRT9RYQYgKMAzT2lkg/783aXw2Ia8TOneVY+rUqTTnB3ktrF69Wm1uIO3t7elaj5AWQHNWEEIIIYQQQgghRK1QYwUhhBBCCCGEEELUCjVWEEIIIYQQQgghRK3QnBWEqJm8vDzExMQgPz8fmpqa6NixI/r27av0d8RnZmYiOTkZBQUFKCsrg7m5Oezs7ODj44M2bdoobT0JCQm4c+cOHj58CAMDA9ja2iIgIACmpqZKW4cqVVVVIT4+HtnZ2Xj69Clqa2thYmICR0dH9OnTBxYWFrzTYhgGGRkZSE9PR25uLsrKyqCrqwszMzN0794dffv2hY6Ojgq3Rnmqq6sRHR2NBw8eoLCwEKampnB3d4e7u7vM5Z48eYJLly4hOzsbdXV1sLa2RkBAAOzs7JqVn9raWsTHxyMrKwuFhYUQCARo164devXqhT59+kAgEDQr/aaor69HWloabt++jYcPH6K8vBwGBgZo27YtevXqBRcXF2hqaiptfQUFBYiNjUV+fj4qKythbW0NDw8PODk5NSvd2tpaxMXFsftWW1sblpaWcHFxQa9evZSUe/WiqvKUk5OD5ORk9vzX0NCAoaEhbGxs0LVrV/Ts2VOpZULZXvX6XFRKSgpSU1ORn58PHR0ddOjQAQEBAbC0tGxWug0NDbh69Sru3LmDwsJC1NXVoV27dnBycoKnp6daH2NZMjIycOPGDTx69Ajl5eWwt7fHxIkTpcYvLCzEzZs3cffuXRQXF6O+vh5mZmbo0KEDvLy80K5duxbM/f9TX1+PmJgYZGVl4cmTJzA2Nkbnzp3h7+8PAwODFs9P4/VYbm4uBAIBrKys4OvrC1tb2yanWVNTg4sXLyIrKwvFxcWwtLSEvb09fH19oa2trcTc81NcXIykpCRkZmaitLQU9fX1MDAwQPv27dG5c2c4Ozs3ed/n5eUhPj4eBQUFKCkpgZmZGWxsbODr6wszMzMlbwlpcQwhROkAsJ9Vq1bxWiYtLY155513GIFAwFkeACMQCJiBAwcy165dYxiGYS5cuMD5/4ULF3ito6qqitmyZQvTuXNnsXU0fnR0dJjx48cz6enpvNL09/dnl/X392f/HhYWxnTt2lXiOjQ1NZkZM2YwBQUFUtP9+OOPpeZR2ufjjz8WS8fOzk7m/6VJTExkxo4dy+jp6Uldn0AgYNzc3Jht27YxlZWVEtOpqKhgDh8+zEyYMIGxsLCQmX99fX1m5syZTFZWFu98NnX7mpr28+fPmcWLFzNmZmYSt8HZ2ZmJi4sTSys7O5sZP348o6mpKXG5d999l8nJyVE4j/fu3WOmTJnCGBkZSd2vlpaWTHBwMFNRUSE1HWWVt+LiYmbPnj3MqFGjGGNjY5nLm5iYMF988QXz+PFjXtt6//59zvJ79uxh9+17773HaGtrS1yPm5sbc/HiRYX3bX5+PjNr1iyZ+7Zjx47M999/z1RVVfFKU3g/29nZyYwrr2wL1z18P/LqY2WVJ1Hh4eGMm5ub3PwZGBgwI0aMYM6ePcs7bXn27NnDWcf9+/elxlVVfa4o0d84vh9R0srbsWPHGBcXF6n1+rhx45h79+4pnO8nT54wn332GWNubi7zvF+2bBlTVFTUnF3EIVo38P2IloVVq1ZJ3J8RERESy6+JiYlYXhITE5klS5YwPXv2lLt+Ly8v5tixY7y3U1r+JJFUf9TX1zMhISFMhw4dJOZHT0+PWbp0KVNeXi43L9LqY0XyHRMTw3h7e0vdPwMGDGCuX7/Oa980evHiBbNy5UrG1NRUav21atUqpra2lmEY6ee8siQlJTEjR45ktLS0ZJYFTU1NxsPDgwkNDeWVbn19PbN3717m7bfflpnmkCFDmISEBKnpKOvcIapDjRWEqIBwhcanseLw4cOMjo6O3MpRT0+POXDgQJMaK1JTU2U2Uoh+tLW1Zf74NhL9oauurmYmTJjAax2Ojo5Mbm6uxHRbq7GipqaGmT17tsLrbmxIErVlyxaF0zIyMmKOHj0qN69N2T5FiKadl5fHdO/eXW7+dXV1mVOnTrHpXLx4UeqFk/CnU6dOCl0AbNmyhdd50/hxcnJi7t69KzEtZZW3hQsXKpxO+/btmZiYGLnbK+ni+Pz580zbtm3lrkMgEDDBwcG8921ERARjaGio0L7lc3Onzo0VyixPjWpra5nJkycrnM85c+bI3Zd8NaexQln1uaJU2VixaNEiXmlZWloyKSkpvPMcHh7OtGnThndera2tmcTERKXsL1U2Vnz66adSlxdtrCguLm5SPqZPn85UV1fL3c7mNFYUFxczAQEBvPLTr18/prS0VKF9rmhjxQ8//CC18V74Y2hoyJw7d07uvmGYl41lvXr14rWNPj4+TElJiUobK37++WdGQ0NDobLg5OQkN93c3FzG1dWVd5oCgYBZv369xLSosUL90TAQQlrZf//9h4kTJ6Kuro79m7GxMYYPH44uXbrgxYsXSE5OxqVLl1BVVYXp06cjODhYoXXExcVh+PDheP78Ofs3MzMz+Pr6wtHREYaGhigqKkJsbCyuX78O4GU36GnTpoFhGEybNo33uubOnYvDhw8DAGxsbDBo0CB07NgRdXV1uH79Os6dO4f6+noAL7s+Tps2DWfOnBFLR1NTk+0q2xgfAAQCATQ0JE+309yutTU1NRg6dCiioqI4f7ewsMDAgQNha2sLfX19FBcX4+bNm0hMTERFRQXv9I2MjODi4gInJyeYm5vDwMAAFRUVyMrKwqVLl1BYWAgAKCsrw4QJE3Dp0iV4eno2a5uUpbq6GiNHjsTt27cBAK6urvD29oapqSkeP36MyMhI5Ofns3EnTpyIjIwMFBQUYOTIkSgrK4O2tjb8/Pzg4uICAwMD3L17F8ePH0d5eTkAIDc3F9OmTcOFCxfk5mfp0qX44YcfOH/r3r07vLy8YG1tDeDlqy/Pnj2LgoICAMCdO3fg4+OD5ORkNk4jVZQ3c3NzODs7o1u3bjAzM4Oenh6eP3+OzMxMREdHs+fjkydPMHLkSFy7dg0ODg5yt71Rbm4ulixZgmfPngEA+vbtCy8vL5iYmCA3NxcnT55kt51hGCxfvhyGhob47LPPZKb7559/IjAwkLMfjI2NMWzYMHTp0gU1NTVISUnB+fPn2Th37tyBl5cX4uPjlT5kTRppx0xDQ0PqMA1px1LZ5anRunXrsH//fs7f3Nzc4OrqCktLS2hqaqKsrAz379/H9evX1e51rcqqzxUlEAjYY9vQ0MB5RWRz6vk1a9Zg69atAF7W64MHD4a9vT0EAgHS0tJw6tQpVFdXA3g5rCowMBDJycnQ1dWVme727dvx2WefcfJpb28PHx8fdOzYEVpaWsjLy8O5c+eQm5sLAHj06BECAgIQHx+Pt99+u8nbBDR9f8kbzrRx40b89NNPAF7+fg0ePBhdu3aFpqYmsrOzcfnyZanLamho4K233sJbb70FGxsbGBkZoa6uDk+ePMHVq1dx48YNNu7u3bvRpk0bbNu2jdf2Kqqurg4TJkxgf9u7du2KgIAAWFlZ4cWLF0hISEBMTAwbPz4+HosXL8auXbtUkp99+/ZhyZIlAF7WrYMGDULXrl2ho6ODu3fvIjIyEmVlZQCAiooKfPTRR0hLS4O5ubnUNCsrKzFo0CDcvHmT/ZtAIICHhwc8PT1hbGyMR48e4b///mOHG8+cOVMl2wcAFy5cwIIFCzh/c3BwgLe3Nzp27Ag9PT2Ul5fj0aNHSE1Nxc2bN9HQ0CA33YyMDAwYMAAPHz5k/2ZoaAgfHx+89dZbMDIyQmlpKRITE3H58mUwLx/M45tvvkFNTQ3WrFnDMxwhAgAAIABJREFUSU9V5w5RotZrJyHk9QWh1ldZT/JKSkrEuiNOnTqVef78uVjc69evMz169GCAl0+uhZeR1bOioKCAsbGx4bTSb926lXnx4oXE+NHR0ZwnEvr6+szt27elpi/cKt/4VFJfX5/57bffmLq6OrH4165d4+QHgNynBnz3pySK9DwQ7VHRtm1bZteuXRK3g2Fedrf866+/GF9fX6ldNXfv3s3MmzePiYmJYbtdSlJXV8fs27ePM3SgR48eTENDg9K2T1HCaTcOMejYsSMTFRUlFvfFixdiT5C/+uorpk+fPgwAxs/PT+JT6Pz8fMbZ2Zmz3JkzZ2Tm68CBA5z4Li4uUnsmVFVVMRs2bOA8wRowYIDM9JtT3oKDg5lly5YxSUlJMo9dVVUVs2nTJs6T/GHDhslMW/QJUGM9IO2Y1NTUMP/73//ElpE1xOvBgweMiYkJZ5lZs2ZJrJMyMjIYT09PTtz+/ftLPV8YRrk9K6TtFz49woSpqjxVVlZyeqd07dpVag+sRhkZGczq1auZ1atXK7QNsjS1Z4Wq6nNFKVJmZC2rra3NCAQCRkNDg9mwYYPEoUv37t0TG74QFhYmcx3R0dGc8mBvb88cP35c4vlfV1fH/Pbbb4y+vj4bv3v37rx6FfClSO8Decs2bteCBQsk9jQQzXdJSQkzaNAg5sCBA3KHuaSkpDD9+/fnrC8+Pl5p2yZcfzSWZXNzc+bvv/+WGP/MmTOc31+BQCCz51RzelY05mfhwoUS92tBQYFYL5CVK1fK3F7RHkMODg5MbGysWLz6+npm27Zt7LAM4etJZfasGDBgAJuugYEBc/jwYZnxCwoKmN9++40ZP3681DgvXrzgXC9oa2szK1euZEpKSiTGv3HjBme4l4aGhszr5eacO0R16EgQogJ8b3aWL1/OiRsUFCQz3UePHjGdOnXiLAPIbqwQvlgzMDBgLl++LDf/OTk5jKWlJa98iXbF1tTUlHuxevHiRc4y8m6ym3PzyPeGRzRP7dq1Y9LS0nivR16jAl9xcXGcC1/h4RSStFRjBQDG1NRUZlf/qqoqxt7ennNhAIDx9PSUOadBWloap6uorO0oKSnhXFD6+Pjwmjvg119/5X1D1ZzypqjDhw9zLo5lNSRI6q5qYmIiszGRYRhm/fr1nGVGjRolNe6UKVM4cRcsWCAz7efPn4uN/f/999+lxle3xgpVlqdz585x4ly6dIl3vpSpqY0VqqrPFaWsxorGz759+2Quk5mZyfsGrq6ujjO8snv37rzm7jh58iRnfip5DSKKUGZjBQDmiy++UFreRFVVVXG68wcGBiqUP1lEf7/atGnD3Lx5U+Yyv//+O+/6vzmNFQCYNWvWyMzLs2fPOPNcySr7ubm5nOsGc3NzucMUdu/eLZYnZTVW1NTUcPKzbt06paQrvB81NDSYiIgIucuUlJRwhq/6+vrySl/Rc4eoDr26lJBWUltbi7CwMDZsbm6OH3/8UeYyVlZW2LJlC+915Obm4uDBg2x41apV6Nevn9zlOnXqxOkq98cff6CkpITXOhcsWICBAwfKjOPr64u+ffuyYVldSVvKt99+ywmHhYWhR48evJdXVpdALy8vDBs2jA0fP35cKekqw4YNG2QOU9DV1cXUqVPZcENDAwQCAcLCwmR2o+7Rowd8fX3ZsKzyEBoayg6f0NPTw/79+3nNID579mxO2Q8NDZW7TEsYP348evbsCQBgGAYnTpxQaPk1a9bIfdvHV199BRcXFzZ84sQJ5OTkiMUrKChAeHg4G7azs8P3338vM20jIyOEhYVxyr+qunKrgirL05MnTzhh4TrvVfGq1ufSjB49GlOmTJEZp2vXrhg1ahQbTkxM5AwzEnbkyBHcu3ePDe/du5fXGy6GDRuGCRMmsGF1qY9E2dvbY8OGDSpLX1dXF19//TUbjoyM5DUUoCnWrl3L1rXSTJw4EVZWVmxYVWW5d+/e+Oabb2TGMTMz4/yePnjwAI8ePZIYd9euXZwyum7dOrnD8aZNm4ZBgwbxzrMinj59ysmPMuq+yspKbN++nQ3PmTMHY8aMkbuciYkJ57r50qVLuHXrVrPzQ1oONVYQ0kouX77MuZgNCgri9fq3cePG8X6d1R9//IHa2loALy8K5syZwzt/48ePZ29A6urqOOM5Zfnkk094xfP392e/Z2ZmcubsaGklJSX477//2HDfvn05F6stTfh1kAkJCa2WD2F6enqcCydpROfY8PX1lXuBCIBz45eZmcmWW1G///47+33MmDEKvfJU+OYgKiqKMza1NTX1eBsYGGDGjBly42lqanLOy4aGBhw7dkwsnvB4fQCYP38+9PX15abv5ubGuaFNSkpi5y9Rd6osT6KvgL527VoTc9l6XsX6XJambE9lZSUePHggMZ5w+fH09FRojqEPP/yQ/Z6cnMyZU0pdTJ8+Xe58Hc0lXP89f/6cnRdJmXR0dDBr1iy58TQ1NeHt7c2G09PTlZ4X4GXdKm3+HGHC5VBWfiIjI9nvhoaGchvkGonOKaEshoaGnLAy6r7jx4+jqKiIDcube0nYkCFDOK8w5TMvFlEf1FhBSCuJj4/nhN99911eywkEAowcOZJX3OjoaPZ7r169YGJiwjt/5ubmaNu2LRtunHhTFmtrazg6OvJKX7jVn2EYlJaW8s6bskVHR3Oe5nz00UcqWc+NGzewcuVKjBw5El27doWFhQV0dXWhpaXF+YSEhLDL5OXlqSQvinJ1deX1xLljx46csI+PD6/0hZdjGEbihXthYSHnYk24NwYfwmXz6dOnKr+hjouLw9KlS/HOO+/AwcEBbdu2hY6OjtjxFu7NoMjxHjBggNgNsTSjR4/mhCU1iog+ReTz1KrRuHHjOOG4uDjey7YWVZenvn37cnqcBAUFITExsYm5bXmvan0ujeiNqCyiT6WLi4vF4tTX1yM2NpYNN6f8NDQ0ICUlRaHlW0JAQECTl3327Bl+/fVXTJ48GS4uLrC2toahoaFY/SfamK2K3zw3NzfedaXwsZd03JVBtBGCT14Ayfmprq7mTFbq7e0t1lggzeDBg1UyUaSxsTG6d+/OhteuXYvw8PBm9ZoRvp61sLDgpC+PhoYGOnfuzIb5XM8S9UFvAyGklYi2kAt305and+/evOIJt2YnJSVBS0uxU164G59wi7Y0NjY2vNMW/TEtLy+XOdO1KgnPng2A11AZRaSlpWH+/PmcH1u+VHWxpCi+x1b0uDZ1OUnlQfTpzGeffYaFCxfySh+A2JPvoqIiscYVZYiLi8O8efOadPOhyPFWpM6wtLSEtbU12404LS1NLM6dO3fY74aGhrxvVIGXjVnCVPF0VNlUXZ6sra0RGBiIP/74AwCQlZUFDw8PuLi4YOTIkfDz80O/fv0UakRuSa9qfS6NmZkZrwZXQPL2iMrKyuI0qm7evFmhYZqi+PzGtjRFbggbVVZWYu3atdiyZQtqamoUXl4Vv3lNLcuSjrsyNOd3UVReXh5nPwv3VJHHyMgI9vb2uH//Pu9l+Pr888/Z3rxVVVUIDAzEkiVLMGrUKAQEBMDb21uh4yJcXz99+lTl17NEfVBjBSGtRPgHWUNDAxYWFryXbd++Pa94whUywzBSx93ywedJGd/WfEB8jofW7JIv+sPVoUMHpaUdGxuLYcOGNfmip6qqSml5aQ6+F/mix7Wpy0kqD0+fPuWEmzu2WRVPf//66y8EBgY2uRu8Isebbz0gHL+xsULSDYHw3ywtLXl1U24kPM5bWvrqpiXKU2hoKB48eMDpaXLjxg3cuHEDwcHB0NDQgLOzMwYOHIjx48crvaG0OV7V+lwaZW/Pq1AfNRefoanCKioqMHz4cFy6dKnJ61TFb15zjr0q8M0Pn3IoWtcK94jlw9zcXCWNFbNnz0Zqaipnnom8vDyEhoayc7TY29sjICAAY8eOxfDhw6GjoyM1PdHzTdXXs0R90DAQQlqJ8M0r3xu6Rnx+6CorK5v0VEMaVU16pQ4a32feiG93UT7pfvDBB5xj/dZbbyE4OBjnz5/HvXv3UFZWhpqaGvZd4AzDYNWqVUpZ/+uG7ySvfCm7TD948ABBQUGchgpPT09s3rwZMTExyMnJQVlZGerq6jjH++OPP2bjK3KTp8gFuGh8SY1nwueBommLnjOi55Q6aonyZGJigujoaOzYsQPdunWTuMz169exefNmeHl5oW/fvk3qgUVanrrXR8qgra2tUPylS5dyGiqMjIwwb948/Pnnn0hNTcWzZ8/w4sULTv0neqOsjg1d6kx4niEAMm/4JVHlnCQ//fQTTp06BT8/P4mNQNnZ2di7dy/Gjh0LBwcH7Ny5U2payjzf1PFcI9JRzwpCWonwxX1lZaVCy1ZUVMiNo6enBw0NDbZSnjRpEvbv369YJt8QxsbGnHB5eblCPV2kCQ0NxePHj9nwwoULsXnzZrlPrFXV9fRVJ9qod+bMGQwePLiVciMuJCQEL168YMObNm3C4sWL5S7X1OPNpx6QFl9Sg5yRkVGT0xbdBuG01FVLlSctLS3MmzePHRp04cIFxMXFITY2Vmyei6SkJAwcOBA7d+7E9OnTlZ4Xojyi5Wfnzp2YOXNmK+Wm9T169Ai//fYbG+7RowfOnDkjt6s//d41j+gwMkUbilU9sevQoUMxdOhQ5Obm4ty5c4iJicHly5eRnp7OaZh6+PAhZs+ejbi4OOzZs0csHeHzzdvbm/ek7+TVRz0rCGklwjMTNzQ0iHVxk0X0lXiSaGhocNaRlZWlWAbfIKJjq5U18eK///7Lfu/SpQs2bdrEq2s9n+P7JhJtQFK3Mi18vAMCAng1VABNP94FBQUKxRdej3DdIOlvBQUFCj3hFG6Uk5a+ummN8uTs7IyFCxciPDwceXl5yMjIwKZNmzhzAzQ0NGDu3LmcV2IS9aPu9VFLi4yM5HTNDw0N5TUnAf3eNY9oOZT0WmppGIZRKH5zdOrUCVOnTsWuXbtw69YtPHnyBL///rvY61P37t0r8cGa8Ha+6efam4YaKwhpJT169OCEhWdzlodv3Lfeeov9npycTE8wpBCdkEpZrwsVnrBwyJAh0NTU5LXc1atXlbL+141weQagVt3lX7x4wZnFfvjw4byWq6+vb/Jr3RSZ0bywsJCdrwIQ35cA4OTkxH6vqKhARkYG7/STk5M54aZMzNfS1KE8OTo6YvHixbh58yZnOFBtbS327dvX4vkh/HXp0oXThV6d6qPWIDpBr5+fH6/l6PeueaysrGBtbc2Gr1y5wnvZjIyMVpu/oV27dggKCsLZs2exa9cuzv8kDQcRrq8fP36s0O8TebVRYwUhrUR0IrUTJ07wWo5hGBw/fpxXXOEW65qaGvz555/8M6hGhGd9bs6kStL4+flxejw0zt7fXMJjLPk+aU5NTX0l3qTQGjp37gwHBwc2HBkZqZILraaUN9FJzvge78jISIWHXDSKiorivew///zDCXt6eorF6d+/Pyd87Ngx3nk5evSozLRUSXRcPd9j1lLliQ9NTU389NNPnHpIkQbs15nw8VVF/d9U+vr68PLyYsMJCQlq8cS3qedDcwn/3pmYmPCeqPLIkSOqytIbQ7i+TUtLQ2pqKq/llHWt01wzZsyAm5sbG5ZU94n2wDh48KDS89Fa5w6RjRorCGklXl5enNn8/+///o/XhfLRo0d5d9sLDAzkXPyuX7++yTdGrUl4/Lsq3jJgYmKCkSNHsuGrV69yuvQ3lXC+s7OzeS2zfv36Zq/3dfbRRx+x30tLS/Htt98qfR1NKW+iczTwOd4NDQ0IDg5WKG/CKioqEBYWxms9wjOya2hoYMyYMWLxhg0bxnlSHBoaypmDQ5pr167h/PnzbNjd3V2hV9I1l+i+V6SOaInyxJeRkRGnq7MyJ0h+lam6/m+OiRMnst8bGhqwYsWKVszNS805H5S13sLCQl5zcZ08eRJJSUmqzNYbYcqUKZzwypUr5S5TWFiIH3/8UVVZUliXLl3Y75LqvlGjRnHK2LZt28SGHzZXa507RDZqrCCklWhra3MmUHv69CkWLVokc5mCggJ8/vnnvNfh5OSEDz/8kA1nZWVh8uTJCl0E19fXIyoqind8VbC3t2e/JyYmqmQdX331FSc8Y8YMhXo4SBrf37NnT/b7v//+K3dsblhYGA4fPsx7nW+ixYsXcy4oNm7ciPDwcIXSePLkCW7evCn1/00pb0ZGRujUqRMb3r9/v9wb/f/973+Ij4/nlb40q1atQmZmpsw4Gzdu5AwZGTlyJGxtbcXitWvXDoGBgWw4Ozsby5Ytk5l2eXk5ZsyYwZldfeHChXyzrxTGxsac1/UpUkeosjylp6crNNnd7du3UVhYyIaFy+GbTHg/VFRUID09vfUyI2Lq1Kmws7Njw+Hh4di4caNCaZSWlip1KIRouVHVb6Yo4d+72tpa7N27V2b8Bw8eYNasWSrO1Zth5MiRnLcNHTt2DBs2bJAav7y8HOPGjVPZzfjjx48VmgujpqaG81soqe5r27YtPvnkEzZcUlKCcePGKdwb7ty5c1L/11rnDpGNGisIaUVffvklOnTowIb37t2LmTNnSrzATU1NxcCBA5GTk6PQq6Y2b97MecoZEREBHx8fue9Bv3fvHr777jt069ZNbiOKqgl3tb1y5QrWrFnDGX+vDP3798e8efPYcGFhIby9vbFnzx6pXQGrq6sRERGBgIAApKSkiP1/7Nix7PeysjK8++67yM3NFYtXVVWFVatWsRduir428k1ibm6On3/+mQ0zDIOPPvoIixYtktkYVF9fj/Pnz2PWrFmwt7fH2bNnpcZtankTPt45OTl4//338ezZM7F4paWlmDdvHnsx2dTjrauri5KSEgwaNAgXL14U+39dXR3Wrl2L5cuXc5YJCQmRmubatWs5s8tv374dc+fOlTjfTVZWFoYMGcKZc8Pb25vT4NFShIfV/fXXXwgNDUVRUZHc5VRZnsLDw9GpUycsXLgQsbGxMicsTU1Nxbhx4zhxxo0bJzf/bwLh8xEA5syZg+TkZLXooq2trY09e/Zwho59+eWXmDRpktwJUhMSEvD555/Dzs5OqW/q8vDw4PSoXLJkCWJiYlTeU2fEiBGc12YuWbIEf//9t8S4Z86cgZ+fH/Lz8+n3Tgk0NTWxa9cuztCbb775BiNGjMDp06dRWlqK+vp65OfnY9euXejVqxdiYmLQtm1bzlxFynL79m107twZEyZMQEREhMyG+8LCQgQGBnIaN6TVfStWrICzszMbvnz5Mtzd3XH8+HGZ9eujR4/w008/wcXFBe+//77UeK117hDZ6NWlhLQiExMThIWFYdSoUairqwPw8un6n3/+iREjRsDBwQFVVVW4du0aoqOj0dDQAB0dHQQHB+OLL77gtQ4rKytERERgyJAhbCt6YmIi/Pz80KVLF/j4+MDKyoq98cnLy0NycjKnG7uLi4vSt10RM2bMwC+//MI+vV29ejVWr14NbW1tzsXR5MmT8csvvzR5PVu3bkV6ejrbk+TZs2eYPn06vvzySwwcOBC2trbQ19dHcXEx0tPTceXKFfa1X5J+KOfMmYMtW7awEy9evXoVjo6OGDZsGDtZVHZ2Nk6dOsUem+7du+Pdd9/FDz/80OTteN0FBQUhMzMT69atA/By32/btg07duyAh4cHevfuDXNzc9TU1KC4uBi3b9/GtWvXeL+iranlbdmyZdi7dy/b2Hjy5EnY29tjxIgRcHR0RE1NDTIzM3H69Gl2OJa/vz86derUpJuVFStWYOvWrcjNzYW/vz88PDzQr18/GBsbIz8/H5GRkWI33CEhIWKT+wqztbXFrl27EBgYyN4M/vrrr/jjjz8wfPhwdOnSBTU1NUhJScH58+fZegsA2rdvj/379/OeSFaZZs+ejcjISAAvGxLmz5+P+fPnQ1dXl3MjuXz5ck7jDaDa8lRaWooff/wRP/74I8zMzODq6gonJyeYmZlBU1MThYWFSEpKQmJiIqcOGTNmjFq9lrc1ubu7o3fv3mzvoEuXLsHNzQ0aGhrQ09Pj3KC1xiTSAwYMwI4dOzB37ly2zjh48CAOHTqEPn36wN3dHRYWFmhoaEBJSQnu3r2LpKQkXo1pTWFlZYXRo0cjIiICwMs5DHx9fSEQCKCvr8/ZX2lpaRJ7WTV1vfPnz8fWrVsBvJx0+P3330efPn3g5+cHExMTPH36FNHR0bh16xaAlzfZmzdvxpw5c5SShzeZr68vfvzxR3z66afs306ePImTJ09KjC8QCLBnzx5s3ryZnRxVuK5srvr6ehw5cgRHjhyBnp4enJ2d0atXL1hYWEBfXx+lpaVIS0tDdHQ0qqqq2OVsbW2xdOlSiWkaGhri2LFj8PPzYx/8ZGZmYtSoUbCxsYGfnx86duwIAwMDlJaW4tGjR7h27RoyMzPZ+lX0Va/CWuvcIbJRYwUhrWzYsGE4cOAAgoKC2Nbb0tJSiRMf6erqIiwsTOHx4H379sXVq1fxwQcfcJ6CZmVl8ZoQTJGeHKrg6uqK77//HkuXLuV0N6+trUVtbS0bFv7BawodHR38999/mDt3Luc934WFhQp3DQde/rBGRETgnXfeYRsjqqurcezYMYmTFzo5OeHUqVMS3zFOuNauXYtu3bph7ty57I1/bW0tYmNjERsbK3d5WWW6qeWtY8eOOHToEN5//332f2VlZVLLjpeXF44eParQ0C5hnTp1wpEjR/DBBx+guLgYV65ckToTvEAgwPr163kN0fjggw/w119/YeLEiey48+fPn8s8B7p168Y2zrSGMWPGYNGiReyNUqPq6mpUV1ezYWlPyFRZnhoVFxfj3LlzMrshAy/HZqti8rhX2f79+zF06FDOa6UbGhp4zYvQEhp72EyaNIkdytPQ0ICkpCReczIo+zd2x44duHPnDmfIDMMwYvtLuH5ThpCQEKSmpnLK+LVr1yS+8UhbWxthYWHw9fVVah7eZJ988glMTU2xcOFCib36GrVt2xb79+/H8OHDOfNkGRsbqyRfVVVVMn+fGjk6OuLkyZMyGxTs7e2RlJSEiRMncnq05efn85owVN651lrnDpGOhoEQogYmTJiA69evY/DgwRJn0BYIBPDz80NsbCwmTZrUpHV07twZV69exaFDh+Dp6cnp6iaJqakp3nvvPezfv7/V56wAXo4tv3btGhYtWgQPDw+Ym5tznnIri46ODnbv3o3Y2FgMHTpUbHZoYRoaGujXrx9CQ0OlPq12c3PD1atXMXr0aKmzo1tZWeGrr75CYmIiZ/wzkW3y5MnIzs7G8uXLeTXgOTo64tNPP0V8fDxnyI8kTS1vI0aMQFxcnMzX9jk4OOC7775DVFQU77eGSDNw4EAkJSVh1KhRUp+Kubq6IioqSqxHgSxjxoxBRkYGZs6ciTZt2kiNZ2Njg5CQENy4cQOdO3dWOP/KtGXLFsTExGD27Nno3bs3zMzMZJ6/opRdnmbNmoXNmzdj0KBBcru6CwQCeHl54fDhw/jnn39gYGDAO99vgp49e+LmzZvYunUrhg0bhk6dOsHAwID3GydawjvvvIOsrCyEhIRwJguUxtbWFjNmzMDZs2fx3XffKTUv1tbWSE5Oxq5duzBmzBg4ODigTZs2Kt9fOjo6OHXqFNasWQNTU1OJcbS1tTF69GhcuXIFQUFBKs3Pm2jy5Mm4ffs2QkJC0L9/f7Rv3x46Ojro2LEjfHx8sGXLFmRkZLCv13769Cm7rKxGAkW4u7tj3759GD9+PCwtLeXG79q1K0JCQpCSksLr3GnXrh3OnDmDkydPYtCgQXLreQMDAwwdOhS//vqr3Feetta5Q6QTMLIG+RBCWlxeXh4uXryIhw8fQlNTEzY2NnB3d+e8Yk8ZSkpKEBcXh4cPH6KoqAgNDQ0wMjKCjY0NnJyc4OTk1CrdudVNeXk5YmJikJubi6KiIggEApiamqJr165wdXVV6GYzPz8fly5dQl5eHhoaGmBlZQUHBwf079+f9rUS3LlzB9evX8fTp09RUlICXV1dmJiYoHPnzujZsyesrKxaND9ZWVmIjY3F48ePoaGhAWtra3Tr1g3u7u4qWd+TJ08QGxuL/Px8VFZWwtraGp6ens0ek1xTU4O4uDhkZWWhsLAQ2trasLS0hIuLC2f88OtGmeWprq4OaWlpyMzMRH5+PsrLyyEQCGBiYgIHBwf06dOnxcsnUa2cnBxcuXIFhYWFKC4uhpaWFoyNjeHg4IAePXq89l3IX7x4gdjYWHaiWXNzc3To0AHe3t6cCXFJ6ykpKUHbtm3ZIRLBwcH4+uuvlb6ee/fu4fbt23jw4AE7f0abNm1gY2MDFxcXODo6Niv9iooKXL58GTk5OSgqKkJtbS3atGkDKysrODk5oUePHip5uEVaBjVWEEIIIYQQQsgbZM+ePZy30p05c4bmyiFqhxorCCGEEEIIIeQNUV5ejj59+uDu3bsAXg79zc/PpyFoRO3QnBWEEEIIIYQQ8go7f/48Zw4KaUpLS/HBBx+wDRUAMHXqVGqoIGqJelYQQgghhBBCyCts6tSpOHLkCEaPHo3Ro0fDzc0NHTt2hL6+PsrKypCRkYH//vsP27dvx+PHj9nlOnTogJSUFJibm7di7gmRjF5dSgghhBBCCCGvuMrKShw6dAiHDh3iFd/ExATh4eHUUEHUFg0DIYQQQgghhJBXmKLDOLy9vXH58mX4+PioKEeENB8NAyGEEEIIIYSQV9yVK1dw+vRpJCQkIDMzE48fP0ZFRQW0tLRgZmYGW1tb+Pj4YPTo0fDz82vt7BIiFzVWEEIIIYQQQgghRK3QMBBCCCGEEEIIIYSoFWqsIIQQQgghhBBCiFqhxgpCCCGEEEIIIYSoFWqsIIQQQgghhBBCiFqhxgpCCCGEEEIIIYSoFWqsIIQQQgghhBBCiFqhxgpCCCGEEEIIIYSoFWqsIITAGw8KAAAAHUlEQVQQQgghhBBCiFqhxgpCCCGEEEIIIYSolf8P802LqQuIHT8AAAAASUVORK5CYII=\n", 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" ] @@ -455,8 +486,6 @@ } ], "source": [ - "from sklearn.metrics import matthews_corrcoef\n", - "\n", "sub_bins = list(range(11))\n", "MCCs = []\n", "for k in sub_bins:\n", @@ -504,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -520,7 +549,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -571,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -587,7 +616,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -632,7 +661,7 @@ "plt.ylim((-0.2,1))\n", "plt.xlim((-0.5, 5))\n", "\n", - "\n", + "plt.plot([-0.49, 4.9], [0,0], color='grey', linestyle='dashed')\n", "plt.show()" ] }, @@ -652,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -661,7 +690,7 @@ "df_test = df_test.loc[df_test[\"type\"] != \"engqvist\"]\n", "df_test = df_test.loc[df_test[\"GNN rep\"] != \"\"]\n", "df_test.reset_index(inplace = True, drop = True)\n", - "df_test[\"pred\"] = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_ECFP.npy\"))\n", + "df_test[\"pred\"] = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_GNN_pretrained.npy\"))\n", "\n", "\n", "df_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"df_train_with_ESM1b_ts.pkl\"))\n", @@ -679,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -709,10 +738,14 @@ " Binding\n", " type\n", " substrate ID\n", - " ECFP\n", " ESM1b\n", " ESM1b_ts\n", " GNN rep\n", + " GNN rep (pretrained)\n", + " ECFP\n", + " ECFP_512\n", + " ECFP_2048\n", + " ESM1b_ts_mean\n", " pred\n", " identity\n", " \n", @@ -726,11 +759,15 @@ " 1\n", " NaN\n", " CHEBI:57502\n", - " 0000000000000000000000100000000000000000000000...\n", " [0.11523847, 0.24259076, 0.009030506, 0.061604...\n", " [0.6218108, -0.20345737, -0.3734264, 0.6232384...\n", " [974.31903, 0.0, 0.0, 42.436066, 61.837727, 40...\n", - " 0.933617\n", + " [0.0, 10.598852, 0.0, 0.0, 389.57013, 258.7587...\n", + " 0000000000000000000000100000000000000000000000...\n", + " 0000000000000000000000100000000010000000000000...\n", + " 0000000000000000000000100000000000000000000000...\n", + " [0.34989148, -0.43482074, -0.3994056, 0.194596...\n", + " 0.927866\n", " <40%\n", " \n", " \n", @@ -741,11 +778,15 @@ " 1\n", " NaN\n", " C00019\n", - " 0100100001000000000000000000000001000000000000...\n", " [0.015422373, 0.13806325, -0.012064625, 0.0822...\n", " [-0.57177407, 0.09689727, 0.40122306, 0.331725...\n", " [1145.3693, 1.9239175, 1.1587791, 80.32902, 10...\n", - " 0.997937\n", + " [0.0, 0.0, 0.0, 1.9413376, 449.93817, 124.3162...\n", + " 0100100001000000000000000000000001000000000000...\n", + " 0100100101000000000000000000000001001000000000...\n", + " 0100000000000000000000000000000000000000000000...\n", + " [-1.399044, 0.6063495, -0.7045651, 0.17933112,...\n", + " 0.995081\n", " 60-80%\n", " \n", " \n", @@ -756,11 +797,15 @@ " 1\n", " NaN\n", " CHEBI:73183\n", - " 0000000000000000000000100000000000000000000100...\n", " [0.08684448, 0.22349858, 0.036589667, 0.031470...\n", " [0.04175865, 0.15151072, 0.4762207, 1.3612334,...\n", " [2153.5093, 0.0, 0.0, 102.47676, 134.5563, 110...\n", - " 0.080586\n", + " [0.0, 129.54646, 0.0, 0.0, 1335.022, 238.67178...\n", + " 0000000000000000000000100000000000000000000100...\n", + " 0000000100000000000000100000000000000000000100...\n", + " 0000000000000000000000000000000000000000000100...\n", + " [-0.00088821945, -0.08815515, -0.09321381, 0.1...\n", + " 0.120230\n", " 40-60%\n", " \n", " \n", @@ -771,11 +816,15 @@ " 1\n", " NaN\n", " CHEBI:17587\n", - " 0100000000000000001000000000000000000010000000...\n", " [-0.09344004, 0.19097549, 0.1482197, 0.0432938...\n", " [0.6215488, 0.8391693, -0.7469239, 1.3165869, ...\n", " [451.4077, 40.089767, 0.0, 0.0, 23.260206, 51....\n", - " 0.126268\n", + " [0.0, 0.0, 0.0, 0.0, 401.96918, 7.882661, 4.07...\n", + " 0100000000000000001000000000000000000010000000...\n", + " 0100000000000000001000000000000000000010000000...\n", + " 0100000000000000000000000000000000000010000000...\n", + " [0.36287606, -0.23329155, 0.10717201, 0.432019...\n", + " 0.381393\n", " 40-60%\n", " \n", " \n", @@ -786,11 +835,15 @@ " 1\n", " NaN\n", " CHEBI:58885\n", - " 0000000000000100000000000000000000000000000000...\n", " [0.035788048, 0.16635701, 0.08017232, -0.06425...\n", " [0.3878585, 0.30874068, 0.47543985, 0.09281093...\n", " [1756.9208, 103.18562, 0.0, 37.571777, 76.9637...\n", - " 0.502568\n", + " [0.0, 0.0, 0.0, 0.0, 848.6748, 156.55234, 7.12...\n", + " 0000000000000100000000000000000000000000000000...\n", + " 0000100000000100000000000100010000000000000000...\n", + " 0000000000000000000000000000000000000000000000...\n", + " [0.025218574, -0.067192145, -0.1081457, 0.2064...\n", + " 0.855245\n", " 60-80%\n", " \n", " \n", @@ -805,13 +858,6 @@ "3 P51635 CHEBI:17587 exp 1 NaN CHEBI:17587 \n", "4 Q19905 CHEBI:58885 exp 1 NaN CHEBI:58885 \n", "\n", - " ECFP \\\n", - "0 0000000000000000000000100000000000000000000000... \n", - "1 0100100001000000000000000000000001000000000000... \n", - "2 0000000000000000000000100000000000000000000100... \n", - "3 0100000000000000001000000000000000000010000000... \n", - "4 0000000000000100000000000000000000000000000000... \n", - "\n", " ESM1b \\\n", "0 [0.11523847, 0.24259076, 0.009030506, 0.061604... \n", "1 [0.015422373, 0.13806325, -0.012064625, 0.0822... \n", @@ -826,15 +872,50 @@ "3 [0.6215488, 0.8391693, -0.7469239, 1.3165869, ... \n", "4 [0.3878585, 0.30874068, 0.47543985, 0.09281093... \n", "\n", - " GNN rep pred identity \n", - "0 [974.31903, 0.0, 0.0, 42.436066, 61.837727, 40... 0.933617 <40% \n", - "1 [1145.3693, 1.9239175, 1.1587791, 80.32902, 10... 0.997937 60-80% \n", - "2 [2153.5093, 0.0, 0.0, 102.47676, 134.5563, 110... 0.080586 40-60% \n", - "3 [451.4077, 40.089767, 0.0, 0.0, 23.260206, 51.... 0.126268 40-60% \n", - "4 [1756.9208, 103.18562, 0.0, 37.571777, 76.9637... 0.502568 60-80% " + " GNN rep \\\n", + "0 [974.31903, 0.0, 0.0, 42.436066, 61.837727, 40... \n", + "1 [1145.3693, 1.9239175, 1.1587791, 80.32902, 10... \n", + "2 [2153.5093, 0.0, 0.0, 102.47676, 134.5563, 110... \n", + "3 [451.4077, 40.089767, 0.0, 0.0, 23.260206, 51.... \n", + "4 [1756.9208, 103.18562, 0.0, 37.571777, 76.9637... \n", + "\n", + " GNN rep (pretrained) \\\n", + "0 [0.0, 10.598852, 0.0, 0.0, 389.57013, 258.7587... \n", + "1 [0.0, 0.0, 0.0, 1.9413376, 449.93817, 124.3162... \n", + "2 [0.0, 129.54646, 0.0, 0.0, 1335.022, 238.67178... \n", + "3 [0.0, 0.0, 0.0, 0.0, 401.96918, 7.882661, 4.07... \n", + "4 [0.0, 0.0, 0.0, 0.0, 848.6748, 156.55234, 7.12... \n", + "\n", + " ECFP \\\n", + "0 0000000000000000000000100000000000000000000000... \n", + "1 0100100001000000000000000000000001000000000000... \n", + "2 0000000000000000000000100000000000000000000100... \n", + "3 0100000000000000001000000000000000000010000000... \n", + "4 0000000000000100000000000000000000000000000000... \n", + "\n", + " ECFP_512 \\\n", + "0 0000000000000000000000100000000010000000000000... \n", + "1 0100100101000000000000000000000001001000000000... \n", + "2 0000000100000000000000100000000000000000000100... \n", + "3 0100000000000000001000000000000000000010000000... \n", + "4 0000100000000100000000000100010000000000000000... \n", + "\n", + " ECFP_2048 \\\n", + "0 0000000000000000000000100000000000000000000000... \n", + "1 0100000000000000000000000000000000000000000000... \n", + "2 0000000000000000000000000000000000000000000100... \n", + "3 0100000000000000000000000000000000000010000000... \n", + "4 0000000000000000000000000000000000000000000000... \n", + "\n", + " ESM1b_ts_mean pred identity \n", + "0 [0.34989148, -0.43482074, -0.3994056, 0.194596... 0.927866 <40% \n", + "1 [-1.399044, 0.6063495, -0.7045651, 0.17933112,... 0.995081 60-80% \n", + "2 [-0.00088821945, -0.08815515, -0.09321381, 0.1... 0.120230 40-60% \n", + "3 [0.36287606, -0.23329155, 0.10717201, 0.432019... 0.381393 40-60% \n", + "4 [0.025218574, -0.067192145, -0.1081457, 0.2064... 0.855245 60-80% " ] }, - "execution_count": 3, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -862,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -876,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": { "scrolled": false }, @@ -885,17 +966,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "<40% 0.877164056059357\n", - "no sub <40% 0.6605166051660517\n", - "40-60% 0.9236417033773862\n", - "no sub 40-60% 0.8089171974522293\n", - "60-80% 0.9398614609571788\n", - "no sub 60-80% 0.8367346938775511\n" + "<40% 0.88836143394991\n", + "no sub <40% 0.7084870848708487\n", + "40-60% 0.936026936026936\n", + "no sub 40-60% 0.8322981366459627\n", + "60-80% 0.9517543859649122\n", + "no sub 60-80% 0.8979591836734694\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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qj5G+IzG572TRNQXVXc6/jC9OfYGY1BikFqZCXaGGq60rAj0DEd4rHBP9J9a5hjJtGb49+y22JW9DYnYiClQFcJA5wLeNL8Z0GYN/9fsXnGycDMYZ+8t/fQZ3HIzYybENGkNED7YxXcdgw9gNOJx+GGeyziC3NBf5qnxU6irhIHOAt5M3Hmn3CMb5jcNI35FGA0JXO1ecmHYCPyb+iE0XNuFM1hnkl+XDQeaAri5d8WTXJzEnaA4cZY5G17BsyDLsvLwT8ZnxyC3NRaG6EHbWdvBy9EKoTyimBUxDT7eeTf2tqBOP7JIIj+wSEZE5TIqahMjzkfX2a+/QHjtf2Im+Hn1F9Z+e/BRvRr+J8sryWscO7TQUUc9EwUHmYNB2IecCxv88Him3Umod7yJ3wbZnt2FQx0GiegakRET3Do/sEhERUZOSQAJnG2fILGUGbZnFmXh2y7OiTJTfn/ser+55VRSMSiAx2En9/ervGPfzOIMslrmluRj6/VCDYNTe2h4S3NmByFflIywyDMl5yaJ+1pbWtSbyuP1VU2fnziZ8J4iIqCYe2SUiIiKzC/MNQ5hvGAZ1GARPR09YWVT9ypGYnYiXdr6EY9ePCX0v5V9CUl4S/F39UaGrEC5jv+3ZHs9i3ZPr4ChzxLHrxzD6p9HCc1AxqTH4/tz3+Efffwj9lx1ZhuzSbKHc3qE9djy/Aw+3exi3VLcwKWoSdqfsBlB1rPelnS/hwD8OCP0Heg1E1tysWj/bqcxTCPxa/ExXRP+Ihn6LiIgI3CElIiKiJjCp9yRM6j0JHZ06CsEoAPR2743X+79u0P/2XXonb5xETmmOqO2zkZ8Jz0cN9BqIGQEzRO1r4teIytsvbReVX+v/mnDJfBt5G3w0/CNRe2xaLP7I+cPkz7bi2ApROcw3DL3ce5k8noiI7mBASkRERPfM+ezz+OTEJ6I6O6kdurp0BQCkK9MN2lztXEV1NTNJns48jQLVncvla87h7eRd53gAiL4abdL60wrTsPXiVlHd3AFzTRpLRESGeGSXiIiImszGMxsxf/986PQ6KDVKgyRFEkiwfOhy4flQO2s7UXupthR5ZXloa9tWqEsrTBP10UOPxOxEDPYeXDWH1E70PjX71ywDwLnscyZ9nk+Of4JKfaVQftjjYQzpNMSksUREZIg7pERERNRkSrWlyC7NRm5ZrkEwGuIdgr2T9mJO0Byhrp9nP1hIxL+evLrnVRRrigFUHeldd3qdwfvkleUJrwd4DRC1rT65GonZiQCAAlUB5u4z3NGsPr42hepCbDi7QVQ3dyB3R4mIGoM7pERERNQs4jLiEJUUhYFeA4WdUXd7d0zuM1kU+P10/idsurAJtlJblJSXGJ1LU6kRXr/92NvYfXk39KjKvnuj6Ab6fNkHDtYOKCkvEepF4ys0BnU1fXnqS9H7d1B0wDM9njHtwxIRkVHcISUiIqImMydoDvSL9Sh/pxzpEenYMHYDOig6AKjaPf3y9JcY8v0Q6PQ6YcyaUWswvPNw0Tw6vU4UDFpKLEXtzjbOwuvgjsFYO3qtKJkSABSXFwvBaM02Z7kz6lJeWY7P4j4T1UX0izCYh4iIGoYBKRERETU5qaUUXgovTHl4Cna+sFPUdjLjJLZc3CKU5VI59oTvwY/jf8TwzsPhIneBlYUV3OzcMNF/Ig5POQwbKxvRHDUTF816dBYSZiRg6sNT0cm5E2SWMthKbdHPsx/WjFyDyX0mi8crxONr+un8T8gszhTKTjZOmBYwzfRvABHdV1RaFWLTYrHi6Aq8E/MOVhxdgdi0WKi0quZe2gOHf9YjIiKie6qnW0842zijQH0nM258Rrzo+KtEIkF473CE9w43GH/s+jGUakuFsovcBX5t/Qz69XLvhfVj1xtdQ7c13UTlxzs8XueaPz7+sag885GZcJA51DmGiO4/hepCLDm4BOvPrEeRpsig3VHmiOkB07EoeBEUNopmWOGDhzukREREZFZKtRJ6veFzmrddK7wmCkYB8TOgdanUVWLRgUWiur/3/jskEonJ69t4ZiMu5V8Syu527gZHhKvbm7IX53POC2VrS2u80u8Vk9+PiO4PMakx6P55d3x84mOjwSgAFGmK8NHxj+C53BMzl89EbGwsVCrumjYGA1IiIiIyq23J29Dzi574+PjHSMpNEoLTCl0F9l/djzH/G2Mw5pF2jwivz2adxcztM3Eg9YDwS6Fer8fpzNMY/dNoxKTGCH0dZY54Y+AbBvNN/7/p2HhmIzKLM4X3zynNwdJDSzFzx0xR34WDFkJmJav186w8vlJUfqHXC2jv0L6+bwMR3UdiUmMwKnIUskqyTOpfKinFutJ1CPlnCDw8PDB37lwolcomXuWDSaKv60+Y1OqoVCpERERg1apVkMvlzb0cIiK6D3179ltM+W2KUJZaSOEgc0ChulCUvOi2ri5dcWbmGdhKbQEAR9KPYNDGQUK7QqZAeWU5VBXiXQhLiSU2P70ZE7pPMJjT91NfXCm4AqBqR9NWaotCdaFBv/F+47H1ma217rCeyzqHvl/1FcoSSHD+pfPo4dajrm8BEd1HCtWF6P55d5ODUZFiAGsAaAAPDw9ERkYiNDTU3Et8oHGHlIiIiJqUVqfFLdUto8FoYPtA7Ju0TwhGjVFqlAbBqKutK/7v+f8zGozWVF5ZbhCMWkgsMCdwDjY/vbnO4741d0fDfMMYjBI9YD449MHdBaMA4ABgcNXLrKwsjBo1CjExMXUOITEmNSIiIiKzGtN1DDaM3YDD6YdxJusMcktzka/KR6WuEg4yB3g7eeORdo9gnN84jPQdaRAQ+rv6Y9mQZTh47SD+zPsT+ap8qLQquNi6oIdrD4zuMhrTAqbVmVRo2ZBl2Hl5J+Iz45FbmotCdSHsrO3g5eiFUJ9QTAuYhp5uPev8HBlFGfj5ws+iurkD5979N4aIWhyVVoWvE75u3CQBAGIAVAAajQbh4eFITk6GQsGkR6bgkV0S4ZFdIiIiImotYtNiEfJdSOMn2gjg2p3iG2+8gZUrV9bane7gkV0iIiIiImqV4jPizTORp7i4fv16Zt81EQNSIiIiIiJqlZQaM2XGtakxr1KJuLg488z9gGNASkRERERErZJCZqbnPNWGVQxITcOAlIiIiIiIWqVAz0DzTJRhWMV7SU3DgJSIiIiIiFqlfp794ChzbNwkahgNSJll1zQMSImIiIiIqFWSS+WYHjC9cZMkAKgwrA4KCmrcvK0EA1IiIiIiImq1FgUvgoe9x90NLgZw0LBaoVAwIDURA1IiIiIiImq1FDYKRE6IhMxS1rCBFQCiAGgMm6ZNmwa5XG6O5T3wGJASEREREVGrFuoTil3hu0zfKS0GEAkg1bDJw8MDixYtMufyHmhWzb0AIiIiajlUWhVOZpxEfEY8lBolFDIFAj0D0c+zH+RS/rWfiB5coT6hSJqdhA8OfYCvE75GkabIsJMaVc+MHoTRnVGZTIbIyEgmNGoABqRERESEQnUhlhxcgvVn1hv9JcxR5ojpAdOxKHgRFDb8RYuIHkxONk5YOXwlloQsQVxGHH469BMit0SiNL+0KpNuBowmMAKqdkYjIyMRGhp6L5d83+ORXSIiolYuJjUG3T/vjo9PfGx8RwBAkaYIHx3/CH6f+yEmNeYer5CI6N6SS+UY7D0YX734FW58fwNvDHgDjgWORoNRhUKBN954A8nJyQbB6NGjR7FgwQIMHz4cvr6+cHFxgVQqhZ2dHXx8fDB69GisWbMGJSUlRtexb98+LF26FOPGjUP79u0hkUhEX7GxsfV+lrKyMqxduxbDhg2Du7s7rK2t4eLign79+mHJkiUoLCysdeyVK1ewYcMGTJ48GYGBgejYsSPkcjlsbW3h4+ODp59+Gr/++mu9a6iLRK/X6xs1Az1QVCoVIiIisGrVKj6ITUTUCsSkxmBU5ChoKo2cPauFzFKGXeG7EOrDXQAiaj1UKhXi4uIQFxcHpVIpZNINCgqq9ffmSZMmITIyst6527dvj507d6Jv376ieolEUue4AwcO4Iknnqi1/cKFCxg/fjxSUlJq7ePi4oJt27Zh0KBBBm329vYoLS2te/EAhg0bhqioKNjb29fbtyYe2SUiImqlCtWFCI8Kb1AwCgCaSg3Co8KRPDuZx3eJqNWQy+UYPHgwBg8efFfjJRIJnJycUFZWBo1G/N/dzMxMPPvss0hOTq43CDVVbm4uhg4diuzsbFH97SDz9r5kfn4+wsLCcPr0afj5+dU5p729PTQaDbRarag+Ojoa//znP7F58+YGr5NHdomIiFqpDw59gKySrLsam1WShcUxi828IiKiB0tYWBh++OEHpKWloby8HLdu3YJarca5c+cwcOBAUd9Lly4hKSlJVBcSEoK33noLv/zyC9LS0hr03suWLRMFo+3bt0dCQgKKi4uRl5eHkSNHCm1lZWV46aWXjM7TpUsXrFq1CteuXUNxcTHKysqwbds2ODs7i/r98ssvuHbtWoPWCHCHlIiIqFVSaVX4OuHrRs2x+vBq6GP0+PeifzOjJBGREZMmTTJa37t3b7z++us4duyYqF6tVovKMTF3/8z+9u3bReXXXnsNDz/8MACgTZs2+Oijj7B7926hPTY2Fn/88Qd69Ogh1H3++eeYNGkSLC0thTorKyuMGzcOeXl5mD59uug9EhIS0LFjxwatkzukRERErdDJjJO1JjAymQ3w6ZZP4efn16hfmoiIWpvz58/jk08+EdXZ2dmha9euZnuP9PR0Udnb21tU9vHxMRgTHR0tKv/jH/8QBaPVDRgwoHEL/AsDUiIiolYoPiPePBN5AllZWRg1ahSDUiKiWmzcuBEeHh5wc3ODTCZD7969cfToUaFdIpFg+fLld5UUqDZ2dnaics0jv8aOAJ87d87k+a9cuWJQ17t3b5PH38aAlIiIqBVSapTmmcim6n80Gg3Cw8OhVJppXiKiB0hpaSmys7ORm5uL8vJyUVtISAj27t2LOXPmmPU9a+5grl69GomJiQCAgoICzJ0712BMXl6eSXNrtVosWbJEVDdq1Ch07ty5wetkQEpERNQKKWRmeuaz2uNOWVlZBr+gEBFR3eLi4hAVFWXS9SoN8fbbb4sy9t64cQN9+vSBo6MjXFxcsHPnToMxNbP/GlNeXo7nnnsOp06dEurc3d3xxRdf3NU6GZASERG1QoGegeaZKENcXL9+PVQqlXnmJiJ6QMyZMwd6vR7l5eVIT0/Hhg0b0KFDBwBVu6dffvklhgwZAp1OZ7b3DA4Oxtq1a2FlJc5jW1xcLFz5UrOtZubcmpRKJUaMGIGoqCihztXVFfv27RM+T0MxICUiImqF+nn2g6PMsXGTqGEQkCqVSsTFxTVuXiKiB5RUKoWXlxemTJlisEN58uRJbNmyxazvN2vWLCQkJGDq1Kno1KkTZDIZbG1t0a9fP6xZswaTJ08W9a+Z+Ki6Gzdu4PHHH0dsbKxQ5+PjgyNHjtzVs6O3MSAlIiJqheRSOaYHTK+/Y10SAFQYVjMgJSKqX8+ePQ12JOPjzZRwrppevXph/fr1uHLlCtRqNUpLS3HixAnMnj0bhw4dEvV9/PHHjc5x/vx5DBgwABcuXBDqAgMDceLEiUZnBmZASkRE1EotCl4ED3uPuxtcDOCg8SYmNiIiqvpv4e2jscZcu3YNBQUFojpTnuE0l40bN+LSpUtC2d3dHcOHDzfod+DAAQwaNAg3btwQ6saOHYvY2Fi4ubk1eh0MSImIiFophY0CkRMiIbOUNWxgBYAoALX83qRQmClhEhHRfWzbtm3o2bMnPv74YyQlJQnBaUVFBfbv348xY8YYjHnkkUfMuobp06dj48aNyMzMFN4/JycHS5cuxcyZM0V9Fy5cCJlM/P8HP/30E8LCwkR/aJwzZw62bdsGW1tbs6xRoq8rbKdWR6VSISIiAqtWrYJcLm/u5RAR0T0QkxqD8KhwZJVk1d+5GFXBaGrtXWJjYzF48GBzLY+I6L707bffYsqUKUJZKpXCwcEBhYWFRpMXde3aFWfOnBEFeoGBgbh+/bpQzs7OFo1xdnaGtbW1UI6Pj4eXl5dQ9vX1Fe4Ltba2hq2tLQoLCw3ee/z48di6dasoK7sSe2IAACAASURBVC8AWFhYGOzyuru71/qZ586da/Q6mbpY1d+FiIiIHmShPqFImp2E92Lew+ojqwFjG6ZqVD0zehC17owCVbujQUFBTbNQIqL7mFarxa1bt4y2BQYG4pdffjHYdczNzTUIQqureeS3srKy1r7l5eUGd6BaWFjg5ZdfxieffGIQjAIweuS4rvWUlJTU2lYbBqREREQEJxsnrBq1CvoYPT796VPAE4AN7mTSzYDRBEY1TZs2jSdsiIgAjBkzBhs2bMDhw4dx5swZ5ObmIj8/H5WVlXBwcIC3tzceeeQRjBs3DiNHjjQaEDbWsmXLsHPnTsTHxyM3NxeFhYWws7ODl5cXQkNDMW3aNPTs2dPs79sQPLJLIjyyS0TUuimVSvj5+SEry4TjuzV4eHggOTmZz5ASEZHJmNSIiIiIBAqFApGRkQaJLeojk8kQGRnJYJSIiBqEASkRERGJhIaGYteuXfDwMO1KGA8PD+zatQuhoaF19rv9jFL1r7ouYd+3bx+WLl2KcePGoX379gZjq1/ObswTTzxhMKa2ryNHjhidQ6fT4eeff8bEiRPRuXNn2NnZwcrKCo6OjvD398eLL76IPXv21PctIiKiWvAZUiIiIjIQGhqKpKQkfPDBB/j6669RVFRk0EehUGDatGlYtGhRvTujKSkpeOeddxq0hhEjRjSov7kplUqMHj0aR48eNWgrLi5GUlISkpKS8MMPP+Cpp57Czz//DEtLy2ZYKRHR/YsBKRERERnl5OSElStXYsmSJYiLi0NcXByUSqWQSTcoKMikfAN6vR7//Oc/UVZWdg9WbZxUKkWbNm1qba9+bcJt8+bNMwhGLSws4ODgILqTDwC2bt2KL7/8ErNnzzbPgomIWgkGpERERFQnuVyOwYMH3/Xdop999hkOHz4MoOpZU42mjntjqgkJCUFgYKDwVdfx3voMHDiw3iO+NUVFRYnKEyZMwIYNG6BQKJCamooRI0bg8uXLQvvevXsZkBIRNRCfISUiIqImc/XqVSxYsAAAYGdnhzfffNPksTExMfjwww8xceJEdOzYsamWWKvS0lJRecqUKcLRZB8fHzz55JOidmanJyJqOAakRERE1CT0ej2mTZsmBHbLli1D586dm2UtSUlJeOyxx9CxY0f4+vpi8ODBmD9/vmiHs6aAgABR+bvvvkNxcTEA4Nq1a9ixY4eoffz48eZfOBHRA45HdomawHux7+H9g++b1HdJyBK8E2yY6ONo+lF8e/ZbnMw4iWvKaygtL4XMSgY3Ozf0dOuJcd3GYVLvSZBZGV7NsOnCJhxNP4rTN08jszgTt1S3UKYtg63UFg85PoS+Hn0xofsETOg+ARYS43+X0ul1+OWPX/DLxV9wJusMskqyoKnQCHM82v5RvNDrBYT5hjXsm0NErcYXX3yBAwcOAACCg4MxZ84cfPfdd82ylpycHOTk5AjlK1eu4NChQ1ixYgUWL16MRYsWGYxZtmwZhg8fLjz7umXLFkRFRcHR0RGFhYVCP0tLSyxcuBDPPfdc038QIqIHDANSohboX7v+hTXxawzqy7RlSCtMQ1phGnZc2oGPT3yM6L9Ho71De1G/ufvmIqM4w2B8cXkxkvKSkJSXhP9d+B8Geg3Ezhd2wsnGSdRPqVZi9E+jcfS6kcyS1eb4IfEHPNX9Kfw88WdYWjCzJBHdkZaWhrfffhsAYGtriw0bNkAikTTzqgxVVlbi3XffhZ2dHV5//XVR22OPPYbjx4/jvffew2+//QadTgedTicKRl1dXbF582Y88cQT93jlREQPBh7ZJboH3O3ca/2yt7YX9f35ws9Gg1GFTGGwm3kx9yJe3fNqne9tZWGFNvI2sJQYBozHrh/DW9FvGdTP+32eQTBqIbGAQmZ4rcPWpK348tSXda6BiFqfadOmoaSkBACwfPnyZjmq26ZNG8yaNQs7d+7EzZs3odFokJKSgvfee88gq+57771nkDkXqAqsMzMzodPpjL5Hbm4uQkND8dZbb9Xah4iIascdUqJ7IGtulsl9o5LFWR27tOmCPZP2oJNzJxRpijDltymISrrTZ2/KXoM5ZgfORi/3Xujn2Q9tbdtCIpGgUleJ6KvRmPLbFGSV3FnPr8m/Yt2T6+pcw4TuE7Bh7AYobBRILUjFiB9H4PKtapklr+zF7CBmliSiKl999RX2798P4M5R3eawdetWg13Zzp07Y/HixbC3t8fcuXOF+uLiYsTExIieA129ejUiIiKEckhICNasWQNvb2+cOnUKkydPRmpqKvR6PVasWAFXV9cGJW0iIiLukBK1OKXl4qyOY7uNRSfnTgAAR5kjJveZLGqXSw2zOs4fNB9juo6Bq52r8MuYpYUlwnzDMLH7RFFfdYW63jVM6TsFCpu/Mks6++DJrjUySxpZAxG1XreDsuY+qlvX+z7//PMGdVevXhVe5+TkYP78+aK5/ve//8Hf3x+2trYIDg7GihUrRONXrlxphlUTEbUuDEiJ7oERP46A76e+8F7ljcCvAzFz+0wcTDtotG9AO3FWx+2XtiOtMA0AUKwpxveJ34vax/uZltWxQleBfVf2YWvS1jrfz1jdd+e+Q7Hmr8yShdew43KNzJImroGIWofbmWjLysrg6+sLiUQifE2ZMkXU99q1a0JbQ+8JbQypVGpQV/3I7alTp6BSqYSyq6sr3N3dRf39/f1F5ZycHOTn55t5pUREDzYe2SW6B/Zd2Se8vqa8hlOZp7AuYR3G+43H9+O/Fz1HGtE/Aj8m/ojUwlQAwKX8S/BZ7QMnGycUa4pRqa8U+v6t29/w32H/rfV9p//fdGy/tB1anRZKtVI0FgDsre2xfOhyg3HLhizD8B+Ho0z7V2bJi1sQlRQFR5kjCtXVMktKLLFw0EI815OZJYmoZTl9+jQ8PDzg6elptP23334zqOvUqZPwuuYdpPn5+SgqKoKjo6NQl5qaajCHlRV/tSIiagjukBI1o23J2/DC1hdEdW3kbRA/PR5zAueIAtVCdaEQUFpZWGHViFX49blf4ShzRG0K1AXILs3GLdUtUTAqgQRT+k7Bqemn0P+h/gbjHuvwGI5PPY7xfuOFREo6vU4UjLrauuL3F3/H+yGmXW9DRK2Hu7t7rV/VAzoAsLCwENpqJhpqjMOHD8PX1xdTp05FbGwsNBoNgKpAc/369QYZdR0cHDBs2DChXHP3s7KyEjNmzEBBQQEAICUlBQsWLBD16dSpExQKw+RvRERUO/4Zj6gJWFtaV93z6TcBA7wG4CHHh5Bflo/fr/6Ot35/S5RUaPul7YhJjUGoT6hQd7PkJm4U3zD6fCdQdfw2Ym8Edl7eic1Pbza4tqU+euix98pe9HLrhW5tuxntk1aYhsziTOj0tWSWLMtF6HehmDtwLpYPXV7rfaZE1PpkZdWeyO3bb78VHdv18vJCWlqa0b6BgYG4fv16rXNNmDBBFMTGx8fDy8tLKKvVamzYsAEbNmyAhYUFFAqFEFDW9J///EcULPfo0QODBg3C4cOHhbqff/4ZmzdvhoODA4qKigzmeOWVV2pdKxERGceAlKgJzH98vkEyjXYO7fD3Pn9Ht7bdMOCbAaJA77fk34SA9Nj1Yxjx4wiUlFddl9BB0QEb/7YR/Tz74UbRDbyy5xXhCHD01WiER4Vj5ws7ja5jyzNbAFTdX3pdeR1bLm7BkkNLoKnUILM4E6/vex2ZxZlYMVycmGP1idWI2Fsts6R3CNaMWgNvJ2+cyjyFyb9ORmphKvTQY8WxFXC1dcWbjzGzJBGZV25uLrKzs2ttrxlcVlbeOQlS8+isTqczGoxaW1tj6dKlRjMBb9q0CaNGjcK5c+eEOr1ebzQYnTNnDgNSIqK7wC0NoiZQV2bHIM8gdHYW38d3tfBOZsdXdr8iBKMAsHLYSoT6hMLO2g7d2nbDj+N/hAR35t91eReScpPqXI+t1Bbd2nbDwuCFWDlcnAVy1clVyCjKEMo5pTmYv79aZklI8L+n/gd/V3/YSm0R3DEYK4bVyCx5nJkliahlmT17No4dO4Z33nkHoaGh8PLygo2NDSwtLdGmTRv0798f8+fPR3Jysuj6l+rat2+P06dP48cff8T48ePRqVMn2NrawtLSEo6OjujVqxdmzJiB48eP47PPPmu2bMJERPcz7pASNQOppTi74+3d0tLyUiTcTBC19XTrKSq72rnC1c4VOaU5Qt3F3Ivo7trdpPcO7hgsKlfoKnA26yw8HasSf5zKPAVVRbXMknaucLevkVnStUZmydIc5Jflw8XWxaQ1EFHrNXnyZEyePNmkvrUd5TWFRCLBgAEDMGDAgLueAwAsLS0RHh6O8PDwRs1DRETGMSA1QWlpKVJSUlBYWAiVSgWFQgFXV1d06tQJFhb3fpP5xo0buH79OkpKSqDX6+Hk5AQPDw906NDhnq+FDN0svonrRdcR5BlktP3PvD/xZ96forpOTlWZHcu0ZdBDL2pLLUwVBZvFmmLkl4mvFbCyuPOvcoGqAM5y51rXd+bmGYM6TaVGeF3zDtL8snwUaYpEyZNuZwCubQ1ERERERKbgb5B1yM7ORlRUFC5cuICKigqDdoVCgUGDBmHkyJFNnua9vLwcBw4cwP79+6FUKo32cXV1xeDBgzFkyJBmCZSpSnZpNvqt74cw3zBMfXgqhnYaCicbJ2grtTh47SBm75ptcP3KhO4TAABtbdvC1dYVuWW5Qtu83+fBt40vurp0hVKtxMu7XjYY/3C7h4XXr+97HRlFGfh7778j1CdU2PksLS/Flotb8OqeV0VjJZDgYY8742vuflbqKzFj+wx8MfoLOMudkXIrBQv218gs6dwJChtmliQiIiKihmFAWosTJ07gp59+EtLEG6NUKrFjxw6cO3cOs2bNQtu2bZtkLdnZ2fj888/rTOwAVCV/2LJlC86cOYMZM2bAyalhmVfJvPak7MGelD0AAIVMgVJtKSp0hn/YeKr7UwjxCQFQdcRs1qOzsOTQEqH9fM55dFvTDQ7WDigpLzHYQR3nNw4dFHd2x/V6PaKvRiP6ajSAqudHpRZSKDXG/5AR3jscPs4+QrmHWw8M6jAIh9OrZZb842ds/mMzHGQOKNIYySwZxEQeRERERNRw3EYz4vz58/j2229FwaibmxuCg4MRFhaGRx55BFLpnWcAr1+/jjVr1kClUhmbrlHy8vLw3//+VxSMWllZoUePHhg+fLiwHhsbG6H9ypUr+Oyzz6BWG78yhJqWpcTSoE6pURoNRl/o9QJ+nPCjqG5R8CK80OsFg77F5cUGwejjHR7Hxr9trHM9ZdqyWoPRF/u8iHVj1hnUb5q4CX3c+4jq9NAbDUbnBM7BK/0YkBIRERFRw0n0er2+/m6th1KpxLvvvisEcxKJBE899ZTBMdji4mKsW7cOly5dEuoCAwMxbdo0s61Fp9Nh2bJlSE9PF+o6deqEf/7zn3B1dRX1LS0tRWRkJE6fPt2o9ahUKkRERGDVqlWQy+WN+wCt2JVbV/Br8q84nH4YF3Mv4mbJTZRpy2AntUMHRQcM9BqIyX0nY6DXwFrniE2LxY+JP+JkxkmkK9NRWl4KmZUMHvYeCGgXgGf8n8FE/4kGWR3/zPsTu1N243D6YSTnJSOvLA+3VLdgZWEFJxsn+LbxxcCHBuL5Xs+jr0ffWt+/UleJTRc2YWvSVpzLPoeskixoKjSws7ZDR0VHDHhoAKY8PAX9H+pvtu8bEREREbUuDEhr+Omnn3Dw4EGhPHbsWIwePdpoX61Wi6VLl+LmzZsAqoLXBQsWmC25UFxcHL755huh3K5dO8yfPx8ymcxof51Oh7Vr1+L8+fNC3fz58+Ht7W3yezIgJSIiIiKie4VHdqspKirC0aNHhbKrqyvCwsJq7S+VSvHcc88JZb1ej127dpltPQcOHBCVn3322VqDUQCwsLBAeHi4aCd3x44dZlsPERERERGROTEgrebs2bOibLqDBg2CpaXh84DV+fn5wcPDQyhfuHChzkRIplKpVKL711xcXNC9e/33TDo7O6Nnzzv3Vl68eBFlZWWNXg8REREREZG5MSCtJjExUVQOCAgwaVz1flqtFhcvXmz0Wm7cuAGdTieUO3XqZPLYzp07C68rKysNPhcREREREVFLwIC0mpSUFOG1o6OjQeKg2lQPAAHg8uXLjV5LSUmJqNyQK1xq9jXHeoiIiIiIiMyNAelfCgsLRde2eHl5mTy2Zt/bSY4aQ6vVispWVqZfGVv9ShpzrYeIiIiIiMjcGJD+JSsrS1Ru06aNyWMdHR1FAWP1O0PvVs0Mtw15DrS0tFRUZkBKREREREQtEQPSvxQWForKzs7OJo+VSCSiY7IFBQWNXk/NY7cNCSozMzNF5bKyMrMkWiIiIiIiIjIn08+BPuBqBmx1Xa9ijI2NjfBap9NBq9UaHJ1tCE9PT9jY2ECtVgMArl69ipKSEtjb29c5Tq/X48KFCwb1Go2mwZ+JiIiIiKg10mq1yMjIQEZGhvB7tKenJzw9PRv1Oz4ZYkD6l9uB320N/UGr+YynRqNp1A+rhYUFevbsiVOnTgEAKioqEB0djfHjx9c5Li4uDrm5uQb13CGtn0qrwsmMk4jPiIdSo4RCpkCgZyD6efaDXCqvfwIiIiIiuq+p1WocPHgQZ86cMfr7s0wmQ0BAAIKDg0UbUnT3GJD+pfr9o0DDkggZ619eXt7oNQ0bNkwISAFg37598Pb2xsMPP2y0f3p6OjZt2mS0zRzreVAVqgux5OASrD+zHkWaIoN2R5kjpgdMx6LgRVDYKJphhURERETU1FJTUxEVFWVw20V1Go0Gx48fx/nz5zFhwgT4+PjcwxU+mPgM6V9qBpQ1A9T61OxvbW3d6DV5e3tj8ODBQlmn0+Grr77CDz/8gLS0NJSXl6OyshI3b97Eb7/9hhUrVgjJj2r+xYbHdY2LSY1B98+74+MTHxsNRgGgSFOEj45/BL/P/RCTGnOPV0hERERETS01NRWRkZF1BqPVlZSUIDIyEqmpqU28sgcfd0j/UjOAq3ntSn1qBqTmCgCffvpp5OXl4Y8//gBQ9YzokSNHcOTIkVrHPPbYY1AqlaJnSW1tbc2yngdJTGoMRkWOgqbStOPMWSVZGBU5CrvCdyHUJ7SJV0dERERE94JarUZUVBQqKysbNK6yshJRUVGYPXt2ncd39Xo9Ll26hHPnziEzMxMlJSWwtLSEo6MjfHx80K9fP7i4uNQ6Pj09HWfPnkVGRgaUSiXKy8thZWUFOzs7uLm5oVu3bujdu3eDT3i2FNwh/UvNALKhz1xWfwbVwsLCbA87S6VSzJkzByNGjKj3h8zCwgKjR49GeHg4lEqlUC+RSOo9467VaqFSqYTPoVaroVKpGhyY3y8K1YUIjwo3ORi9TVOpQXhUOJRqZf2diYiIiKjFO3TokMk7ozWVlJTg0KFDtbar1WpERkZi06ZNSEpKglKpRGVlJcrLy5GXl4f4+HisXbsWJ0+eNDp+165d2LhxI86cOYOcnBxoNBro9XpotVoUFhbi0qVL2L59O9atW4fi4uK7+gzN7f4Mo5tAzWtWGnJ1i16vF10b05ArY0xhYWGBCRMmICQkBMePH0dSUhJycnJQUlICa2trtGnTBj169MDAgQPh4eEBvV6P/Px8YbyHhwcsLOr+28OePXuwY8cOoTxv3jwAwJgxY/Dkk0+a9fO0BB8c+gBZJVn1dzQiqyQLSw4twcrhK828KiIiIiK6l7RaLRISEho1R0JCAkJCQoxuSG3evNngWK+VlRV0Oh10Oh2Aqsfy9uzZAxsbG/Tp00fod+HCBcTHxxvMKZPJUF5eDr1eL9Tl5uZiz549ePrppxv1WZoDA9K/tGvXTlSuHtDVp6ioSHRk18PDw2zrqs7Z2RmjRo3CqFGj6uyXk5MjPEsKVD2LWp+wsDAMHToUarUa8+bNw/Lly2FjY3Pfbv3XRaVV4euErxs1x/qE9VgSsoTZd4mIiIjuY7evdWkMjUaDjIwMg9+5//zzT1EwKpFI8NRTT8Hf3x86nQ4HDx7E4cOHhfbdu3fDz89POLmZnJwsmq9NmzaYNGkSnJ2dodFo8NtvvyEpKUloT0lJadTnaC48svsXhUIBufxOcHH9+nWTx6anp4vKTRWQmqrmD6Mp2b+kUinkcrlwtNfGxgZyufyBvGfpZMbJWhMYmUqpUSIuI85MKyIiIiKi5pCRkdFk8/z555+isq+vL3r06AGJRAJLS0uEhITA3t5eaNdoNDh37pxQrnlLRrdu3YSTmDKZTLSbCjT82sqWggFpNb6+vsLr4uJio/d5GnPlyhVRuUuXLmZdV0NV39q3srJCQEBAM66m5YnPMDz6cDcYkBIRERHd3xq7O1rXPEVF4g2Qmo8ISiQSKBTiKwWvXr0qvK55gvPSpUvCY4IajQaJiYmidj8/v4YvvAV48M5jNkKfPn1w/vx5oXz69GmEhYXVO676uXOpVAp/f/8mWZ8psrKyRNv7vXv3hoODQ7OtpyVSasyTkMhc8xARERFR8zDXzRjG5qm5Y1k95wxQlYemeiJSAMjOzhZe9+/fH4mJicK4/Px8rF69GjY2NkJyo9u6deuGYcOGNfpzNAfukFbTp08f0TOTR44cqTf9c3JysugHp2fPns165+emTZuEH06JRILhw4c321paKoVMUX+nezgPERERETUPT0/PJpvnoYceEpVTUlJw8eJF6PV6VFZW4uDBgwbZfavngZHL5Zg+fToCAwNhbW0t1KvVauH3fQsLC4wYMQLPPfdcs8YgjcEd0mocHR3x+OOPIzY2FsCdbFWjR4822l+r1WLTpk1CWSKR1JlwKC8vDwsXLhTKLi4u+M9//mOexaMqi1f1B5sHDx5s0vOjrU2gZ6BZ5gnyDDLLPERERETUPDw9PSGTyRp1dFcmkxkNSAMCAnDs2DEhyNTr9fjll18MsuxWVz1RKlB1rUxxcbFB/W06nQ579+7F5cuX8fTTT9d71WNLxB3SGkaOHCn6B7l9+3ZER0cb/MAUFxfj008/xc2bN4W6Rx99FB06dDDrenbu3IktW7YgMzOz1j65ublYs2YN9u/fL9S5ublh3LhxZl3Lg6KfZz84yhwbNYdEI0HZ5bL6OxIRERFRiyWVShudbyUgIMBoQiG5XI7nn38etra2ovqKigohtqh5NWPNJKvffPMNkpOTodPpoFAo8OKLL2L+/PmYPXs2OnfuLPS9evUqoqKiGvU5mgt3SGtwcnLCtGnT8Pnnn0Ov10Ov12PLli04dOgQ/Pz8YGdnh5ycHCQmJkKr1Qrj2rVrh/DwcLOvR6VSITo6GtHR0XB1dYW3tzecnZ0hlUpRVFSE69evIy0tTTTG2dkZr7zyiugHmu6QS+WYHjAdHx3/6K7n0J/WY/zH47Fr1y6EhoaacXVEREREdC8FBwfj/PnzBsdnTWFvb4/g4OBa2x966CG8/PLLOHHiBC5fvoxbt25Bp9PByckJfn5+cHR0xO7du4X+1RMf7d69W5Rpd9iwYcLpx7Zt22L8+PFYuXKl0H758mXk5ubC1dW1wZ+jOTEgNaJXr16YPHkyIiMjhR+CnJwc5OTkGO3v5eWFWbNmNXkAmJubW2/m3y5dumDq1KlCSmgyblHwIkSej0RWSVbDBxcDOFiV3Sw8PBzJyckGGdKIiIiI6P5gY2ODCRMmIDIyst78MdVZWlpiwoQJ9R6TtbOzw5AhQzBkyBCDtu3bt4vKXl5eAKqufKl+EhOoOgFZc147OzuUlpYKdfdjQMoju7Xo378/Fi5ciL59+8LS0tJoH4VCgdGjR2PevHlo27Ztk6zDz88P/v7+9T6k3LFjR0ydOhVvvPEGg1ETKGwUiJwQCZllAx/+rgAQBeCvxwyysrKwZMkScy+PiIiIiO4hHx8fhIeHi+4FrYu9vT3Cw8Mbla8lNzfX4OqW23eLVj+JeVvNLL0ajUaUBAkwPAJ8P5Doq+cLJqNKSkpw5coVFBQUQK1Ww9HREW3btoWvr+89+4deWVmJ9PR0ZGVlobi4GFqtFtbW1nBxcYGPj4/ZglCVSoWIiAisWrWqVRz53Z28G6O/GQ29vQn/GhSjKhhNFVcrFArcvHmzVXy/iIiIiB5karUahw4dQkJCgtFERzKZDAEBAQgODjYpgdDRo0eh1+vh5+cHZ2dnWFpaQqvVIjk5GXv37hXtbnbv3h3PPPMMgKoESCtXrhQFnG5ubnjmmWfg4uICtVqNXbt2ia6sBICIiIj77uQej+yawN7eXvhrRXOxtLSEj48Ps+aamTxLDv0aPRAMIACAsf+uqAEkADgIYWe0OqVSibi4OAwePLgpl0pERERETczGxgbDhw9HSEgIMjIykJGRAY1GI2TS9fT0NJrAqDb5+fk4c+YM9u/fD4lEAhsbG9G1Lbc5OzvjySefFMoSiQSPPvooDh06JNTl5ORgzZo1sLa2Fj1bepufn999F4wCDEiplYuPj68KOPcBiAHg+deXDarqM/76Mp5pW8CAlIiIiOjBIZVK4e3tDW9vb7PNqdfroVKpDOq9vb0xceJEg9N2wcHBKCgoMNgFNRaMdujQAX/729/MttZ7iQEptWpKpfJOoQLAtb++GjMPERERERGqroSxtrbG9evXUVxcjLKyMlhYWMDe3h5eXl7o2bMnunTpYnTs7aRJAQEBSExMREZGBpRKJcrLy2FlZQV7e3u0a9cO/v7+8Pf3h0QiucefzjwYkFKrZq5jDffj8QgiIiIialoPPfQQHnrooUbNYe6d2pbm/kvDRGRGgYGBZpknKCjILPMQEREREbUmDEipVevXrx8cHR0bNYdCoWBASkRERER0FxiQUqsml8sxffr0Rs0xbdo0XvlCRERERHQXGJBSq7do0SJ4eHjc1VgPDw8sWrTIzCsiIiIiImodGJBSq6dQKBAZGQmZTNagcTKZDJGRkUxoRERERER09i6/SgAAIABJREFUlxiQEgEIDQ3Frl27TN4p9fDwwK5duxAaGtrEKyMiIiIienAxICX6S2hoKJKSkvDGG2/UmuhIoVDgjTfeQHJysknBaHZ2Nt5//30MHDgQ7u7usLa2hqurK/z9/REeHo5169ahsrJSNGbfvn1YunQpxo0bh/bt20MikYi+YmNjTfo8iYmJmDVrFrp27Qp7e3vY29uja9eumDlzJs6dO1fv+IMHD+Lvf/87fH19YWtrCzs7O/j6+mLq1KmIj483aQ1ERERERHWR6PV6fXMvgloOlUqFiIgIrFq1qlUn6lGpVIiLi0NcXByUSqWQSTcoKMjk78uXX36JuXPnorS0tM5+xcXFsLe3F8r1XWp84MABPPHEE3X2+fe//433338fOp3OaLuFhQUWL16Md99916BNq9Vi6tSp+OGHH2qdXyKR4M0338Ty5cvv20uYiYiIiKj5WTX3AohaIrlcjsGDB2Pw4MF3Nf7DDz/EvHnzDOplMhmsra1RXFzc2CXW6pNPPsHixYtFdTY2NpBIJFCpVAAAnU6HxYsXw8HBAa+99pqo78yZMw2CUWtra0gkEmg0GgCAXq/Hf//7X9jZ2RkNaomIiIiITMEju0RmdvLkSSxcuFAoS6VSLFy4ENeuXYNKpUJRURFUKhVOnjyJDz/8EFKpVDQ+JCQEb731Fn755RekpaU16L1v3LiBBQsWiOreeecdFBUVQalUGmQEXrhwITIyMoRyYmIiNm7cKOrz0UcfobS0FKWlpfjqq69EbUuWLEFKSkqD1khEREREdBsDUiIzW7Jkiei50DVr1uCDDz5Ahw4dhOOtNjY2CAoKwltvvWWQ3TcmJgYffvghJk6ciI4dOzbovdevXw+1Wi2UfX198e9//xtSqRRSqRTvv/8+OnfuLLSrVCqsX79eKG/fvl00X48ePfD666/DysoKlpaWmDFjBoKCgoT2iooKgyCViIiIiMhUDEiJzKigoAC7du0Sys7OzvD398fkyZPh7++Pjh07IigoCG+++SauXbtm9vffvXu3qDxkyBDRM54SiQRDhgypdUx6erqozdvb2+A9fHx8ROXo6Oi7XS4RERERtXJ8hpTIjI4dO4bqecJUKhUGDRok6pOeno74+Hh8/vnn2LhxI5599lmzvLdOpzPIntupUyeDfjUDyrNnz0Kn08HCwgJ2dnb/z959h3dV3v8ff50MMsmAgEAYYQmydwEBmQICguBPqCiCUEWpLba2xTpQv85SFS3gpIIKbrAqlopsZMtIAJEVVhIIK5CQnc/5/UFzmpPFJ8lJPpE8H9eVq5/7jPu8E2IvXtz3uW/bucKmDOc/tmfPHuXk5Mjb27t0hQMAAKDKYoQUcFD+sJZ3+mx+aWlpuuuuu7R582ZHnn3x4kVr0aFc4eHhBa7LfywjI0OXLl2SJPXo0cN2bu/evXrttdeUk5Mjl8ul+fPna8uWLbZrcnJylJSU5MS3AAAAgCqGQAo4qLBgFhYWpm+++UbJycnasGGDGjRoYJ3Lzs62LYBUFoVtL+PjU3ASRP5FlCQpJSVFkjRq1Ci1bNnSdm769OkKDAxUYGCgpkyZUuiz8wdhAAAAwB0EUsBB+RcokqTf/e53GjZsmIKDg3XjjTfq6aeftp1fu3btVfcqdUf+6bbSlT1F3TmWuw+qr6+vvvrqqwLTejMzM63QWVjILWwkFgAAALgaAingoJo1axY41rlzZ1u7U6dOtnZOTo4SEhLK/OzQ0FBVq1bNduzChQsFrjt//ryt7efnp5CQEKvdvHlz7d69Wy+88IK6du2qkJAQ+fr6qnHjxpo2bZoWLVpku7927doKCAgoc/0AAACoegikgIPatWtX4JiXl/0/s8IW//H39y/zs728vNS+fXvbscOHDxe47siRI7Z2hw4dCtRYvXp1zZgxQ1u3btXFixeVmZmpI0eOaM6cOQX2He3Vq1eZawcAAEDVRCAFHNS+fXvVqlXLdiwmJqbYdlhYmOrWrevI84cOHWprr1y50rbqr2maWrVqle2aIUOGuN3/6dOn9dprr9mOTZgwoRSVAgAAAARSwFE+Pj667777bMdee+017dixQ9KVEctnnnnGdn7MmDGObZkyefJk23usR44c0RNPPKGsrCxlZWXpiSeesI2Q+vv7a/LkybY+Fi5cqCeeeEK7d++23hvNzMzUsmXL1Lt3byUmJlrXdu7cWSNGjHCkdgAAAFQ9hpl3+ARVXlpamqZPn67Zs2fzXmApXb58WR06dCgwtTUoKKjA4kURERHasWOHbeXdrl276sSJE1b79OnTtnvCw8Nt74pu27bNdv+sWbP05z//2XZP7p9lWlqa7fisWbP0yCOP2I49++yzeuKJJyRJhmEoPDxcycnJBRZDioiI0ObNm9W0adNCfgoAAADA1RVcLhNAmQQFBWnlypUaOnSo9u3bZx3PH0YbNGigpUuX2sKkJJ05c6ZACM0r/0JFOTk5tvaf/vQnJScn67nnnpPL5ZJUMIh6eXnpscceKxBG8zNNs8AiSNKVd2W/+OILwigAAADKhEAKlIOGDRtq165dmj9/vj777DPFxMQoKSlJ1atXV6tWrTRy5Ejdd999ttVtnfTMM89o9OjRmjdvnlavXq34+HhJUr169dS3b19NmzZNHTp0KPTe0aNHKyUlRevXr9fx48d17tw5SVdW0+3WrZtGjx6tO+64o8BCSAAAAEBJMWUXNkzZBQAAAFBRGOIAAAAAAHgEgRQAAAAA4BEEUgAAAACARxBIAQAAAAAeQSAFAAAAAHgEgRQAAAAA4BEEUgAAAACARxBIAQAAAAAeQSAFAAAAAHgEgRQAAAAA4BEEUgAAAACARxBIAQAAAAAeQSAFAAAAAHgEgRQAAAAA4BEEUgAAAACARxBIAQAAAAAeQSAFAAAAAHgEgRQAAAAA4BEEUgAAAACARxBIAQAAAAAeQSAFAAAAAHgEgRQAAAAA4BEEUgAAAACARxBIAQAAAAAeQSAFAAAAAHgEgRQAAAAA4BEEUgAAAACARxBIAQAAAAAeQSAFAAAAAHgEgRQAAAAA4BEEUgAAAACARxBIAQAAAAAeQSAFAAAAAHgEgRQAAAAA4BEEUgAAAACAR/h4ugAAAFB5ZGVlKS4uTnFxccrIyJCfn58iIyMVGRkpX19fT5cHALjGEEgBAIDS09O1du1a7dy5UxkZGQXO+/n5qVOnTurTp4/8/f09UCEA4FpEIAUAoIqLjY3VkiVLlJKSUuQ1GRkZ2rRpk2JiYjR69Gg1bty4AisEAFyreIcUAIAqLDY2VosWLSo2jOaVkpKiRYsWKTY2tpwrAwBUBYyQAgBQRaWnp2vJkiXKyckp0X05OTlasmSJpk2bVuT03dTUVB0+fFhxcXGKj49XQkKCsrOzrfOhoaGaPn36VZ/lcrm0Z88e7d+/X/Hx8bp8+bK8vLwUGBio8PBwNWrUSO3atVN4eLjtvnPnzik2Nlbx8fGKj49XYmKiTNO0zrdv316jRo0q0fcNAHAegRQAgCpq3bp1bo+M5peSkqJ169bp5ptvLvT8gQMH9K9//ass5ens2bP69NNPdebMmQLnMjMzlZSUpNjYWAUEBKhbt2628+vXr9fu3bvL9HwAQPljyi4AAFVQVlaWduzYUaY+duzYoaysLIcqsjt79qzmz59faBj19/eXYRjl8lwAQMVihBQAgCood1uXssjIyFBcXJyioqIKnAsICFCrVq1Ur149RUZG6vTp01q+fLlb/bpcLn3++edKT0+3jjVp0kQ33XST6tWrJx8fH7lcLl26dEnHjh1TjRo1CvRRo0YNdezY0Xp+7oJMAIDKhUAKAEAVFBcX51g/hQXSFi1aqEWLFlY7KSnJ7T7379+v06dPW+2GDRvqzjvvlLe3t3XMy8tLYWFhCgsLK7SPPn362NpeXkwKA4DKiEAKAEAVVNbRUaf7ySs6OtrWbteundavX6/Dhw/r0qVLqlatmurUqaN27dqpefPmjj8fAFBxCKQAAFRBfn5+laqfvE6cOGFrL1++3LZCr3TlHdM9e/aodevWuu2222yjpwCAXw7mrwAAUAVFRkZWqn5yZWZmKjU11XYsfxjNa+/evfrPf/7jaA0AgIrDCCkAAFVQZGSk/Pz8yjTl1s/Pz/FAmncho7x69OihG2+8UTk5OVqxYoX27Nljndu+fbt69uxZ5PukAIDKixFSAACqIF9fX3Xq1KlMfXTq1Em+vr4OVXSFj0/BfysPCwvToEGDFBQUpJCQEA0fPlzVqlWzzpumqUOHDjlaBwCgYhBIAQCoovr06aPg4OBS3RscHFxgJVsnFLbHaJ06dWzH/Pz8Cmz1cunSJcdrAQCUPwIpAABVlL+/v0aPHl3iBYG8vb01evRo+fv7O16Tl5eXatWqZTuWP6AWdqywkVUAQOVHIAUAoApr3Lixxo8f7/ZIaXBwsMaPH6/GjRuXW01NmjSxtRMTE23t7OxsnT9/3nYsIiKi3OoBAJQfAikAAFVc48aNNW3aNPXo0aPIbVz8/PzUo0cPTZs2rVzDqCR17tzZNgJ67tw5/fDDD3K5XMrOztZ3331nW4zJ399fTZs2LdeaAADlwzBN0/R0Eag80tLSNH36dM2ePVsBAQGeLgcAUMGysrIUFxenuLg4ZWRkWCvpRkZGlmgBoxMnTuiTTz6x2tnZ2bYQaRiGAgMDrXaDBg00duxYq71y5Upt2LDB1qe3t7dM05TL5bIdHzZsmLp06WI7tnHjRm3cuNFqZ2Rk2LaP8fHxsYXvnj17qmfPnm5/fwAAZ/DCBQAAsPj6+ioqKkpRUVFl6icnJ0eXL18u8rxpmrbzaWlptvP9+/dXZmamtm7dauszL8Mw1K9fvwJhVLqyn2lxz8/OzrYF1MzMzKK/GQBAuSGQAgCASscwDA0dOlRt2rTRjz/+qGPHjiklJUWGYSgkJERRUVHq1q2bateu7elSAQBlwJRd2DBlFwAAAEBFYVEjAAAAAIBHEEgBAAAAAB7hWCDduXOnU10BAAAAAKoAxwJp586d1b17dy1YsEDp6elOdQsAAAAAuEY5OmV327Ztmjx5surVq6fp06frp59+crJ7AAAAAMA1xPF3SE3TVFJSkv7xj3+oTZs26tu3rz755BNlZWU5/SgAAAAAwC+YY4H0ySefVGRkpO2YaZpav3697rzzTtWvX1+PPvqoYmNjnXokAAAAAOAXzNF9SF0ul7766iu9/fbb+u677+RyuWQYhnIfYRiGDMPQwIED9cADD2jEiBHy8qr8C/1evnxZhw4dUlJSktLS0hQaGqpatWqpSZMmFV6/y+VSQkKCTpw4oZSUFGVmZsrf31/Vq1dXw4YNVbt2bRmGUer+2YcUAAAAQEVxNJDmdezYMb355ptasGCBTp8+feVh/w2nuYGpXr16+s1vfqMpU6aoXr165VFGmZw+fVpLlizRnj17lJ2dXeB8aGioevfuraFDh8rHx6dca0lLS9N//vMf/fDDD7p06VKR19WsWVN9+vTRgAED5OvrW6rnEEgBAAAAVIRyC6S5srOztWTJEr311ltavXr1lYfmGzX19vbW8OHDNXXqVN18883lWY7bNm/erMWLFysjI+Oq1zZo0EBTp05VREREudRy+PBhvf3220pKSnL7nuuuu04PPPCA6tatW6JnEUgBAAAAVJRyD6R5HTx4UG+++abef/99nTt37koB+UZNo6KidP/992vSpEmqVatWRZVmExMTo7lz5yrvj6Z27dpq2bKlAgMDdebMGUVHR9sWaqpbt67+8pe/OB7iTpw4ob///e+2rXQMw1DTpk3VqFEjBQQEKDU1VceOHdPhw4dt94aEhOjRRx9VjRo13H4egRQAAABARanQQJorIyNDn376qd566y1t3LixYFGGIV9fX40ZM0b333+/+vTpU2G1Xbx4UU8++aQVAA3D0JgxYzRgwADb+6LJycl6++23deDAAetY165dNWXKFMdqMU1TL774oo4ePWodq1evnqZMmVJgASlJOn78uN59911rirQkdejQQQ888IDbzySQAgAAAKgoHllRyM/PT3fffbc2bNigmJgYTZs2TcHBwdaiR6ZpKjMzUx9//LH69eundu3a6b333lNOTk6517Zs2TLbaOSIESM0aNCgAosXVa9eXb/73e9sU2K3b9+u48ePO1bL0aNHbWE0MDBQ06dPLzSMSlLDhg318MMP24Lk7t27deHCBcdqAgAAAACneHyJ2+PHj+vYsWNKTU21juUGU+nKKOGePXs0ZcoUtWzZUt9++2251XLp0iX98MMPVrtWrVoaMmRIkdf7+vpq3LhxVts0TUfr++mnn2zt3r17KzQ0tNh7wsPD1atXL1tNeUdxAQAAAKCy8EggTUxM1AsvvKAmTZpo+PDhWrZsmfW+pmmaMk1TnTp1kp+fn6T/vWd6+PBhjRgxQk8//XS51LVr1y7barq9e/eWt7d3sfe0bNlSderUsdp79uxxayEkd+RfxKhJkyZu3de0adNi+wEAAACAyqBCA+nq1as1duxYNWzYUI8//riOHj1qBVDTNFW9enX99re/1b59+7Rt2zbFx8fr5ZdftoJYbjB95pln9P333zteX3R0tK3dqVMnt+7Le11WVpb27dvnSD35X++tVq2aW/flBvlcZdmXFAAAAADKS7kH0gsXLujVV19Vy5YtNXDgQH3++efKzMy0XdOmTRvNmzdPcXFxev3119WyZUtJUlhYmB5++GH9/PPPmjdvnvz9/a1w9Y9//MPxWg8dOmR9DgkJcXuV3/wjkgcPHnSknvzbyJw/f96t+86ePWtr165d25F6AAAAAMBJPuXV8aZNm/Tmm2/q888/V3p6um3fUUny8fHRbbfdpgcffPCqq+h6eXlp6tSpMgzDWjF2y5YtjtablJSktLQ0q92gQQO3781/bUJCgiM1tW7dWkuWLLHa27dvt70fWpTt27dbn/38/NSiRQtH6gEAAAAAJzk6QpqSkqI33nhD7du3V69evfThhx/aQp50Zb/Op556SseOHdPHH39coi1dfv3rX1ufc/cxdcqpU6ds7ZLs3RkSEiIfn/9l+7zbrpRF/fr11bZtW6v9008/ae3atcXes3LlSv38889We+DAgWzfAgAAAKBScmyE9P7779dHH32ky5cvFxgNNU1TN910k6ZNm6bbbrvtqgsFFSUkJESBgYFKS0uTy+VyqnRJBRf+CQ8Pd/tewzAUFhZmTZV1cpuVu+++W3/729+svhcvXqyDBw+qd+/eatiwofz9/ZWenq6jR49q7dq12rlzp3Vv27ZtNWzYMMdqAQAAAAAnORZI33nnHSuA5i4+FBwcrLvuukvTpk1Tq1atHHlObt9OL9STf2Xc/AsDXY2/v7/12eVyKSsrS76+vmWuKzQ0VH/5y1+0ePFiK2xu27ZN27ZtK7aWwYMHa8iQIQX2TwUAAACAysLxd0hN01SrVq304IMPasKECQoODna0/7feektZWVmO9ilJ6enptnZJw2TeKbvSlYDrRCCVrowMT506VTExMVq0aFGxI7C1atXSr3/9a7Vu3dqRZwMAAABAeXEskPr4+GjkyJGaNm2a+vbt61S3Bdx5553l0m/e/UelggHzavJfn38l4bJISkrSp59+qh07dhTYCia/M2fO6PXXX1eTJk109913q169eo7VAQAAAABOciyQHj169BcdfvIHyvwB9WryX+/unqFXc+LECc2ePVspKSmSrkxZ7tq1q3r27KkGDRooICBAaWlpOn78uDZt2qRt27bJNE0dOXJEL7zwgh566CFdf/31jtQCAAAAAE5y7AXDX3IYlezvgEoq8bTg/IG0pO+gFuby5cuaM2eOFUa9vb314IMPavLkybrhhhsUHBwsb29vBQcHq1WrVpo8ebIeeOABa9GozMxMvfnmm7p48WKZawEAAAAAp7HizX/lD5D5Fzm6mrzvoHp5eTny/ui///1v2+q/I0eOVLt27Yq9p3379ho5cqTVvnz5sr799turPisrK0tpaWnW95Genq60tLRyeV8XAAAAACSHFzW6dOmS9TkkJKTC7y+LsLAwW7skW7eYpmkLjiXZMqa4Pjdv3my1/fz81K9fP7fu7devn5YtW2aF6q1bt2rs2LHFrri7fPlyffPNN1Z7xowZkqThw4drxIgRpfkWAAAAAKBYjo2QLliwQOHh4QoPDy/1okZ9+/a1+vj444+dKs0tdevWtbXPnTvn9r2XLl2yTdmtU6dOmes5c+aMkpOTrXZUVJTb76VWq1ZNUVFRVjs1NVVnzpwp9p4hQ4Zo9uzZevHFFyVJL774ombPnq0hQ4aUvHgAAAAAcIOjgTR3Bdg//OEPperjD3/4g0zTlGmamj9/vlOluSU0NFQBAQFW+8SJE27fe/z4cVvbiUCaN4xKV+orifwjzLnvoRbF19dXAQEB1ru0/v7+CggIcGzrGgAAAADIz5FAmpycrI0bN0q6MrV01KhRperntttus0Lh+vXrlZqa6kR5bmvWrJn1OTk5+aqjirkOHz5sazdv3rzMteQPgiXdRib/9U6t+gsAAAAATnEkkO7YsUPZ2dkyDEOdO3dWcHBwqfoJCgpSly5dJF1ZZGfHjh1OlOe29u3b29o//vijW/flrdPX11etWrUqcy3Vq1e3tU+dOlWi+xMSEmztin4nFwAAAACuxpFAeuDAAevz1VaBvZq2bdsW2m9FaN++vW0/0g0bNignJ6fYe/bv36/Tp09b7TZt2jiy5Ut4eLhtoaVTp065PY346NGjSkxMtNo1a9Ys8ZRfAAAAAChvjgTS8+fPW58jIiLK1FfNmjUL7bcihISEqFevXlb7zJkzWr58eZHXZ2Vl2RZfMgxDt9xyS5HXnz17Vvfff7/19de//rXYevKH+48++qjAfqeF1fTRRx8V2w8AAAAAVAaOBFLDMKzPJX3XMb+8+15eLXyVh6FDh1oL+0jS119/rRUrVsjlctmuS05O1uuvv26bGtulSxc1bNjQsVoGDx5sG7E9fPiwXnvttSLfbT116pReeeUVHT161Drm6+urwYMHO1YTAAAAADjFkX1I846KxsfHl6mvuLg463Pe0dKKEhYWpilTpmju3LnWir+ff/651q1bp5YtWyooKEiJiYmKjo62hee6detq/PjxjtYSERGhsWPHatGiRdaxAwcO6Mknn1SzZs3UoEEDBQQEKC0tTceOHdPhw4etlY5zjR8/3pF9UQEAAADAaY4E0vr161uf16xZU6a+Vq9ebX3OvzdoRWnbtq0mTpyoRYsWWSO+iYmJtvcy82rQoIGmTp1q2zbGKX369JEkffrpp1YAdrlcOnDgQLHv2Pr5+WncuHHq0aOH4zUBAAAAgBMcCaQ33nijfHx8lJOTo5MnT+pf//qXRo4cWeJ+/vWvf+nkyZOSJC8vL9v7nBWte/fuioqK0tKlSxUTE1Po4kahoaHq1auXbrnlFtvUWqf16dNHLVu21Pfff68tW7YoPT29yGv9/f3VvXt3DRo0qMzv8wIAAABAeTLM/HM8S2ngwIFatWqVJKlRo0bavHmzrrvuOrfvP3XqlLp3767jx4/LMAzdeOONWrdunROllVlKSooOHz6sCxcuKD09XSEhIYqIiFCzZs3k5eXIa7huc7lcOnnypOLi4nT58mVlZGTIz89PQUFBql+/viIjI8tUU1pamqZPn67Zs2eXy4gvAAAAAORybFjv0Ucf1apVq2QYho4dO6Z+/frp448/dmuF1927d2vcuHG2bU0effRRp0ors+Dg4AJ7lHqKl5eXGjZs6OjiSQAAAADgCY4N7w0YMEC33nqrTNOUYRjav3+/unbtqnHjxunLL7/UqVOnbNcnJCToyy+/1B133KGuXbta70Pmbp0ydOhQp0oDAAAAAFRCjk3Zla5Mbe3evbv27dsnwzCscGo9zDAUGBio1NRU22qwudeZpqk2bdpo48aNCg4OdqoslABTdgEAAABUFEdfgAwODtaGDRs0ZMgQWxjN3T7F5XIpJSVFLpfLOpbLNE0NGzZMa9euJYwCAAAAQBXg+Io8YWFhWrZsmRYuXKhWrVoV2BczP9M01bZtWy1atEhff/01e2YCAAAAQBVRLnuVGIahu+++W3fffbeio6O1Zs0a7dmzR+fOnVNycrJCQkJUs2ZNtW3bVn379lWbNm3KowwAAAAAQCVWfptn/le7du3cWmkXAAAAAFC1VOwmmgAAAAAA/BeBFAAAAADgEQRSAAAAAIBHEEgBAAAAAB5RbosamaapXbt2acuWLTp58qQuXLig9PT0q24Dk8swDM2fP7+8ygMAAAAAeJjjgTQ7O1uvvvqq5syZo5MnT5aqD9M0CaQAAAAAcI1zNJAmJCRo1KhR2r59e7EjoYZh2NrujpoCAAAAAK4djgXSzMxMjR49Wtu2bbOOeXl5qXbt2jp16pQVQhs2bKiUlBQlJSUpJydH0v8Cqr+/v2rXru1USQAAAACASsyxRY3eeustbdmyRYZhyDAMPfjgg4qPj1d8fLy8vb2tUdDY2FidOXNGqamp2rx5sx5++GGFhITINE1lZmZqwoQJio2NVWxsrFOlAQAAAAAqIccC6auvvmp9vvfeezVnzpxiRzt9fX3VrVs3vfzyy9q3b5/69++vnJwcPfvss3rooYecKgsAAAAAUEk5EkiPHDmio0ePSpJ8fHz0wgsvlOj+unXr6ttvv1Xv3r1lmqbmzZunpUuXOlEaAAAAAKCSciSQ5r43ahiGbrzxRkVERJS4j2rVqundd9+Vt7e3JOlvf/ubE6UBAAAAACopRwLpmTNnrM9t2rQpcD7vqrrp6elF9tO8eXP16tVLpmlq69atvEcKAAAAANcwRwLppUuXrM81atQocD4wMND6nJKSUmxfHTt2tD7v3LnTgeoAAAAAAJWRI4E0ICDA+uxyuQqcr169uvX5xIkTxfZotObEAAAgAElEQVQVEhJifY6Pj3egOgAAAABAZeRIIM37zmje0dJcDRs2tD7v3r272L7yhtCrjaYCAAAAAH65HAmkLVq0sD4fOXKkwPl27dpZn7/55psi+8nJydF//vMfq12axZEAAAAAAL8MjgTSNm3ayNfXV5K0Z8+eAucHDx4sSTJNU1999ZU2bdpUaD9PPfWUbUpvp06dnCgPAAAAAFAJORJIAwMD1a1bN5mmqWPHjunYsWO288OGDVOtWrVkGIays7N1880369lnn9X27dt1+PBhrVy5UuPGjdPzzz8vwzBkGIaaNm1KIAUAAACAa5gjgVSSbr31VuvzV199ZTvn6+urF198UaZpyjAMXb58WTNnztSvfvUrXX/99br55pv12WefyTRNmaYpSXr++eedKg0AAAAAUAk5FkjHjh2rdu3aqV27dtq6dWuB85MmTdL06dOtUCrJCqC5ITTXE088odtvv92p0gAAAAAAlZCPUx01bNhQu3btKvaaV155Rd27d9fTTz+tn376yXbONE21bdtW//d//2cbbQUAAAAAXJscC6TuuuOOO3THHXfoyJEj+vnnn5WUlKTq1aurTZs2ioqKquhyAAAAAAAeUuGBNFeTJk3UpEkTTz0eAAAAAOBhjgTS5ORkxcbGWu3WrVvL29vbia4BAAAAANcoRwLp4sWL9eCDD0qSGjVqpCNHjjjRLQAAAADgGubIKrvnzp2zVsodOHCgE10CAAAAAK5xjgTSmjVrWp/r1q3rRJcAAAAAgGucI4E0bwhNTk52oksAAAAAwDXOkUB64403ysfnyuuou3fvdqJLAAAAAMA1zrEpu4MGDZJpmtqwYYNtxV0AAAAAAArjSCCVpFmzZikgIEDZ2dn6zW9+o6ysLKe6BgAAAABcgxwLpK1atdJbb70lHx8frV69WgMHDtTBgwed6h4AAAAAcI1xZB9SSVq3bp0aNmyomTNnaubMmdqwYYNuuOEG9evXT3379lXLli0VFhYmX19ft/vs06ePU+UBAAAAACoZxwJp3759ZRiG1TZNU6ZpatWqVVq1alWJ+zMMQ9nZ2U6VBwAAAACoZBwLpLlM05RhGLZwmnvcHYZhuH0tAAAAAOCXy9FAmhskyxIoCaMAAAAAUDU4FkhXr17tVFcAAAAAgCrAsUB60003OdUVAAAAAKAKcGzbFwAAAAAASoJACgAAAADwCAIpAAAAAMAjCKQAAAAAAI8gkAIAAAAAPMKxVXb79+/vVFeSJMMwtHLlSkf7BAAAAABUHo4F0jVr1sgwDEf6Mk3Tsb4AAAAAAJWTY4E0l2mabl+bN3SW5D4AAAAAwC+fY4G0T58+JRrVTElJUXx8vBISEiRdCafVqlVT9+7dnSoJAAAAAFCJOTpltzQOHz6suXPnas6cOcrMzFTjxo311ltvydfX16nSAAAAAACVkMdX2W3atKleeeUVrVq1SoGBgVq4cKHuvPNOT5cFAAAAAChnHg+kuXr16qW5c+fKNE0tWbJEb775pqdLAgAAAACUo0oTSCXp7rvvVqNGjWSapl566SVPlwMAAAAAKEeVKpAahqEBAwZIko4fP66NGzd6uCIAAAAAQHmpVIFUkurVq2d9PnDggAcrAQAAAACUp0oXSDMzM63Pp0+f9mAlAAAAAIDyVOkC6datW63PwcHBHqwEAAAAAFCeKlUgXb9+vdauXWu1mzZt6sFqAAAAAADlqdIE0k8++USjRo2y2v7+/rrppps8WBEAAAAAoDz5ONXRM888U6LrTdNUamqqjh07pg0bNighIUGmaUq6strub3/7WwUEBDhVHgAAAACgknEskD711FMyDKNU9+YNoqZpqkePHnrqqaecKg0AAAAAUAlVmim7uaZMmaLly5czOgoAAAAA1zjHRkil/410uisoKEjh4eFq1aqVevbsqbvuuktNmjRxsiQAAAAAQCXlWCB1uVxOdQUAAAAAqAIq3ZRdAAAAAEDVQCAFAAAAAHgEgRQAAAAA4BEEUgAAAACARzi2qNHFixe1du1aSZKXl5eGDx9e4j6++eYba3Gkfv36qXr16k6VBwAAAACoZBwbIZ03b55uu+023XbbbXrnnXdK1ce7775b5j4AAAAAAL8MjgXSRYsWWfuQPvTQQ6Xq47e//a1M05Rpmvrwww+dKg0AAAAAUAk5EkgTEhK0b98+GYah8PBw9e/fv1T99O/fXzVr1pQk7d69W4mJiU6UBwAAAACohBwJpLt27bI+d+vWTV5epevWy8tLv/rVrwrtFwAAAABwbXEkkB45csT63LJlyzL1df311xfaLwAAAADg2uJIIL106ZL1OTQ0tEx95b3/4sWLZeoLAAAAAFB5ORJI/f39rc/Jycll6ivv/d7e3mXqCwAAAABQeTkSSCMiIqzPZZ1mm/f+vP0CAAAAAK4tjgTS5s2bS5JM09Tq1auVmZlZqn4yMzO1evVqqx0VFeVEeQAAAACASsiRQNq1a1dVr15dhmHo0qVLmjt3bqn6mTNnjvXeaEBAgHr27OlEeQAAAACASsiRQOrt7a2RI0fKNE2ZpqnHH39cmzZtKlEfP/zwgx5//HEZhiHDMDR8+HBVq1bNifIAAAAAAJWQI4FUkh5//HF5e3vLMAylpaVp0KBBmjdvnnJycoq9LycnR3PnztXgwYOVkZEh0zTl5eWlJ5980qnSAAAAAACVkI9THV1//fV67LHH9Mwzz8gwDKWmpuqhhx7S888/r9tvv13du3dXvXr1FBwcrJSUFMXFxWnLli36/PPPlZCQINM0rdHRGTNmqFWrVk6VBgAAAACohAzTNE0nOxw/frw++ugjGYah3K4Nwyjy+rzXmKap8ePH64MPPnCyJJRAWlqapk+frtmzZysgIMDT5QAAAAC4hjk2ZTfXokWL9Nxzz1l7iOaG0dz3S/N+5T3v7e2tl156iTAKAAAAAFWE44FUkh599FEdOHBAU6dOVY0aNVTUIKxpmqpZs6YeeughHTp0SH/605/KoxwAAAAAQCXk+JTdwkRHR2vPnj06d+6ckpOTFRISopo1a6pt27Zq06ZNeT8eJcCUXQAAAAAVxbFFjYrTrl07tWvXriIeVS4uX76sQ4cOKSkpSWlpaQoNDVWtWrXUpEkTeXmVyyAzPCArK0txcXGKi4tTRkaG/Pz8FBkZqcjISPn6+nq6PAAAAOCaUyGB9Jfq9OnTWrJkifbs2aPs7OwC50NDQ9W7d28NHTpUPj7O/ygXLFhQ4v1cC9OjRw9NnDix7AVdo9LT07V27Vrt3LlTGRkZBc77+fmpU6dO6tOnj/z9/T1QIQAAAHBtIpAWYfPmzVq8eHGhASXXxYsX9c0332j37t2aOnWqIiIiKrBC9/n5+Xm6hEorNjZWS5YsUUpKSpHXZGRkaNOmTYqJidHo0aPVuHHjCqwQAAAAuHYx37QQMTExWrBggS2M1q5dW3369NGQIUPUuXNn2xTOEydOaM6cOUpLS3O0Di8vr1J95depUydH67pWxMbGatGiRcWG0bxSUlK0aNEixcbGlnNlAAAAQNXg2Ajp0qVLNXnyZElSs2bNtHXr1hL30a1bNx06dEiGYejDDz/U0KFDnSrPbRcvXtS7775r25ZmzJgxGjBggC3sJScn6+2339aBAwckSQkJCVq0aJGmTJniWC0TJkzQhAkTSnTP8ePH9dxzz1ntmjVr6vrrr3espmtFenq6lixZopycnBLdl5OToyVLlmjatGnFTt9NTEzUsWPHdOzYMZ0/f14pKSlKTU2Vl5eXqlevrvr166tDhw6FjrY+/fTTJf5+Zs6caWufO3dOsbGxio+PV3x8vBITE22rXbdv316jRo0q8XMAAAAAJzk2QvrOO+8oKSlJFy9e1KRJk0rVx6RJk5SUlKSkpCS99dZbTpVWIsuWLVN6errVHjFihAYNGlRg5LF69er63e9+p7p161rHtm/fruPHj1dYrYXJ/85p9+7drb1e8T/r1q1ze2Q0v5SUFK1bt67I86mpqXrjjTf07bffau/evUpISFBycrJycnKUlZWl8+fPKzo6Wu+//76WLl0ql8tV2m+jSOvXr9eyZcu0c+dOnT59usitlwAAAABPciSQpqena82aNZIkb29v3XHHHaXqZ9y4cfL19ZVpmvr++++VlZXlRHluu3Tpkn744QerXatWLQ0ZMqTI6319fTVu3DirbZqmvv3223KtsTg5OTnatm2b1TYMQz169PBYPZVVVlaWduzYUaY+duzYUaLfTz8/v0L/YSA6OlorV660HQsKCir2y9vb23Z9eHh46b4JAAAAwMMcmbK7c+dOpaenyzAMdezYUTVr1ixVP+Hh4erUqZO2bNmitLQ07dy5U926dXOiRLfs2rXLtppu7969C/zlP7+WLVuqTp06OnXqlCRpz5491pYhFS0mJkbJyclWu1mzZqpVq1aF11HZ5W7rUhYZGRmKi4tTVFRUkdfUqVNHXbt2VYsWLRQUFKScnBxFR0dr2bJltqnC27ZtU9++fa33kh955JEi+8zKytLs2bOVmppqHevevXuB62rUqKGOHTuqXr16ioyMtBZlAgAAACoTR0ZIf/rpJ+tzhw4dytRX+/btrc/79+8vU18lFR0dbWu7uxhQ3uuysrK0b98+R+tyV/7puoyOFi4uLq5c+/Hx8dHtt9+u+++/X506dVJQUJCkK7MHOnbsqJ49e9quz8rK0tmzZ9165q5du2xhNCAgQB07dixwXZ8+fXTrrbeqS5cuqlu3LvvlAgAAoFJy5G+p586dsz7Xrl27TH3lHdFz9y/pTjl06JD1OSQkxO3RxaZNm9raBw8edLQud6SkpNhGwKpVq6bOnTtXeB2/BGUdHb1aP9WqVVPr1q2LvK9+/fqlep5pmtq8ebPtWJcuXWwrPgMAAAC/JI4E0ryLspR18ZS89zsVHNyRlJRk27alQYMGbt+b/9qEhATH6nLX1q1bbdNAO3XqVOwqsFWZU9OpS9vPhQsXbG0fHx+3prnv379f58+ft91XkVPaAQAAAKc5Ekjz/mU6913K0sp7f0Uu1pK/7ho1arh9b0hIiHx8/vc67unTpx2ry11M13VfZGSkx/pJT08v8GfVoUMHVatW7ar35r+vXbt2Cg4OLnENAAAAQGXhSCDNu/VJcdthuGP9+vXW5+uuu65MfZVEUlKSrV2SMGwYhsLCwqx2/hGw8hYXF2fbbqZmzZpq0aJFhdbwSxIZGVnmUVI/P78SB9L09HQtXrxYFy9etI5FRERowIABV7335MmTOnHihO0Y/+gAAACAXzpHAmnPnj3l5eUl0zR1+PBhW6gsiXXr1tnev8y/+Et5yj89uKSBJe/0WJfLVaFb1rD3aMn4+vq6vWBVUTp16lSidzcvXbqk9957zxYqa9SoobvuusutqdUbN260tVu0aKGIiAj3CwYAAAAqIUcCaXh4uLp27SrpyjugDz30kFJSUkrUR0pKih566CFJV0YcO3ToUKEjpOnp6bZ2SReKyTtlV6q4919dLpe2bt1qO8bI2dX16dOn1NNdg4OD1adPH7evT0xM1Pz585WYmGgdq1u3riZNmqTQ0NCr3n/hwoUCK05X5D/WAAAAAOXFsb0gcvdONAxDMTExuuWWW9xe3Cc+Pl5Dhw61rRJb3F6M5SHv/qNSwYB5Nfmvz8zMLHNN7ti7d69tCih7j7rH399fo0ePvuo+s/l5e3tr9OjRbi8YFRsbq3/+85+6dOmSdax58+aaOHGi24F406ZNtsW+IiMj1bBhwxLVDQAAAFRGjgXSMWPGqEePHtZfnH/44Qe1bt1aM2bM0K5du2wr8UpXRvZ27dqlP//5z2rdurU2btwowzBkGIa6du2qX//6106V5pb8gTJ/QL2a/Ne7s0iNE/JP12XkzH2NGzfW+PHj3Q6GwcHBGj9+vBo3buzW9TExMVq0aJFttLxz584aN26c278faWlp2rVrl+0Yf8YAAAC4VpRsGPAqvvjiC3Xp0sUaGU1KStKsWbM0a9Ys+fv767rrrlNwcLBSUlJ0+vRpa5qsaZoyDEOmaap+/fr68ssvnSzLLflHvEr6Dmj+QOrU1iLFSU1NVXR0tNVm79GSa9y4saZNm6Z169Zpx44dhU619vPzU6dOndSnTx+3R0Z/+OEHff/997ZjAwYMUK9evUpU37Zt22y/i+Hh4brhhhtK1AcAAABQWTkaSOvUqaN169ZpzJgx2r17t7WwjmmaSktL09GjRwvckzsqapqmOnXqpM8++0x16tRxsiy35A+QJX0HNO87qF5eXiV+B7U08ocV9h4tHX9/f918883q16+f4uLiFBcXp4yMDGsl3cjIyBL9eV68eLFAGDUMQ5s3b9bmzZsLvWfIkCFq06aN7Vh2dra2bdtmO9ajRw+3FqzauHGjbSGk/L/Pe/fu1aFDh6x2z549GXkFAABAhXM0kEpSkyZNtGnTJs2aNUv/+Mc/dPbsWdv53PCZyzRN1a5dW7///e/1xz/+scKmuuaXd9sWqWRbt5imads2pqL2T3Vy79GsrCxlZ2dbwTr3f318fCokXFcGvr6+ioqKUlRUVJn6yfv7nffY5cuXi7ynsCni0dHRtsXBAgIC1KFDB7dqyMzMvOrz8j6zot55BgAAAPJyPJBKV0acnnjiCT3yyCNasWKF1qxZoz179ujcuXNKTk5WSEiIatasqbZt26pv374aOHCgx0f28u6lKknnzp1z+95Lly7Z/nJfESO8p06dUmxsrNWuUaNGmfYeXb58ub755hurPWPGDEnS8OHDNWLEiNIXilIxTbPAaGrXrl2rzD8OAAAAoGowzMKGcyqBhISEAiGxvE2fPl1paWmSpOrVq+vvf/+7W/fFxMRozpw5VnvAgAG64447yqXGXEuXLtXy5cut9i233KKRI0eWur+8I6QzZszQiy++KH9//yo1QgoAAACgYjm2yq4TMjIy9Mknn+iWW25Ro0aNKvz5zZo1sz4nJyfrzJkzbt13+PBhW7t58+aO1pWfy+XSli1bbMfKuveor6+vAgICrJFqf39/BQQEEEYBAAAAlJtKEUg3bdqkqVOnqm7durrzzju1fPly5eTkVHgd7du3t7V//PFHt+7bsWOH9dnX11etWrVytK789u/fb3vHtVmzZqpdu3a5PhMAAAAAnOaxQHrixAk9//zzatGihXr16qV33nlHSUlJhS4IU1Hat29v2490w4YNVw3G+/fv1+nTp612mzZtyn3LFycXMwIAAAAATymXRY2KkpaWpi+++EILFizQmjVrZJqmFUDzbv8iSQ0bNqzI0iRJISEh6tWrl9asWSNJOnPmjJYvX65hw4YVen1WVpY+/vhjq20Yhm655ZYi+z979qwee+wxq12zZk09//zzJaoxPT1du3btstrVqlVTly5dStQHAAAAAFQGFTJCum7dOt17772qU6eO7rnnHq1evVoul0umadr2Kg0KCtI999yjlStX2laQrUhDhw61rfj79ddfa8WKFXK5XLbrkpOT9frrryshIcE61qVLl3IP0tu3b7dt0dGxY0ePr1AMAAAAAKVRbiOksbGxWrhwod5//30dO3ZMkn1/xtzRUMMwNGDAAE2YMEGjR49WYGBgeZXklrCwME2ZMkVz5861RnA///xzrVu3Ti1btlRQUJASExMVHR2trKws6766detq/Pjx5V4f03UBAAAAXCscDaQpKSn69NNPtXDhQm3YsEGSCp2Sm/d/jx8/rnr16jlZRpm1bdtWEydO1KJFi6zRyMTERCUmJhZ6fYMGDTR16lQFBASUa11nzpyxrehbo0YNtWzZslyfCQAAAADlxZFA+v3332vhwoVaunSptY9n3iCaO9Lo5+enYcOG6csvv7TOV7Ywmqt79+6KiorS0qVLFRMTU+jiRqGhoerVq5duueUW22JI5WXz5s22Uebu3btbU54BAAAA4JfGMEu5rO2BAwe0cOFCffDBB4qLi5NkD6F52z169NCECRM0duxYhYWFydfXVzk5OTIMwyPbu5RUSkqKDh8+rAsXLig9PV0hISGKiIhQs2bN5OVVKXbOcUxaWpqmT5+u2bNnl/uILwAAAICqrUTDehcvXtTHH3+sBQsWaOvWrZIKn5JrmqaaNGmiu+66S3fffbeaNm3qfOUVKDg4uMAepQAAAACAsnE7kI4dO1Zff/21MjIyJBU+JTc0NFT/7//9P02YMEG9evUqn4oBAAAAANcEtwPpZ599ZmvnBlFvb28NHjxYEyZM0K233io/Pz/HiwQAAAAAXHtKNGU377uhvr6+euyxxzR16lTVqlWrXIoDAAAAAFy7SrUij2EYyszM1Isvvqjf//73+ve//y2Xy+V0bQAAAACAa5jbgdTHx8d6V1S6EkrT0tL0ySefaPjw4apXr57+8Ic/aOfOneVWLAAAAADg2uF2II2Pj9err76qDh06WME07xTexMREvfbaa+rSpYvatGmjWbNmWdvBAAAAAACQn9uBNCIiQr///e+1Y8cO7d69W9OnT1ft2rUL3Xt03759mjFjhqKiojRo0CB98MEHunz5cvl8BwAAAACAX6RSvUPatm1bvfLKKzp58qS++uorjRkzRr6+vtaoae4KvDk5OVq1apUmTpyoOnXqaMKECVqxYoXT3wMAAAAA4BfIMHOHOMvowoULWrx4sd5//31t27btSud5Rk3ztnOPGYahnJwcJx4Ph6SlpWn69OmaPXu2AgICPF0OAAAAgGtYqUZICxMeHq5p06Zpy5Yt2rt3r/70pz+pbt26hU7pzat///56//33mdILAAAAAFWMY4E0rxtuuEEvvfSSjh8/rn//+9+644475OfnZwujuQF17dq1mjRpkurUqaOJEydq1apV5VESAAAAAKCSKZdAanXu5aXBgwfr448/1qlTp/TGG2+oR48etu1jpCujppcvX9YHH3ygQYMGqVGjRnr88cfLszQAAAAAgIc59g5pSRw8eFALFizQhx9+qBMnTlwppJD3TXm/tOLxDikAAACAilKuI6RFad68uZ577jkdPXpUK1as0Pjx4xUQEGDb2xQAAAAAcG3zSCDNZRiGBgwYoA8++ECnTp3Su+++q169ehFKAQAAAKAK8PF0AbmCg4N177336t5779WRI0f0wQcfeLokAAAAAEA58ugIaVGaNGmimTNneroMAAAAAEA5qpSBFAAAAABw7SOQAgAAAAA8gkAKAAAAAPAIAikAAAAAwCMIpAAAAAAAjyCQAgAAAAA8gkAKAAAAAPAIAikAAAAAwCMIpAAAAAAAjyCQAgAAAAA8gkAKAAAAAPAIAikAAAAAwCMIpAAAAAAAjyCQAgAAAAA8gkAKAAAAAPAIAikAAAAAwCMIpAAAAAAAjyCQAgAAAAA8gkAKAAAAAPAIAikAAAAAwCMIpAAAAAAAjyCQAgAAAAA8gkAKAAAAAPAIAikAAAAAwCMIpAAAAAAAjyCQAgAAAAA8gkAKAAAAAPAIAikAAAAAwCMIpAAAAAAAjyCQAgAAAAA8gkAKAAAAAPAIAikAAAAAwCN8PF0AAFQmpmnK5XJ5ugwAAIBy5eXlJcMwPF0GgRQAXC6XLl++rJSUFKWmpso0TU+XBAAAUO78/f0VEBCgoKAg+fv7e6QGAimAKs3lcikhIUFpaWmeLgUAAKBCpaenKz09XRcuXFDNmjUVFhZW4aOmvEMKoMoijAIAAFxx7tw5JSYmVvhMMQIpgCrr8uXLhFEAAID/Sk5OVkZGRoU+kym7AKqslJQUW9swDEVERCgwMFBeXvx7HQAAuDaZpqnMzEwlJycrOTnZdu7ixYsV+j4pgRRAlWSaplJTU23HIiIiFBoa6qGKAAAAKo6Pj48CAwPl7e2tpKQk63hKSopq165dYe+SMgQAoEpyuVwF3pEIDAz0UDUAAACeERISYmubplmh75ESSAHgv6r6NN0FCxbIMAwZhqGjR48WOD9x4kQZhqGoqKgKr62q6Nu3rwzDUN++fT1dSpUUFRUlwzA0ceJET5cCABXG29u7wDECKQAAAADgmkcgBQBUmNwR2KeeesrTpRTrqaeesmq9lhw9etT6vhYsWODpcqqka/V3CwBKi0WNAAAApEKnqgMAyhcjpAAAAAAAjyCQAgAAAAA8gkAKAA4wTVMnT57U3r17dfLkyQpdnS7Xnj179Oyzz2rw4MGqX7++/Pz8FBwcrObNm+uee+7R5s2bK7ymXLmrl+Z6+umnrffocr+KWtn05MmTevTRR9WpUyeFh4fL399fDRs21NixY7V69epin5uTk6MFCxZo8ODBqlOnjqpVq6awsDA1b95cAwYM0PPPP699+/ZZ1+euNPz0009bx/LXWdQqxO7YtGmTbr/9dtWpU0f+/v5q3Lix7rvvPv38889u3Z+QkKB58+bp9ttvV/PmzRUUFCQ/Pz9FRkZq5MiR+uSTT+RyuQq91zAMNW7c2GpPmjSpwPeV/93eI0eO6OWXX9aIESMUFRWlgIAABQQEqFGjRho7dqyWL19eqp9DXvnfqUxKStLMmTPVunVrBQcHq0aNGurbt68WLVrkVn9Hjx7Vww8/rNatW6t69eoKDAxU8+bNdf/99ysmJqbYe4tbZXfNmjVWnWvWrJEkffrppxowYIBq1aqlgIAAtWjRQn/+8591/vz5AveX9nfrxx9/1OTJk3X99dcrKChI/v7+atCggTp37qxp06bpq6++8sj/3wCAY0wgj9TUVPO+++4zU1NTPV0KUK6ys7PNgwcP2r6ys7NL3E90dLQ5ZcoUs169eqYk6ysyMtKcMmWKGRMTUw7VF7R69Wrb84v6mjFjRpF9vPfee9Z1sbGxBc7fc889piSzUaNGJa6vUaNGV63tnnvuKXDfu+++awYEBBR73+TJk82srKwC9yYnJ5u9e/e+6nPHjBlT6M+guK/Cfj5X8/e//9308vIqtL+goCDz22+/NW+66SZTkgkqdyoAACAASURBVHnTTTcVuD87O7vI+/N+DRo0yExOTi5wvzvf18yZM63rjxw54tY9d911V6E/f3fNnDnT6uvIkSNm06ZNi3zW7bffXuyzFi5caPr5+RV5v7e3t/n8888XeX/u72lhv4t5/xv7/vvvzTvvvLPI5zRr1sxMSEiw3V+a361XXnnFrT/zwv68AcBdTv2dqLRY1AgASiEjI0OPPfaYZs+erZycnALn4+Li9O677+q9997T9OnT9dxzz8nPz6/c6snOzlZQUJCGDRum/v37q2XLlgoJCVFiYqL27t2r119/XceOHdOLL76o66+/XpMmTSq3Wgrz3XffKTMzU23btpUkPfDAA3rwwQdt14SHh9va//znPzVlyhRJUps2bXT//ferY8eOCgwMVGxsrObPn69vv/1W8+fPV2hoqF5++WXb/U899ZTWr18vSRo+fLjGjx+vhg0byt/fX2fOnNHu3bv1zTff2EZuR40apS5dumjevHl64403JKnQUbXIyMgSff9ffPGFHnnkEUlSaGio/vKXv1h7ja5atUp/+9vfdOedd6pWrVpF9mH+dxSsf//+Gjp0qNq2batatWopOTlZR44c0TvvvKNNmzZpxYoVmjZtmhYuXGi7PyYmRvHx8Ro8eLAk6dlnn9XIkSNt19SuXdv6nJOTo2rVqmnw4MEaNGiQWrVqpRo1auj8+fM6cOCA5s6dq7179+rDDz9UkyZNbCN/pTV27FjFxsZq6tSpuv322xUaGqro6Gi99NJLOnDggD7//HPVrVtXr7/+eoF7ly1bpokTJ8o0TQUHB+uPf/yjBg4cKB8fH23cuFEvvPCCzp49q7/+9a8KCwvTAw88UOo6n3zySW3cuFGjRo3ShAkT1KhRI50+fVpz587VsmXLdOjQIT388MP66KOPrHtK+rsVHR2tRx55RC6XS40bN9Zvf/tbdejQQTVq1FBKSooOHjyo1atXa+nSpaX+PgCgUqiw6ItfBEZIUVWU5V8D09PTzUGDBrk12pH7dfPNN5vp6enl9v2cOXPGvHDhQpHnMzIyrJobNWpU6PdaniOkuXL7zzsSV5jjx4+bgYGB1mhVUaNif/3rX01JppeXl/nzzz/bzjVo0MAaVSvOuXPnChzLO2pXVhkZGWbdunVNSWZoaKi5b9++AtfExMSYISEh1jMLGyF1uVzmwYMHi33Wk08+aUoyDcMwDxw4UOB8bGys9Yz33nuv2L5SUlLM+Pj4Is+7XC5z4sSJ1ghvUlJSsf0VJe/PWpK5ePHiAtdcunTJbN++vfVnHR0dbTufmZlpRkZGmpLM4OBgc+fOnQX6OHr0qPXnEBgYaJ45c6bANe6OkEoyn3322QLXuFwu8+abbzYlmT4+PmZiYmKx329xnnjiCetne+rUqSKvS0pKMnNycortCwCK4+kRUt4hBYASeuyxx7RixYoS3fPdd9/pscceK6eKpIiICIWFhRV5vlq1apo1a5Yk6dixY9q1a1e51eKE1157TampqapXr57+P3t3Hldz9v8B/HXrti83KVJSEZOIsoUkOzGJmAwhGpP1NzRixs5Yv4OZBmUbirHOGIwl+zbWQZTCUJStFFq0163z+6O5n7m3u9dNmPfz8biPx733c7bP53O63XPPtmHDBvD5sgf0LFq0CDY2NigvL8f27dsljr18+RIA4OnpqTAvc3NzzRRajoMHDyItLQ0AMG/ePDRv3lwqTMuWLZXWDx6PB0dHR4Vh5s+fDwsLCzDGcOjQoaoXGoCRkREaNGigsDyrV6+GtrY28vPzcfr06WrlB1T0ZA8fPlzqfRMTE2zatAkAUF5ejg0bNkgcP3DgAF68eAGg4u/T1dVVKg07Ozvub6CgoACRkZFVLmfbtm0xe/Zsqfd5PB6+/vprABWjFq5evVrlPET1t1mzZqhfv77ccAKBAFpa9HWOEPLhok8wQghRQ3x8PMLCwqoUNywsDAkJCRoukWzFxcV4+vQp7t27h4SEBCQkJEgsfBIXF/dOylFVf/zxBwDAx8cH+vr6csPx+Xx06tQJAKS+/IsaU3v37kVBQUENlVQ5UUONx+MhMDBQbjjRIkOqKi8vR2pqKh48eMDd4/v376Nhw4YANH+PS0tL8fz5c9y/f5/LLzU1FXXr1tVYfoqGknfo0AEtWrQAAKnGr/g1DgoKkpvGZ599BoFAIDMNdYwYMULuvWrbti33/PHjx1XOQ1R/7927h+vXr1c5HUIIed9Rg5QQQtSwZs0amXNGVVFWVoaffvpJwyX6V35+PpYvX47WrVvDyMgIdnZ2aNGiBVxcXODi4gI3Nzcu7OvXr2usHNWVk5ODpKQkAMDGjRtlrkQq/ti3bx+Af3uURESNvytXrnBz8A4cOIBXr1690/MRzRN0cHCAhYWF3HCWlpawt7dXmBZjDDt27ED37t1hbGwMGxsbODk5cffYxcWF6/3WxD0uLS1FeHg4OnbsCGNjY9ja2sLZ2Vkiv4yMDI3l1759e4XHO3ToAABITExESUkJ977ohx57e3uJebCV6erqcn8H1flxyMnJSe4x8R733NzcKucxfPhw6OjooLi4GB4eHvDx8cGGDRtw9+5dWlWXEPJRoUWNCCFERYwxREdHyzympaUlsd1G5dcix44dA2NMrZ4wVaSkpKBHjx5ITk5WKXxhYaFG89ckUQNHXZV7QefNm4cXL14gMjISGRkZCA8PR3h4OHg8Hlq0aAE/Pz9MmjRJ4XBITcjKygIAhQ0lkfr168u9h0VFRfDz88OxY8dUyre69zgzMxN9+vRBTEzMO8kPUH6NRPeKMYasrCzutWibFVXupZWVlUScqjA0NJR7THz4bFV/vAIqGr27d+/Gl19+iaysLBw5cgRHjhwBUDFEv1+/fggODlY6JJ0QQt531ENKCCEqevHiBVJTU2Ueq9z4lLcXpKI0qmPUqFFITk7mhiyePHkSz549Q1FRERhjYIxJfDl+n3tYxMs5bdo0xMfHq/Q4ceKERDo6OjrYsmULEhISMHfuXHTu3Bm6urpgjCEhIQHfffcdHB0dueHBNUV0rVX5EULRfVm6dCnXGPXy8sKvv/6KpKQk5OXloaysjLvPogZKde/x1KlTucbooEGDcOjQIaSkpKCgoADl5eVcfra2thrJD1B+jZTlUd1r/L4ZMmQIkpOTsXHjRvj5+XGrML9+/Ro7duxA165dMWbMGLmfN4QQ8iGgHlJCCFFRTk6ORtLJzs5We9sQRf7++29cunQJADBr1iwsXbpUZjhRT937TjQnEajo9WzZsmW10nN2dsbixYuxePFiFBYW4vLly9i1axe2b9+OvLw8DB8+HI8ePVK4gE91iIZwpqenKw0rr3eYMYaff/4ZANClSxecPXtW7kI2mrjPb9++xd69ewFUzJfcuXOn3LCarFfp6elcA1cW0fXh8XgS2wSJrnHlYdvy8hCP874TCAQIDg5GcHAwgIo5pYcOHcLatWuRmpqKbdu2wc3NDVOnTq3lkhJCSNVQDykhhKhItBhKdSlaDbcq7t69yz3//PPP5Ya7efOmRvOtKZaWllyD/fTp0xrt0TIwMECvXr2wdetWbsXVwsJCbiikiCaHVIv2Xk1OTsabN2/khnv16hVSUlJkHsvMzOQaW/7+/nIbo3l5eXjw4IHcPFQ9r8TERJSWlgJQXKcePHiAvLw8ldJUxY0bN1Q63rRpU+jq6nLvi360SElJUTjku7S0FLdv35aI865Vt245Ozvj22+/xbVr12BkZAQA+PXXXzVRNEIIqRXUICWEEBXZ2NjA2tpa5rHKDQR5DQZFaVSVUCjknitaTbbyVhm1QbRibnFxscJwAwcOBFCxSqlo0SJN69mzJ/e88oI84iv7KiurMr169QJQ0ctZeWsacVFRUXIb36re4y1btnANSVlUPa/aqlPbtm2Te+zmzZvcQkSiayoifo23bt0qN419+/ZxIx0qp/GuaKpu2draolmzZgDe70XKCCFEGWqQEkKIing8Hvr37y/zmKpzSL29vTW+oFHTpk255/K+0K9fvx4HDx7UaL5VIRoW++jRI4XhZsyYAT09PQDAhAkTlPbuRkdH486dO9zrzMxMHDp0SGHv6smTJ7nnDg4OMsupSlmVGTRoEJfe4sWLZfZg3rt3T+5Qa6Ci11jUs75nzx6JFWZFbty4gblz5yosS926dbmeRUXn5ejoyNVTeY3oI0eOYO3atQrzU9ehQ4dk9vbl5eVxQ1a1tLQwfvx4ieODBw/mfuhZtmyZzC1onj17htDQUAAVixIp2mKmJqlatw4ePIjs7Gy5x589e4a///4bgHT9JYSQDwnNISWEEDV89dVXiIyMrNLqmdra2jUyz8vNzQ0tW7ZEQkIC1q9fj+zsbAQEBKBBgwZ49uwZduzYgX379sHDwwOXL1/WeP7q6Ny5M5KTk3Ho0CFs3LgRHh4eXI+Rqakpt8qqg4MDNmzYgLFjxyIzMxMeHh4YNWoUPv30UzRq1AhCoRDPnz/H9evXsW/fPjx69AiHDx9Gq1atAFTMgfT19YW9vT38/Pzg7u4OOzs78Pl8pKWl4fDhw9yczIYNG8LHx0eqnCIhISGYM2cOGjRowDXS7O3tweer9i9UV1cXa9euxdChQ5GVlYWOHTvim2++Qbdu3cAYw/nz5/G///0PQMWPC4mJiVJpaGlpISAgAOHh4YiNjYWnpydCQkLg6OiInJwcREdHIyIiAsbGxrC2tsbDhw9lloXP56N9+/a4fPkytm7dCjc3N7i6ukJHRwdAxbxKc3Nz1K1bF/3798fRo0cRHR2Nfv36Yfz48WjUqBEyMjLw+++/IyoqCo0bN0Z2drbGttJp164dRowYgQsXLmDo0KEwNTXFnTt38L///Y9ryE+ePJm7zyI6OjrYtGkTfHx8kJubiy5dumDGjBno2bMn+Hw+rly5ghUrVnDDeVetWqVwC56apGrdCgsLQ0BAAAYMGIAePXqgefPmEAgEyMrKws2bN7F27VpuZeOJEyfWyrkQQohGMELEFBQUsODgYFZQUFDbRSGkRgmFQpaYmCjxEAqFKsWdPn06A6D2Y/r06TV2Prdv32Z16tSRm7eLiwtLTU3lXi9YsEAqjcjISO54cnKy1PHAwEAGgNnZ2VWrnHp6ejLLGBgYKBV+z549zNTUVOm11dLSYmfPnuXiJScnq3RPbGxs2K1bt2SW1d/fX248WddHmZUrVzItLS2Z6RkaGrKjR48yLy8vBoB5eXlJxc/Ozmaurq5yy2Rubs4uXLigMA3GGDty5Ajj8Xgy0xCvF0+fPmWNGjWSm1+jRo3Y3bt3mZ2dndz7p4oFCxZwaT5+/Jg5ODjIzXPIkCGstLRUblpRUVFy6xcApq2tzZYtWyY3vqJzOXfuHJfOuXPnFJ6Tor8zxlSrW6L7qOih7HwIIUQV1flOpAk0ZJcQQtS0dOlS9O7dW604ffr0UTgks7pcXV0RGxuLCRMmwM7ODjo6OjA3N0eHDh2watUqXL9+vcZWkVW3nFevXsXw4cPRqFEjbliuPMOGDUNKSgpWrFiBbt26oV69etDR0YGhoSEaN24MHx8f/PDDD0hJSUH37t25eHZ2doiNjcXKlSvh7e2NTz75BGZmZuDz+bCwsICXlxdWrVqF+/fvw83NTWbeO3bswPfff48OHTpAIBDInResqtDQUFy8eBF+fn6oV68e9PT0YGdnh6CgINy8eVPucHARgUCAy5cvY/HixXBxcYG+vj6MjY3RvHlzhIaGIi4uDl27dlVajgEDBuDMmTPw9fWFtbU11ztama2tLW7duoUZM2agWbNm0NPTg0AgQOvWrbFgwQLExsbC2dm5StdCHgcHB8TExGD27Nlo3rw5DA0NIRAI0LVrV66nX1HPdGBgIP7++29MnToVzZs3h5GREQwMDNCkSRN8+eWXuH37NmbNmqXRMleFKnXr119/xc6dOzFmzBi4urrCysoKfD4fxsbGaNmyJSZNmvTenA8hhFQHj7EPaEMuUuMKCwsxbdo0hIWFwcDAoLaLQ0iNKSsrQ3JyssR7Dg4O0NbWVil+cXEx5syZg7CwMIXDd7W1tTFt2jQsXbpUaeOLkP+ihQsXYtGiRQA+rD1CCSHkY1Hd70TVRT2khBBSBXp6eli1ahViY2Mxbtw4qX1FbWxsMG7cOMTGxmLVqlXUGCWEEEIIkYEWNSKEkGpo2bIlNm/eDMYYUlNTkZ2dDTMzM1hbW2t8NV1CCCGEkI8NNUgJIUQDeDwebGxspHpKCSGEEEKIfDRklxBCCCGEEEJIraAGKSGEEEIIIYSQWkENUkIIIYTUmoULF4IxRivsEkLIfxQ1SAkhhBBCCCGE1ApqkBJCCCGEEEIIqRXUICWEEEIIIYQQUiuoQUoIIYQQQgghpFZQg5QQQgghhBBCSK3g13YBPgT5+flISkpCdnY2CgsLIRAIYGlpicaNG0NLq/ba9Lm5uXj8+DFev36NoqIi8Pl8mJiYoF69emjYsCH09fVrrWyEEEIIIYQQogw1SBVIT0/H/v37kZCQAKFQKHVcIBDA09MT3t7e4PPf3aWMi4vDqVOnkJSUJHeZfB6PBzs7O3Tv3h0dO3Z8Z2UjhBBCCCGEEFVRg1SOa9euYdeuXSguLpYbJicnB0eOHEFcXBwmTJgACwuLGi1TXl4eoqKiEB8frzQsYwwpKSmIj4+nBikhhBBCCCHkvURzSGWIj49HVFSURGO0Xr166Nq1K/r164e2bdtCR0eHO/bs2TOsW7cOhYWFNVamrKwsfP/991KNUWtra3h4eMDb2xt9+/ZFx44d0aBBgxorByGEfIhSUlLA4/HA4/EQFRVV28X5zzl//jx3/c+fP1/bxfmg2dvbg8fjYcyYMdVKJyoqirsnKSkpGinbf52yzxm65oTIRj2kleTk5ODnn3/mhsLyeDwMGTIEPXv2lJgvmpubi02bNuHhw4cAgLS0NOzcuRPjxo3TeJlKS0uxbt06pKenc+85OjpixIgRsLGxkRnn9evXuHr1KgoKCjReHkIIIYQQQgjRBOohreTo0aMoKiriXvv4+KB3795SixeZmJjgq6++kuiNvHnzJp4+fVojZXr+/Dn32t3dHaGhoXIbowBgYWEBHx8fDBs2TOPlIYS8fz6kX9411cPzvlm4cCF3D0jt+FjrlqpoJAAh5ENEDVIxb9++xeXLl7nXlpaW6Nevn9zwOjo6+Pzzz7nXjDFER0drtEypqak4efIk97pRo0YIDAykLzyEEEI+GN26dQNjDIwxdOvWrbaL80FLSUkBY4wanISQjwY1SMXExsZKrKbr6ekJbW1thXGcnJxgZWXFvU5ISFC4EJK6Tp8+jbKyMu71sGHDlJaJEEIIIYQQQj4E1CAVc+fOHYnXbdq0USmeeLjS0lLcu3dPI+UpKirCzZs3ude2trZwdHTUSNqEEEIIIYQQUtuoQSomKSmJe25qagpLS0uV4jVp0kTidWJiokbKc+fOHYne1vbt22skXULIx0O0eunYsWO59xwcHLh5ZMpWNj116hRGjhwJBwcHGBgYwNTUFK1bt8bMmTORlpamMO/U1FR8++23aNOmDQQCAXR1dWFlZQUXFxcMHz4cUVFRePv2LRe+W7du4PF4ePLkCQBg27ZtUuWs6nDOsrIyhIeHw93dHaamphAIBGjTpg1WrVql8qiVa9euYe7cuejWrRusrKygq6sLU1NTODs7Y+LEiXJ/bBTN3120aBH3XuXzqjy3t7y8HGfPnkVoaCg8PDxgYWEBHR0dmJmZwdXVFaGhoRpZk6DynMobN25g+PDhsLW1hb6+PmxtbTFmzBjcv39fpfQOHz6MoUOHomHDhtDT00PdunXRqVMnrFixAnl5eXLjKVtld8yYMeDxeLC3twcAZGdnY/78+WjRogWMjIxgZmaGrl27YufOnTLTr0rdKisrQ1RUFPr27cvdbzMzMzRt2hQ9e/bEsmXL1P6BuaysDAKBADweD7NmzZIZZsmSJVyZfHx8ZIY5ePAgF+bu3bsSx+TNk+XxeHBwcOBejx07VuoaLFy4UG7Zy8vLsWnTJnTu3Bl16tSBkZERWrVqhaVLl2pkgcSzZ89i+PDh3GeNoaEh7O3t0bFjR4SGhuLs2bNScSrPjS8pKcEPP/yAdu3aQSAQwNzcHN26dcPRo0cl4uXm5uL777+Hm5sbTE1NYWZmht69e+PMmTMKy5iWloaIiAgMHToUTZs2hZGREfT09GBjYwNfX1/s3bsX5eXl1b4WhJBKGGGMMZaVlcWCg4O5x08//aRy3OzsbIm4YWFhGinTnj17JNJNSkrSSLqKFBQUsODgYFZQUFDjeRFSm4RCIUtMTJR4CIXC2i6W2s6dO8cAKH2cO3dOIl5eXh4bPHiwwjjGxsbs8OHDMvP9888/mampqdJ8xeN7eXkpDe/l5aX2NXj79i3z8PCQm2bbtm3ZrVu3uNeRkZFSaURGRiotm7a2NgsPD69SXAAsOTmZi7NgwQKl4Q0NDdn+/fvVvh7i7OzsGAAWGBjItmzZwvh8vsy89PT02J49e+SmU1hYqLS+WFtbs9u3b8uML15PK9dFxhgLDAxkAJidnR27f/8+s7e3l5vP5MmTpeKrW7dyc3OZp6en0jhDhgxR+5r379+fAWDu7u4yj/fs2ZNLXyAQyPzcmTp1KgPALCwsWHl5ucQx8XsqTpU6uGDBAi68eL1NSEhgPXr0kBuvQ4cOLC8vT+1rIRISEqK0bHXr1pWKJ17GuLg45u7uLjf+6tWrGWOMPXnyhLVo0UJmGB6Px3755ReZZRQKhUxLS0tpOXv37s1yc3NlppGcnKzy54z45wEhta22vxPRti//ePnypcRrc3NzleOampqCz+dz80/Ft2epDtEvvQCgpaUFW1tbABW/HF+7dg1xcXF48+YNCgsLYWxsDAsLCzg5OcHd3R0WFhYaKQMh5P3Wvn17xMfH448//sDcuXMBACdOnIC1tbVEOPGek7KyMvj4+ODcuXPg8Xj4/PPP4efnBwcHB5SWluL69etYvXo1nj59iiFDhuDKlSto27YtF7+4uBiff/453r59CxMTE0ycOBHdu3dHvXr1UFpaiidPnuDq1av4/fffJcoQGRmJ/Px89O3bF6mpqfD19cWSJUskwhgZGal9DQICArgF6Tp06ICQkBA0bdoU6enpiIqKwm+//Ybx48crTEMoFKJOnToYOHAgvLy8uN6R1NRU3Lp1C2vWrMHr168xZcoUODk5oUePHlzcQYMGoV27doiIiMD69esBQGrPaAASK6MLhUI0aNAAgwcPRqdOndC4cWPo6+vj2bNnuHLlCiIiIpCXl4cRI0bg1q1baN68udrXRVxsbCx27dqFevXqYdasWejQoQOKiooQHR2NsLAwFBcXcz3lHTp0kIofGBiIAwcOAABat26N6dOno3nz5sjMzMSePXsQFRWF1NRU9OzZE3fu3FG4CrwiBQUFGDhwIN68eYO5c+eiV69eMDY2xu3bt7Fo0SI8f/4c4eHh8PHxQd++fbl46tathQsX4uLFiwCATz/9FAEBAWjUqBH09fXx6tUrxMXF4ciRI1VaQNDLywvR0dGIiYlBXl4ejI2NuWOlpaW4evUq9zonJwe3b99Gu3btJNIQ9SJ7eXmpXIb4+HikpqZy12XJkiXw9fWVCFOvXj2ZcYODg3Ht2jUEBgbC398fVlZWePr0Kb7//ntcvXoV169fx5IlS7B8+XKVyiLuyJEj+PHHHwEArVq1wsSJE9G8eXMIBALk5OTg77//xqlTpySui7wyxsTEYNKkSRg8eDDq1KmD2NhYzJs3D2lpaZgxYwb69OmDMWPG4PHjx/j222/Rr18/GBkZ4fLly1iwYAFycnIwadIk9OnTR+pasH+2++vRowe8vb3h4uICS0tL5Obm4vHjx9i8eTOuXr2KU6dOYfLkydi2bZva14IQIsc7a/q+565evSrRG3nkyBG14s+ePZuLO2HCBI2Uadq0aVya33zzDWOMsfPnz7P/+7//kyhr5cfEiRPZ7t27WUlJidp5Ug8p+a+o7V8DNU2dX95XrVrFADAdHR0WHR0tM0xmZibXy9ClSxeJY2fOnOHykteDyhhjpaWlLCcnR+p9eT08VXHo0CGuLP3792elpaVSYRYtWiTRwyGr5+L58+csPz9fbj7Z2dmsVatWMq+HiHivpzLJyckKP6OfPXvGbGxsGAA2cuRIpenJI7rW+Kf3MS0tTSrM2bNnuZ7Tdu3aSR0/cuQIl0bPnj1ZcXGxVJhNmzZxYfz9/aWOq9pDCoCZmZmxhIQEqTCJiYlMX1+fAWADBw5UeL7K6patrS0DwIYOHaow3Js3bxQel+Wvv/7izuXYsWMSxy5evMgAMFNTU65Xd+XKlRJhMjMzuZ66NWvWSKWv6ByV9dCJq9yzL6vnsKioiLVs2ZLrwZT196XMqFGjuPonr2eRMdnXWryMPB6PHThwQCrMnTt3mLa2NgPALC0tmZ6eHrt27ZpUuKNHj3Jp/fDDD1LHy8vLWWJiosJzmT9/PleWhw8fSh2nHlLyoart70Q0h/QflecY6enpqRVfX1+fe15eXo7S0tJqlae8vByFhYXca4FAgD/++AO7du1SOh+qrKwM586dw48//iixpyohhJSWlmL16tUAgClTpsDb21tmuDp16mDlypUAgEuXLknMsRcfUdK1a1e5efH5fJiammqi2HKJeiT19PSwefNm8PnSA3/mzp2Lli1bKkzHxsYGhoaGco8LBAJ89913ACqux5s3b6pR6op5gDo6OnKPN2zYEDNmzAAAHDp0iOu9qY7Vq1dLrAov0r17d3z55ZcAKvbTvnHjhsTx8PBwABVbnUVGRkJXV1cqjS+//BK9evUCAOzfv1/p/GNFvvvuO7Ro0ULqfUdHRwwaNAgAuN7NqhLVYU9PT4Xh1BktJdK2bVuYmJgAgNR82QsXLnD59uzZU2aYTFsx5AAAIABJREFUP//8k5un+K62yPHz88PIkSOl3tfT08OUKVMAAG/evKnSoo2ia92mTRuJ3uLKlF1rf39/7v6Lc3FxQZcuXQAAr169QkhICNzd3aXC9e/fH3Z2dgBk1x8ej6d04cj58+fDwsICjDEcOnRIYVhCiOqoQfqPyg03RV8UZKn8Jai6W78UFhZKfAFJT0/n9jjV0tJCt27dMGvWLPz0009Ys2YNZs+eje7du0NL699b+ujRI2zfvr1a5SCEfFyuX7/ONRb8/f0VhhVvbIoPp2vQoAH3PDIyUsMlVJ1QKOS+4Pfp00dqmLKIlpYWAgMD1Uo7Pz8fKSkpuHv3LhISEpCQkCDxfyEuLq7qBZfh7du3SE5OlshP1EAWHauOOnXqSA3fFBcUFMQ9P336NPdc/Br37t2bmzoii6hRKxQK5S6ipQyPx8OIESPkHhcNHc/KykJ2dnaV8gD+rcN79+7VyII94rS1teHh4QFAurEpet2tWzeusXnp0iWJ7d1EYerWrav0hxRNCQgIkHtMfLj+48eP1U5bdK3//PNPPHr0SP3C/UN83/fKWrVqxT0fNmyY0nCqnEd5eTlSU1Px4MED7m/y/v37aNiwIQDNfwYQ8l9Gc0j/Ib7/KCDdwFSmcviSkpJqladyg1bUW6qjo4NJkybB2dlZ4ridnR3s7Ozg6uqKdevWcT20MTExiIuLQ+vWratVHkLIx0F8K6lOnTqpHE+8V7RLly5o3LgxHj9+jGnTpmHnzp0YPHgwvLy80K5dO5k9aDXh0aNHXGNC2SrksuZFVvb69Wv88MMP+P3335GYmKiwV/L169fqFVaGJ0+eYNWqVTh8+LDEmgHy8mvcuHGV83Jzc1P4f83V1RW6urooKSlBQkIC9/7jx4+5ayyr10mc+HHxNNRhYWGBunXryj0u3ouWm5sLMzOzKuUTGBiIxYsX48qVK3BwcMBnn32Gnj17okuXLiqvsK+Il5cXjh8/LjGPtLS0FFeuXAFQ0SBt1aoVDAwMpOaRihqkXbt2rdIc1qpwcnKSe6zyNVfX6NGjsX37drx58wYtW7aEr68v+vbtC09PT7W2smvWrJncY+L1QJVw8s6DMYadO3diy5Yt+OuvvyRGqlWmic8AQkgF6iH9R+V/1JUbqMpUDl/dL2Tyemh9fX2lGqPinJycMHjwYIn3Tp48Wa2yEEI+HhkZGVWKJ96LpKOjg8OHD3ML7dy4cQOzZ8+Gh4cHzMzM4O3tjV27dkn0+tSErKws7rm8xVpE6tevr/B4TEwMnJycsHz5cjx8+FDpEFlFX1RVcezYMTg7O2PdunVKG6OayE/Z9eHz+VzDIzMzk3tf/Lmyayg+HFg8njoUDZsGIDEKqDr1a968eQgKCgKPx0NGRgbCw8Ph5+eH+vXrw8XFBQsWLKjWAoWi3k+hUIhLly4BqBidUFBQAFNTU7i5uUFXV5f7UUjUCM3Ozub2RPfy8qpy/upSdN2re8179uyJdevWwcDAAEVFRdi7dy+CgoLQtGlTNGzYEBMmTFCpt1HVMqoSTtZ5FBUVYcCAARg1ahTOnz+v9G+uun+ThJB/UYP0H+JzQAGoPQe0coNU3TmolcmKb2hoqNJ8Ei8vL27+ClDRi5Cfn1+t8hBCPg6VhwbGx8er9Jg4caJEOs7OzoiPj8eBAwcQFBTE7cdcWFiI48ePIyAgAO7u7lVuAKtCvNGorCdJUQOzpKQE/v7+ePPmDXR0dPD111/jwoULSEtLQ1FRERhjYIxJDDeszpzON2/eYMSIESgoKICxsTEWLlyIq1evIiMjA8XFxVx+4nsmVncOqSo9bcryeFe9de+Cjo4OtmzZgoSEBMydOxedO3eGrq4uGGNISEjAd999B0dHR/zxxx9VSr9du3bcfElRY1N8/qi2tjaAfxuuojC1MX/0XZg8eTJSUlLw448/on///hAIBACAFy9eYOPGjXBzc+NWCa8tS5cuxbFjxwBUfI/69ddfkZSUhLy8PJSVlXF/l6J5x5qY100IqUBDdv9RuQGo7hxQ8TmoWlpaas9BrUxXVxdaWloSGzA3b95cpXT5fD6cnZ3x119/AQD3RUp8jgUh5L9JfDikrq5uteaoaWtrY9CgQdxCI2lpaTh27BgiIiIQExODmJgYjB8/ntsuRNPEhxIq681S1DA+e/YsN6csPDycmwtZmXiPbHX89ttv3PzH/fv3o3fv3jWaH6D8+giFQi4/8esq/rzy9miViR+vymJAtcHZ2RmLFy/G4sWLUVhYiMuXL2PXrl3Yvn078vLyMHz4cDx69Ehi3rQq+Hw+OnfujJMnT3KNTfH5oyKi5xcvXkRZWRkXpk6dOnBxcanm2b1f6tWrh2nTpmHatGkoLy9HbGws9u/fj/DwcGRnZ2Pp0qVo3769wrnONYUxhp9//hlAxZSEs2fPSvS6itPk3yUhpAL1kP6j8jwUdT5wGGMSiyvUqVNHI2WqnI46+7pVDqts8YfS0lIUFhZyDeuioiIUFhZWe7VgQsi7oWrvlZubG/dc08P5GzRogKCgIFy9ehVt2rQBULEHYeWhbZrqaWvSpAk3PK/yyrCVKTp+9+5d7rmihVPE59/Koup5ifIzNzeX2xhVJT91xMbGKpyKEhcXx619IP4jRePGjblrLPqRU57r169zz9/VYjyVVaduGRgYoFevXti6dSu3wnRhYSGOHDlSpfREQ25jYmKQlZUlMX9UxN3dHQYGBnj79i1u374tMX9UXoNIkQ+lF1tLSwtt2rTBkiVLJEYC/Prrr7VSnszMTO4HFX9/f7nXPi8vDw8ePHiXRSPkP4EapP+o/OunOkv6v337VuIfvaxl9TVRJmVzaxSFVTZk9/jx45g2bRq+/fZbAMC3336LadOm4fjx4yrnSQipPeLTDhSN8OjSpQvXe7Vhwwa8fftW42XR0dHhvowLhUKpH8REZa3uauR8Pp/L5+TJk3K3GikvL1e4ib3457e8FVfLy8uxadMmheVR9R6I8isuLpYYBSOuoKBAo6ukZ2Zm4vDhw3KPb926lXsu2r4FkLzGp06dwrNnz+SmIeph0tbWrrXhppqqW6ItWYCqL14jPo/0xx9/RH5+Pjd/VER8HunBgwe5uZRVnT+qah18n7Rp04b7Ab62FgpS5TMAALZs2UI/1BNSA6hB+g+BQAADAwPutaJ/upU9ffpU4nVNNUjVWWipclhlQ3379euHsLAwrFixAgCwYsUKhIWFoV+/firnSQipPeKfF4q2VtDX10doaCiAiiGWn3/+ucIfrHJzc7Fu3TqJ9y5evCixL2llJSUl3Hw5Y2NjqVVLRWWtzhYQIqK5rcXFxRg/frzMxUqWL1+O+Ph4uWk0bdqUey6v4Tpr1izcunVLYVlUvQei/PLz87Fv3z6p42VlZRg3bhxSU1MV5qeur7/+WubQ3QsXLnCN7bZt20qtWDx58mQAFSNpgoKCZK4iv3XrVq7HfciQIWoPcdUUVepWZmam0r1dxUcPODg4VKks7du3534cXrNmDQDJ+aMioobrunXrqj1/tG7dutyiipr4+9KEvXv3KlwA6ObNm9yotKpe6+qytLTkRsrt2bNHZh2/ceNGrc9zJeRjRXNIxTg6OnJfWnJzc/Hq1SuVln+v/KEv/uWmOpo1a4ZTp05xr9UZRlw5rKLNqIGKBqt4o1VfX1+igU4Ieb+5ublBX18fRUVFmDdvHvh8Puzt7bmhZzY2Ntzf9MyZM3HmzBmcOXOGW+11woQJ6NSpE8zMzJCbm4sHDx7g/PnzOHjwIPT19TFlyhQurzNnzmDx4sXw9PTEgAED0KpVK1haWqKwsBAPHz7Ehg0buMbbuHHjpFYx79y5M86dO4cbN25gxYoV8Pb2hpGREYCKYZPqTE/w8fGBj48PDh8+jMOHD8PDwwMhISFo2rQpMjIyEBUVhb1796J9+/Zyh+327dsX9erVQ0ZGBubMmYMnT55g4MCBsLCwQFJSEjZv3owzZ87Aw8MDly9flluWzp07c89DQkIwZ84cNGjQgBtGaW9vDz6fD39/f8yePRvFxcUYM2YMYmNj0atXL5iamuLu3btYu3YtYmJilOanjtatW+PevXto27YtZs2ahQ4dOqC4uBjR0dH48ccfIRQKwefzER4eLhV3wIAB+Oyzz/Dbb7/h9OnTcHd3x/Tp09G8eXNkZWVhz549XA+rubk5fvjhB42UuSpUqVtv376Fr68v7O3t4efnB3d3d9jZ2YHP5yMtLQ2HDx/mensbNmwIHx+fKpVFR0cHnTt3xunTp5GTkwNAdkNT9J4ojJmZWZW3auPz+Wjfvj0uX76MrVu3ws3NDa6urtz/d3Nz83c+v/ebb77BhAkT4Ovri65du6JZs2YwMjLCmzdvcOnSJaxduxZARc+6vPnbNU1LSwsBAQEIDw9HbGwsPD09ERISAkdHR+Tk5CA6OhoREREwNjaGtbU1Hj58WCvlJOSjxQjnzz//ZMHBwdzj2LFjKsWbN28eF2fy5MmsqKhII+UpKSlh//d//8elvWzZMpXjfv/99xLn8uLFC5XiFRQUsODgYFZQUFDVYhPyQRAKhSwxMVHiIRQKa7tY1TJz5kwGQObj3LlzEmELCgrY6NGj5YYXfzg4OEjEXbBggUrx/Pz8WGFhoVQ5nz9/zszNzWXG8fLyUvu83759yzw8POSWo02bNuzWrVvc68jISKk0jh8/zvT19eWm0a1bN5aQkKAwDcYY8/f3l5tGcnIyF27r1q1MS0tLbthhw4ax06dPy71/qrKzs2MAWGBgINu8eTPj8/ky89PV1WW7d++Wm05hYSEbPHiwwvttbW3Nbt++LTP+uXPnFJ5LYGAgA8Ds7OwUnk9kZKTM6ymiSt1KTk5Wqf7a2NiwW7duKSyPMosXL5ZI88aNG1JhiouLmYGBARfGx8dHYZri91SWI0eOMB6PJ/OcFixYwIVTdi1FxK+XvHqvSnkVPfT19dm2bduk4qpaRvHPJEUU1bPs7Gzm6uoqt4zm5ubswoULzMvLS+5nlbJrper5EPKu1fZ3IhqyK6Z169YSv+RfunRJ6Z5bf//9t8QQqJYtW1Z7yxcRHR0duLq6cq9TUlLw4sULpfFevnwp0WtrZmYGa2trjZSJEPL+WrFiBTZv3gxPT0+Ym5tLDQ0UZ2BggG3btuHmzZuYOHEiWrRoAYFAAD6fDzMzM7i6uuKLL77Avn37cP/+fYm4M2fORHR0NEJCQtCxY0c0atQI+vr60NfXh729PYYNG4ajR4/i999/l9pSC6jorb1+/Tq++OILODo6ygyjDhMTE5w/fx5r165F+/btYWxsDBMTE7i6umL58uW4fPmy0sXm+vbti5s3b2LkyJGwtraGjo4OLC0t4eXlhU2bNuHMmTNcT5siO3bswPfff48OHTpAIBDIXRxl7NixuHjxIgYNGgRLS0vo6OigQYMG6NevH/bu3Ys9e/YovH9VMW7cOFy8eBH+/v6wtraGrq4ubGxsMHr0aNy+fVvhgk76+vrYv38/Dh06BD8/Py5+nTp14O7ujuXLl+PBgwcS/7Nqgyp1y87ODrGxsVi5ciW8vb3xySefwMzMDHw+HxYWFvDy8sKqVatw//59ifmeVSHeIyoQCGSmp6urK9G7Xt39RwcMGIAzZ87A19eXq8u16c8//8TPP/+MYcOGwcXFBZaWluDz+TA1NUWbNm0wY8YM3Lt3D6NHj67VcgoEAly+fBmLFy+Gi4sL9PX1YWxsjObNmyM0NBRxcXHo2rVrrZaRkI8VjzHaSEnc7t27uVXuAGDgwIEYMGCAzLClpaVYunQpt5AGj8fD7Nmz0ahRI5nhX79+jTlz5nCv69ati2XLliksT3p6OhYuXMjNK3F0dMTXX38t94tKWVkZwsLCJIaTDB48WOW5oIWFhZg2bRrCwsJoyC75qJWVlSE5OVniPQcHB403AgipTfb29njy5AkCAwMRFRVV28UhhBDyHqrt70TUQ1qJt7e3xC+qhw8fxqlTp6RWQszNzcWaNWskVnVs166d3MZoVdWvX1/iF7mkpCSEh4dzc03E5eTkYP369RKN0bp1635Um2sTQgghhBBCPh60qFElZmZmGDduHMLDw8EYA2MM+/btw59//gknJycYGRkhIyMDd+7ckVj6u0GDBggICKiRMg0dOhTPnz/nVrW8e/cu5syZA2dnZ25FwZcvX+LevXsSK8Pp6upiwoQJ1R4ORwghhBBCCCE1gRqkMri4uGDMmDHYuXMn18DLyMhARkaGzPC2traYMGFCjQ1x1dHRwaRJk7B582ZuLldpaSni4uK4PcsqEwgEmDRpksZ7bAkhsjHG8CL3BXKKciDQF8DGxOaD2aSeEEIIIaS2UINUjo4dO8Le3h4HDhxAfHy8zMWNBAIBunTpgv79+0tta6BpRkZGmDp1Ki5evIizZ8/K3QDeyMgInp6e6NOnj0oLcBBCqic+PR5r/lqD6KRopOb+u2+kjYkNvB29MbXjVLSs17IWS0gIIYQQ8v6iBqkCVlZWmDhxIvLy8vDo0SNkZWWhqKgIpqamsLCwgKOjo9wVFGWxsLDAxo0bq1weHo+Hrl27omvXrnj+/DnS0tKQnZ2N8vJybm8sOzs7tcpECKmaYmEx5pydg7BrYShj0j9Yvch9gZ9v/4zI2EhM6zgNS3sshR5fMytwE0IIIYR8LKhBqgJjY+Mqb1JdUxo2bIiGDRvWdjEI+U8qFhbDZ7cPTj0+pTRsGSvD6qurEZ8Rj0OfH6JGKXmnUlJSarsIhBBCiELUlUYIIWqac3aOSo1RcScfncScs3OUBySEEEII+Q+hBikhhKghPj0eYdfCqhQ37FoYEjISNFwiQgghhJAPFzVICSFEDWv+WiNzzqgqylgZfrr2k4ZLRAghhBDy4aIGKSGEqIgxhuikaJnHtHhaCl+LHEs6BsaYxsv2MVq4cCF4PN5Hv31Ot27dwOPx0K1bt9ouSrUkJCRg5MiRsLW1ha6uLnfvYmNja7toH5Tz589z1+78+fO1XZwPWkpKCncto6Kiaiyfj+VvmJDaQosaEUKIil7kvpDY2kVcOStX+LpyGjamNhovHyG1JSYmBp6enigsLKztohBCCPnAUA8pIYSoKKcoRyPpZBdlayQdQt4Xs2bNQmFhIUxNTREREYHr168jPj4e8fHxcHJyqu3ivRdEPXULFy6s7aJo1H9lJAN5N2iEwH8T9ZASQoiKBPoCjaRjpm+mkXQIeR+UlpbiwoULAIDg4GBMnDixlktESAV7e/t3MkWCGk6EVA/1kBJCiIpsTGxgbWIt85iqc0gVpUHIh+j169coKSkBADRr1qyWS0MIIeRDQw1SQghREY/HQ3/H/jKPqTqH1NvRm4a2kY9KcXEx91xHR6cWS0IIIeRDRA1SQghRw1fuX0Gbp12luNo8bUztOFXDJVLdmDFjwOPxYG9vDwB4+fIlQkND0axZMxgaGsLGxgb+/v64e/euRLyUlBR89dVXaNasGQwMDFC/fn0EBATg0aNHSvMsKSlBREQEunfvDktLS+jq6sLKygr9+/fHjh07UF4uu+GuDqFQiC1btqB///6wtraGnp4eLCws0LVrV4SFhaGoqEhpGuXl5di9ezeGDBmCRo0awcDAAHXr1kXr1q0RFBSE48ePQygUSsSpfD3liYqK4uZEpaSkVPk8s7KysGTJEnTq1AkWFhbQ09ODtbU1fH19sX//fqXxDxw4gEGDBqFhw4bQ09ODiYkJGjduDE9PT8ybNw/Xr19XqzyiuYMODg7ce2PHjuXOtfJ8SXt7e/B4PIwZMwZAxUJIY8aMgYODA/T09GT+UBMfH4/g4GA0bdoUhoaGMDExQYsWLRASEqLwWspaXXX//v3o06cP6tWrByMjI7Ru3Rpr165FaWkpF48xhl27dqFbt26oV68eDA0N0aZNG2zYsKHKQz9F5y2yaNEiiWskfk3k+fXXX9GzZ09YWlrCwMAAn3zyCWbOnInMzEyVynDq1CmMHDkSDg4OMDAwgKmpKVq3bo2ZM2ciLS2tSuclqteLFi3i3qt8XpXrfOXVaBMTEzFlyhTu/lYOn5aWhoiICAwdOhRNmzaFkZER9PT0YGNjA19fX+zdu1fhZ4iyVXYrz38tKirCypUr0aZNG5iYmMDExAQdOnTAunXrpP7+xSlaZVdWGU6dOgUfHx9YWVlBT08PDg4OmDhxIp4/fy43D5HXr19jxowZEp/HvXv3xoEDBwBo7vOmup8X169fx5dffolmzZrB2NgYRkZGcHJywuTJk5GYmCgVXnSdunfvzr3XvXt3qfpUk6slk1rECBFTUFDAgoODWUFBQW0XhZAaJRQKWWJiosRDKBSqFHf6iekMC6H2Y/qJ6TV8VooFBgYyAMzOzo7FxsYyKysrBkDqYWhoyC5evMgYY+zMmTNMIBDIDFenTh2WkJAgN7+UlBTWvHlzmXFFjy5durA3b97IjL9gwQIunDxJSUnM2dlZYR5NmzZlDx8+lJtGcnIyc3V1VZgGAHbu3Dm511ORyMhILo3k5GSp415eXgwA8/LykpvG0aNHmZmZmcLyDRgwgOXm5krFFQqF7LPPPlN6fm3btlV4HpWJ3x95jwULFnDh7ezsGAAWGBjI1q9fz/h8vlR4ccuWLWNaWlpy09bT02Pbtm2TWbbk5GQuXGRkJJs4caLcdPz8/JhQKGRFRUVs6NChcsN9+eWXal2fyuet6BEYGMiFP3fuHPf+6dOn2YgRI+TGc3R0ZGlpaXLzzsvLY4MHD1aYt7GxMTt8+LDa5yVerxU9xOu8eF0/ePAgMzIykhteKBQqvP+iR+/evWXWe8ak60Fl4nX45cuXrHXr1nLz8fHxYWVlZTLzUfQ3XLkM33zzjdw8LC0t2b179+Re89jYWGZpaSk3fnBwsNLPG2Wq+3lRWlqq8O8NANPR0WGbNm2Se50UPWTdR1J91flOpAm0qBEhhKhpaY+luJN+B6cen1I5Tp8mfbC0x9IaLJXqCgoKMHjwYJSUlGDZsmXw8vKCtrY2jh8/jmXLlqGgoACjRo3CqVOnMHjwYJiamuK7776Du7s7hEIhfv/9d4SFhSErKwtffPEFrl27JpVHXl4eevTogcePHwMABg0ahKCgIFhbWyM5ORnr1q3DhQsXcOnSJXz66ae4ePEitLXV63lOS0uDh4cH0tPTYWJiguDgYPTq1Qv169dHTk4OTp48iZ9++gmJiYno168fbt26BYFAcmGq9PR0eHh4IDW1YjufHj16IDAwEE5OTuDxeEhOTsbZs2fx22+/VfFqV9+pU6cwcOBAlJWVwd7eHhMnToS7uztMTU3x4sUL7N27Fzt27MDRo0cRGBiI33//XSL++vXrufJ36dIF48aNQ5MmTWBsbIzMzEwkJCTg2LFjKve2iUyaNAlDhw5Famoq+vbtCwBYsmQJfH19uTD16tWTinfjxg3s2LEDtra2CA0NRdu2bVFWVoaLFy9yYSIiIjB79mwAgKWlJb755ht4eHigrKwMp0+fxsqVK5Gfn48xY8bAwsIC/fvLHkoPABs2bMBff/2F/v37Y9y4cbCzs8OzZ8+wfPly/PXXX9i/fz8iIyNx584d7Nu3DyNGjMCIESPQoEEDJCYmYuHChfj777+xefNm+Pn5oV+/fmpdp5MnT6KkpAQuLi4AgIkTJ2LSpEkSYerUqSMz7vz583HlyhUMGjQIo0ePhp2dHdLT0xEeHo6jR48iKSkJISEh2L17t1TcsrIy+Pj44Ny5c+DxePj888/h5+cHBwcHlJaW4vr161i9ejWePn2KIUOG4MqVK2jbtq3K5zVo0CC0a9cOERERWL9+PYCKHu3KbGykt7h6+vQpRo4cCUNDQ8ybNw+enp7Q1tbGjRs3YGxsDABcj3SPHj3g7e0NFxcXWFpaIjc3F48fP8bmzZtx9epVnDp1CpMnT8a2bdtULrssfn5+uH//Pr766iv4+PjA3NwcDx48wOLFi3H//n0cPnwYmzdvxvjx46ucx+bNm3HlyhV4eXlh/PjxaNasGbKzs7F9+3Zs374dr169QlBQEK5evSoVNysrC/369cOrV68AAAEBARg5ciQsLS2RlJSEn376CZs2bUJcXFyVywdU//Piiy++wPbt2wEA3t7eCAgIQLNmzbg9icPCwnD37l0EBwfDysoKPj4+ACrqSXx8PG7cuIGgoCAAwNatW9G+fXuJ9Bs2bFit8yPvqXfW9CUfBOohJf8V1f01sKi0iE0/MZ1pL9JW2CuqvUibTT8xnRWVFtXg2ahG1KMHgFlYWLCkpCSpMOHh4RK/1jdt2pRlZGRIhZsxYwYX7tatW1LHQ0NDueNz586VOl5eXs4CAgK4MBEREVJhlPWQfvrppwwAs7W1ZY8ePZIZ5tatW1wvjKxyDBo0iMvjf//7n8w0GKvoacrMzJR47130kObl5bH69eszAKxPnz4sPz9fZh6bNm2S6FUT5+npyQAwd3d3VlpaKrec8nqqlVHWCyUi3lPo4uLCsrKyZIbLyMhghoaGDACztrZmT58+lQojfl9tbGxYSUmJ3DIBYNOmTZNKIz8/n9nb23N/Dzwej4WFhUmFS0tLYyYmJgwAGzhwoJKrIZ+oLOK9xrKI95ACYEuWLJEKU15ezvr06cMAMD6fL/NvdNWqVVxvVHR0tMy8MjMzWYsWLRhQMVqhKlQZySAiquuie/vkyRO5YcvLy1liYqLC9ObPn88AMB6PJ3MUhDo9pDo6OlKjIBir+LsQ/Q22atVK4Xkp6yEFKnray8vLpcKNGzdO4WfqV199xR1ftWqV1HGhUMh8fX1l9jarozqfF/v27ePy3rx5s8x4hYWFrEePHgwAs7e3l8pDvP7Luh+kZtR2DynNISWEkCrQ4+thVZ9ViJ0Qi3Fu42BjItkLYGNig3Fu4xA7IRar+qyCHl+vlkoq2+LFi9GkSROp98eOHQt9fX0AwKtXr7B7yP7vAAAgAElEQVR27VpYWlpKhRPf2kO8ZwuoWOTm559/BgA4OzvL3HeRx+MhIiICdevWBQCsW7dOrfInJCTgyJEjXNzGjRvLDOfm5obJkycDqPi1Xdzff/+NP/74AwDg6+uLmTNnys3PyMhIbi9WTYqMjER6ejr09fXxyy+/wNDQUGa4L7/8Eh06dODiiHv58iUAoHPnzuDz5Q+MMjc311CplQsPD4eZmeztjyIjI1FQUAAAWL16NWxtbaXCuLm5YdasWQCAFy9e4ODBg3LzsrW1xffffy/1vqGhIQIDAwFUzMtzd3fH1KnSc7ytrKwwePBgANJ1vaa1bduW6ykWx+Px8PXXXwOomENduUettLQUq1evBgBMmTIF3t7eMtOvU6cOVq5cCQC4dOkSkpKSNFl8hVasWIFGjRrJPc7j8eDo6Kgwjfnz58PCwgKMMRw6dKha5fm///s/mXNAzc3NMXbsWADAnTt3kJNT9f2oGzRogLVr18qcLx0aGso9r1zPioqKuB7gNm3acPdenLa2NjZu3Mh9fldVdT4vli9fDgAYPHgwxo0bJzOevr4+93mfkpJCW+YQALSoESGEVEvLei2xeeBmPAt5huchz5EwMQHPQ57jWcgzbB64GS3rtaztIkrh8Xjw9/eXeczAwABNmzYFUPFltU+fPjLDOTg4wMTEBAC4YbkiMTExyM7OBlCx8I+8obimpqZcOe7du6fW4iqihqShoSEGDBigMGzXrl0BAKmpqXj27Bn3fnR0NDcsMCQkROW83yXReXp5eckc/ipOdJ6VGycNGjQAABw+fBivX7+ugVKqx9bWFp6ennKPnz59GgBgZmaGIUOGyA0n/oVXFEcWPz8/uav/tmrVins+bNgwuWm0bt0aQMWwSVHdfhdGjBghd1Vu8eG1lf8Gr1+/zv09yftbFxHVG0C67tQUXV1dfPbZZ2rFKS8vR2pqKh48eICEhAQkJCTg/v373BDO6g5VDQgIkHtM/FonJydXOY+hQ4dCT0/2j5OffPIJN1xZ1meqqCE8evRouXWifv363PD5qqrq58WLFy8QExMDQHmda968OSwsLAC8uzpH3m/UICWEEA3g8XiwMbVBi3otYGNq815v7WJhYaGwN0zUc+Xo6KjwPEThcnNzJd5PSEjgnru7uyssi/hx8XjK3Lx5E0DFfFg+ny9zdU/R49NPP+XiiX79B4Dbt28DqNiqpGPHjirn/S6JzvPEiRMKz5HH42HVqlUAJM8RANcLmJSUBEdHRwQFBWH37t0qrehZE8QbgbKI6oGbm5vCbWTq16/PrXCsqO4o2htVvJdW1XCV63tNcnJykntM/G+4cplE9QYAOnXqpLDeiBpBgHTdqSlNmzZVqSePMYYdO3age/fuMDY2ho2NDZycnODi4sI9YmNjAaDaP7ZU9VprKg/g37nEij5Tlc3zbdeuXRVLV6GqnxfidW748OFKP69E9+td1TnyfqMGKSGE/MfIG/YpoqWlpVa4srIyiffFF7uoX7++wjSsrKxkxlMmIyND5bDiRENBgX+/wJqbm8vttahNpaWlVeqNEz9HAAgKCsLs2bPB5/ORk5ODyMhIjBgxAra2tnB0dERoaKhUj0xNUjb0WVQPlNUd4N/6o6juKKrHojqsTrjK9b0mVbVMmvj7qEmqDH8vKirCgAEDMGrUKJw/fx6FhYUKwys7rsy7uP9V/UzNysrinisbKSFrioU6qvp58b7XOfJ+o1V2CSGE1BhlPcWiIbPqEn1hc3BwUGvumPiemSLva2+2+JdSf39/zJs3r8ppLV26FMHBwdi5cyfOnDmDa9euoaCgAI8ePcLq1auxZs0arFmzBhMmTNBE0RVSdTVlVe5LVevPx0687pw/f56bq62MssaOpqhSB5YuXYpjx44BqBiyPnnyZLRp0wZWVlYwMDDgGm9du3bFxYsXqS5oUFU+L8Tr3M6dO5WOhBCpjbn55P1DDVJCCCEaJT687eXLlwqHQqanp8uMp4zoC3Z6ejqcnJwULr4hj2gO05s3b1BSUgJdXV214ou+EJeXlysMl5+fr3bZgIrFPwwNDVFQUIDs7Gy0bFm9+ch2dnaYPXs2Zs+ezW378dtvv2Hjxo0oKirCpEmT4O7uDjc3t2rlU13m5uZIS0tTaSifqP68ywWZPgTiDVBdXd1q1513jTHGLYzWpUsXnD17VqKXUpx47+HHSrzRlpGRofAzVbQtTHWp+3khXud4PN4HV+dI7aIhu4QQQjRK/IvIX3/9pTDs9evXZcZTRvQlqKCgAJcvX1azhBXatGkDoGJobFUW1hAt6qRsWO2DBw/UL9w/ROd5+fJljQ5t09HRgYeHB8LCwrBr1y4AFY2Affv2aSyPqhLVg9u3b6O0tFRuuIyMDDx58kQiDqkg/qPCyZMnazSvmhhhkJmZyf0g4e/vL7cxmpeXV62/rw9FixYtuOficzVlUXa8KlT5vNBUnXtfR6yQmkUNUkIIIRrVtm1bbhGYbdu2yZ1zlZubi19//RVAxfYwotUdVeHr68s9l7WlhyoGDBjAffn58ccf1Y4vGv6bm5sr90txSUkJfv/99yqVDwAGDhwIoKKXNTw8vMrpKNKzZ0/u+fuwCm+vXr0AVDT0FV27LVu2cMM0RXHeZ6JFfIqLi2s8ry5dunC9xhs2bMDbt29rLC/xxYk0dW5CoZB7ruiHmC1btij80eJj0a5dOwgEAgDAL7/8Ind4cnp6Ok6cOFGjZZH3eeHo6AhnZ2cAwJ49e/D06dMqpV8T9Ym8/6hBSgghRKP09PS4LTnu3r2LRYsWSYVhjGHKlCncF5opU6aolUf79u25LWmio6OxYMECheFTUlKwe/duifeaNWvG7S/5xx9/cPsxypKfny81NNDLy4t7LtrzURxjDFOnTkVqaqrik1FgwoQJ3NDiefPmcXPq5Ll8+TL+/PNPifd27Ngh8QW/MvHeDFlzbN+1sWPHcou/TJ8+XWKrHpG4uDgsW7YMAGBjY4NBgwa90zJWhegHl0ePHtV4Xvr6+ty+li9fvsTnn3+ucOh4bm6u2nsBi4j/kKSpc7O0tOR+1NqzZw9KSkqkwty4cQNz587VSH7vO319fYwePRoAcOvWLfzwww9SYcrLyzF+/HgUFRVVK6/qfF6I7kdRURH8/PwUDh8uLi5GRESEVHlroj6R9x/NISWEEKJx8+fPx/79+/H48WMsXrwYCQkJCAoKgrW1NZKTk7Fu3TpuQ/ROnTohODhY7TwiIyPRrl07pKWl4bvvvsOJEycQFBQEFxcX6Ovr482bN7hz5w6OHz+Os2fPYtCgQRg+fLhEGhEREbh27RpSU1Mxc+ZMHD9+HGPGjIGTkxN4PB63cfuePXuwb98+dOvWjYvr5uaGjh074tq1a9i8eTNKSkoQGBgIgUCAxMREbNiwAefPn0enTp2qvNeeqakpdu/eDW9vbxQXF+PTTz/FkCFDMGTIEDRp0gQAkJaWhpiYGBw4cAB37tzB2rVrJfaWHDVqFEJDQ+Hn54fOnTujSZMm0NfXR3p6Ok6dOoX169cDAIyNjTFy5MgqlVOTLC0tsXLlSkyePBmpqalo164dvv32W3Tu3BllZWU4ffo0Vq5ciby8PPB4PGzatEnh9jDvi86dOyM5ORmHDh3Cxo0b4eHhwfUGmZqaanxBoZkzZ+LMmTM4c+YMjh07BmdnZ0yYMAGdOnWCmZkZ17N//vx5HDx4EPr6+mr/MCQ6L5GQkBDMmTMHDRo04EYf2Nvbqz3HW0tLCwEBAQgPD0dsbCw8PT0REhICR0dH5OTkIDo6GhERETA2Noa1tTUePnyodrk/NAsXLsRvv/2Gly9fIjQ0FLdv38aoUaNgaWmJpKQk/PTTT7hy5Qo6dOjATYWoyvDX6nxeDB8+HCdOnMC2bdsQExMDZ2dnjB8/Hl5eXrC0tER+fj4ePXqEixcvYv/+/cjMzOQa2iKNGjVCw4YN8fz5c6xatQo2Njb45JNPuDpUv359broE+YgwQsQUFBSw4OBgVlBQUNtFIaRGCYVClpiYKPEQCoW1XawaFRgYyAAwOzs7heG8vLwYAObl5aUwnJ2dHQPAAgMDZR5PTk5mTk5ODIDch4eHB3vz5o3M+AsWLODCyZOSksLat2+vMA/RY+zYsTLTePToEWvZsqXS+OfOnZOKe//+fVavXj25cb7++msWGRnJvU5OTpZKQ5XrfebMGWZlZaXSeW7btk0iripxzMzM2IkTJ+Tmr0hycjKXTmRkpNxwyupLZUuXLmVaWlpyy6ynpyd1ruqW6dy5cwrvr4iye6iK27dvMz09PZnnIn5NVC0TY//e2wULFsg8XlBQwEaPHq1SHXBwcKjSeTHGmL+/v9x0xa+Xqp8tjDGWnZ3NXF1d5aZrbm7OLly4oDBNZfVAlc8YxpTfk+qUQZyyv5HY2FhmaWkp95qMGTOGbdmyhXv98uVLhfnJUt3PC6FQyGbOnMm0tbWVpmNkZCTz+2ZERITcOMquIama2v5OREN2CSGE1Ah7e3vExcVh3bp18PLyQt26daGjo4P69eujX79++OWXX/D/7N13WJPn3gfwbyRAWAnIUEBlCBRBFHCgoGLde7buiuKkek71OHq07tWhtp4qttUqaGur1WrdWhW3IspQcCKCCxQF2TNwv3/QPG9CBgkEgvr7XBfXleS51zPJnXtduHChRjOkOjg44Nq1azhw4ABGjRoFJycnGBsbQ19fH9bW1vD398ecOXNw/vx5bNu2TWEazs7OiIuLQ3h4OPr37w9bW1sYGBjAysoKrVu3xpQpU3D69GmZVkcJd3d3xMTEICQkBA4ODjAwMIC1tTX69OmDo0ePKuzKWx3dunVDUlISNm3ahD59+nBlFAgEaNq0KXr16oXVq1fj3r17ci0O9+7dw8aNGzFkyBB4eHjA0tISfD4fFhYW6NChA5YtW4b79+9zXaDri4ULFyI2NhZTpkxB8+bNYWRkBBMTE7Ro0QKfffaZwn2tz7y9vXH16lWMHj0azZo1q5O1b42MjLBjxw7cuHEDISEh8PT0hEgkAp/Ph7m5Oby9vTFp0iTs27cPd+/erXY+v/76K7755hu0b98eIpFI6SREmhCJRLh8+TJWrlzJ9XowNTVFixYtMHfuXNy8eVPhPfkua926Ne7cuYM5c+bA1dUVhoaGsLKywocffojffvsNYWFhMuOFJeNONVHT54Wenh6+/vprrpw+Pj6wsLCAnp4ezMzM4OnpibFjx2LHjh1IS0uDkZGRXBohISH4888/0atXL9jY2FRrFnXyduExRgs3kf9XWFiIWbNmYcOGDQofEoS8K8rKypCcnCzzmZOTk9prJBJCCCH1zeTJk7Ft2zY0adJE4fhrQhTR9XciaiElhBBCCCHkLVdYWIiDBw8CADp06KDj0hCiPqqQEkIIIYQQUs8lJSUpXfKlrKwMISEh3MzlQUFBdVk0QmqEOmUTQgghhBBSz61cuRJRUVEYNWoU/Pz8YGNjg8LCQty6dQtbt25FTEwMgIq1Qvv376/j0hKiPqqQEkIIIYQQ8ha4e/euynWXAwICsGfPnmot+UKIrlCFlBBCCCGEkHpuwYIFcHNzw6lTp/D48WO8evUKpaWlsLS0RNu2bTFy5EiMGjVKK7McE1KXqEJKCCGEEEJIPffBBx9g4cKFWLhwoa6LQohW0U8ohBBCCCGEEEJ0giqkhBBCCCGEEEJ0giqkhBBCCCGEEEJ0giqkhBBCCCGEEEJ0giqkhBBCCCGEEEJ0giqkhBBCCCGEEEJ0giqkhBBCCCGEEEJ0giqkhBBC3nkpKSng8Xjg8XgIDw/XdXHeO+fOneOO/7lz53RdnLeao6MjeDweJkyYUKN0wsPDuXOSkpKilbK976p6ztAxJ0QxqpASQgghhBBCCNEJqpASQsg74G365V1bLTz1zbJly7hzQHTjXb221EU9AQghbyO+rgtACCGEkHdb165dwRjTdTHeCfX9BydCCNEUtZASQgghhBBCCNEJqpASQgghhBBCCNEJqpASQshbTDJ76cSJE7nPnJycuHFkVc1seurUKYwbNw5OTk4wMjKCUChE69atMX/+fKSlpanMOzU1Ff/973/h6+sLkUgEAwMDNG7cGF5eXhg9ejTCw8ORk5PDhe/atSt4PB4eP34MANixY4dcObt27Vqt41BWVobQ0FD4+flBKBRCJBLB19cX69atQ3FxsVppREZGYtGiRejatSsaN24MAwMDCIVCeHh4ICQkBHfu3FEYTzJ+d/ny5dxnlfer8tje8vJyREREYO7cuQgICICVlRX09fVhbm4Ob29vzJ07F0+ePKnWsZBWeUzl9evXMXr0aDRt2hQCgQBNmzbFhAkTcPfuXbXSO3z4MD766CM0adIEhoaGsLS0RMeOHfHVV18hLy9PabyqZtmdMGECeDweHB0dAQBZWVlYsmQJPD09YWJiAnNzc3Tp0gW7du1SmH51rq2ysjKEh4ejd+/e3Pk2NzeHq6srunfvjjVr1ig958qUlZVBJBKBx+NhwYIFCsOsWrWKK9PAgQMVhvnrr7+4MLdv35bZpmycLI/Hg5OTE/d+4sSJcsdg2bJlSsteXl6OLVu2wN/fHxYWFjAxMUGrVq2wevVqFBQUqHcAVIiIiMDo0aO5Z42xsTEcHR3RoUMHzJ07FxEREXJxKo+NLykpwbfffou2bdtCJBKhYcOG6Nq1K44ePSoTLzc3F9988w18fHwgFAphbm6Onj174syZMyrLmJaWhs2bN+Ojjz6Cq6srTExMYGhoCHt7ewwePBh79uxBeXl5jY8FIaQSRoiUgoICNnXqVFZQUKDrohBSq8RiMUtMTJT5E4vFui6Wxs6ePcsAVPl39uxZmXh5eXls6NChKuOYmpqyw4cPK8z3woULTCgUVpmvdPzAwMAqwwcGBmp8DHJyclhAQIDSNNu0acNiYmK492FhYXJphIWFVVk2PT09FhoaWq24AFhycjIXZ+nSpVWGNzY2Zvv379f4eEhzcHBgAFhQUBDbtm0b4/P5CvMyNDRku3fvVppOYWFhldeLnZ0di42NVRhf+jqtfC0yxlhQUBADwBwcHNjdu3eZo6Oj0nxmzJghF1/Tays3N5d17ty5yjjDhw/X+Jj369ePAWB+fn4Kt3fv3p1LXyQSKXzufPbZZwwAs7KyYuXl5TLbpM+pNHWuwaVLl3Lhpa/bhIQE1q1bN6Xx2rdvz/Ly8jQ+FhKzZ8+usmyWlpZy8aTLePPmTebn56c0/vr16xljjD1+/Jh5enoqDMPj8dgvv/yisIxisZg1aNCgynL27NmT5ebmKkwjOTlZ7eeM9POAEF3T9XcimtSIEELeYu3atUN8fDwOHjyIRYsWAQBOnjwJOzs7mXDSLSdlZWUYOHAgzp49Cx6Ph1GjRmHYsGFwcnJCaWkpoqKisH79ejx58gTDhw/HlStX0KZNGy5+cXExRo0ahZycHJiZmSEkJAQffvghbGxsUFpaisePH+Pq1av4888/ZcoQFhaG/Px89O7dG6mpqRg8eDBWrVolE8bExETjYzB27FhcvnwZANC+fXvMnj0brq6uePnyJcLDw7F3715MmzZNZRpisRgWFhYYNGgQAgMDudaR1NRUxMTE4Pvvv8fr168xc+ZMuLu7o1u3blzcIUOGoG3btti8eTN++OEHAEB8fLxcHvb29jL52draYujQoejYsSOcnZ0hEAjw9OlTXLlyBZs3b0ZeXh7GjBmDmJgYtGjRQuPjIi0uLg6//fYbbGxssGDBArRv3x5FRUU4duwYNmzYgOLiYq6lvH379nLxg4KCcODAAQBA69atMWfOHLRo0QKZmZnYvXs3wsPDkZqaiu7du+PWrVsy+6qJgoICDBo0CBkZGVi0aBF69OgBU1NTxMbGYvny5Xj27BlCQ0MxcOBA9O7dm4un6bW1bNkyXLx4EQAwYMAAjB07Fs2aNYNAIMCrV69w8+ZNHDlypFozJgcGBuLYsWOIjo5GXl4eTE1NuW2lpaW4evUq9z47OxuxsbFo27atTBqSVuTAwEC1yxAfH4/U1FTuuKxatQqDBw+WCWNjY6Mw7tSpUxEZGYmgoCCMGDECjRs3xpMnT/DNN9/g6tWriIqKwqpVq/Dll1+qVRZpR44cwXfffQcAaNWqFUJCQtCiRQuIRCJkZ2fj3r17OHXqlMxxUVbG6OhofPrppxg6dCgsLCwQFxeHxYsXIy0tDfPmzUOvXr0wYcIEPHr0CP/973/Rp08fmJiY4PLly1i6dCmys7Px6aefolevXnLHgv0z6Va3bt3Qt29feHl5wdraGrm5uXj06BG2bt2Kq1ev4tSpU5gxYwZ27Nih8bEghChRZ1Vf8lagFlLyvtD1r4Hapskv7+vWrWMAmL6+Pjt27JjCMJmZmVwrQ6dOnWS2nTlzhstLWQsqY4yVlpay7Oxsuc+VtfBUx6FDh7iy9OvXj5WWlsqFWb58uUwLh6KWi2fPnrH8/Hyl+WRlZbFWrVopPB4S0q2eVUlOTmYlJSVKtz99+pTZ29szAGzcuHFVpqeM5Fjjn9bHtLQ0uTARERFcy2nbtm3lth85coRLo3v37qy4uFguzJYtW7gwI0aMkNuubgspAGZubs4SEhLkwiQmJjKBQMAAsEGDBqnc36quraZNmzIA7KOPPlIZLiMjQ+V2Ra5du8bty/Hjx2W2Xbx4kQFgQqGQa9Vdu3atTJjMzEyupe7777+XS1/VPlbVQietcsu+opbDoqIi1rJlS64FU9H9VZVPPvmEu/6UtSwypvhYS5eRx+OxAwcOyIW5desW09PTYwCYtbU1MzQ0ZJGRkXLhjh49yqX17bffym0vLy9niYmJKvdlyZIlXFkePHggt51aSMnbStffiWgMKSGEvEdKS0uxfv16AMDMmTPRt29fheEsLCywdu1aAMClS5fw8OFDbtuLFy+41126dFGaF5/Ph1Ao1EaxlZK0SBoaGmLr1q3g8+U7/ixatAgtW7ZUmY69vT2MjY2VbheJRFixYgWAiuORkZFRg1JXjAPU19dXur1JkyaYN28eAODQoUNaWTJl/fr1aNy4sdznH374IaZMmQIAuHHjBq5fvy6zPTQ0FACgr6+PsLAwGBgYyKUxZcoU9OjRAwCwf//+Kscfq7JixQp4enrKfe7i4oIhQ4YAANe6WV2Sa7hz584qwzVs2FDjtNu0aQMzMzMAkBsve/78eS7f7t27Kwxz4cIFbpxidcdUa2rYsGEYN26c3OeGhoaYOXMmACAjI0PjMbXA/x9rX19fmdbiyqo61iNGjODOvzQvLy906tQJAPDq1SvMnj0bfn5+cuH69esHBwcHAIqvHx6PBxcXF5VlWLJkCaysrMAYw6FDh1SGJYSojyqkhBDyHomKiuIqCyNGjFAZVrqyKd2dztbWlnsdFham5RKqTywWc1/we/XqJddNWaJBgwYICgrSKO38/HykpKTg9u3bSEhIQEJCgkwF8ubNm9UvuAI5OTlITk6WyU9SQZZsqwkLCwu57pvSgoODudenT5/mXksf4549e6Jp06ZK05BUasVisdJJtKrC4/EwZswYpdslXcffvHmDrKysauUB/P81vGfPHq1M2CNNT08PAQEBAOQrm5L3Xbt25Sqbly5dQllZmVwYS0vLKn9I0ZaxY8cq3SbdXf/Ro0capy051hcuXEBSUpLmhfvHqFGjlG5r1aoV93rkyJFVhlNnP8rLy5Gamor79+9z9+Tdu3fRpEkTANp/BhDyPqMxpIQQ8h65ceMG97pjx45qx5NuFe3UqROcnZ3x6NEjzJo1C7t27cLQoUMRGBiItm3bKmxBqw1JSUlcZaJdu3YqwyoaF1nZ69ev8e233+LPP/9EYmKiylbJ169fa1ZYBR4/fox169bh8OHD3OywqvJzdnaudl4+Pj4KW48lvL29YWBggJKSEiQkJHCfP3r0iDvGilqdpElvl05DE1ZWVrC0tFS6XboVLTc3F+bm5tXKJygoCCtXrsSVK1fg5OSEjz/+GN27d0enTp1gbW1drTSlBQYG4sSJEzLjSEtLS3HlyhUAFRXSVq1awcjISG4cqaRC2qVLl2qNYa0Od3d3pdsqH3NNjR8/Hjt37kRGRgZatmyJwYMHo3fv3ujcuXOVLZLS3NzclG6Tvg7UCadsPxhj2LVrF7Zt24Zr166hsLBQaVraeAYQQipQCykhhLxH0tPTqxVPuhVJX18fhw8f5ibauX79OhYuXIiAgACYm5ujb9+++O2332RafWrDmzdvuNfKJmuRaNSokcrt0dHRcHd3x5dffokHDx5U2UVW1RdVdRw/fhweHh7YtGlTlZVRbeRX1fHh8/lcxSMzM5P7XPp1VcdQujuwdDxNqOo2DVS0dkvU5PpavHgxgoODwePxkJ6ejtDQUAwbNgyNGjWCl5cXli5dipcvX1Y7fUnrp1gsxqVLlwBU9E4oKCiAUCiEj48PDAwMuB+FJJXQrKws3Lp1C0BFpbauqDruNT3m3bt3x6ZNm2BkZISioiLs2bMHwcHBcHV1RZMmTTB9+nS1WhvVLaM64RTtR1FREfr3749PPvkE586dq/Keq+k9SQj5f1QhJYSQ90jlroHx8fFq/YWEhMik4+Hhgfj4eBw4cADBwcFo3rw5gIovaSdOnMDYsWPh5+dX7QqwOqQrjVW1JKmqYJaUlGDEiBHIyMiAvr4+/vOf/+D8+fNIS0tDUVERGGNgjMl0N6zJmM6MjAyMGTMGBQUFMDU1xbJly3D16lWkp6ejuLiYy096zcSajiFVp6WtqjzqqrWuLujr62Pbtm1ISEjAokWL4O/vDwMDAzDGkJCQgBUrVsDFxQUHDx6sVvpt27blxktKKpvS40f19PQA/H/FVRJGF+NH68KMGTOQkpKC7777Dv369YNIJAIAPH/+HD/99BN8fHy4WcJ1ZfXq1Th+/DiAih8D/vjjDzx8+BB5eXkoK+BGjfUAACAASURBVCvj7kvJuGNtjOsmhFSgLruEEPIeke4OaWBgUKMxanp6ehgyZAg30UhaWhqOHz+OzZs3Izo6GtHR0Zg2bRq3XIi2SXclrKo1S1XFOCIightTFhoayo2FrEy6RbYm9u7dy41/3L9/P3r27Fmr+QFVHx+xWMzlJ31cpV9Ld9tWRHp7dSYD0gUPDw+sXLkSK1euRGFhIS5fvozffvsNO3fuRF5eHkaPHo2kpCSZcdPq4PP58Pf3x99//81VNqXHj0pIXl+8eBFlZWVcGAsLC3h5edVw7+oXGxsbzJo1C7NmzUJ5eTni4uKwf/9+hIaGIisrC6tXr0a7du1UjnWuLYwx/PzzzwAqhiRERETItLpK0+Z9SQipQC2khBDyDlC39crHx4d7/ffff2u1DLa2tggODsbVq1fh6+sLoGINwspd27TV0ta8eXOue17lmWErU7X99u3b3GtVE6dIj79VRN39kuTXsGFDpZVRdfLTRFxcHMRisdLtN2/eRElJCQDI/Ejh7OzMHeNr166pzCMqKop7XVeT8VRWk2vLyMgIPXr0wPbt27kZpgsLC3HkyJFqpSfpchsdHY03b97IjB+V8PPzg5GREXJychAbGyszflRZhUiVt6UVu0GDBvD19cWqVatkegL88ccfOilPZmYm94PKiBEjlB77vLw83L9/vy6LRsh7gSqkhBDyDhAIBNzr4uJipeE6derEtV79+OOPyMnJ0XpZ9PX1uS/jYrFYbjZUSVlVlVMdfD6fy+fvv/9WutRIeXm5ykXspStqymZcLS8vx5YtW1SWR91zIMmvuLiY655ZWUFBAXbu3KkyP01kZmbi8OHDSrdv376dey1ZvgWQPcanTp3C06dPlaYhaWHS09PTWXdTbV1bkiVZgOpPXiM9jvS7775Dfn4+N35UQnoc6V9//cWNpazu+FF1r8H6xNfXFxYWFgB0N1GQOs8AANi2bRtKS0vrokiEvFeoQkoIIe8A6S6FqpZWEAgEmDt3LoCKLpajRo1Cfn6+0vC5ubnYtGmTzGcXL16UWZe0spKSEm68nKmpqdyspZKy1mQJCAnJ2Nbi4mJMmzZN4WQlX375JeLj45Wm4erqyr1WVnFdsGABYmJiVJZF3XMgyS8/Px/79u2T215WVobJkycjNTVVZX6a+s9//qOw6+758+e5ynabNm3kZiyeMWMGgIo1bIODg7mWVGnbt2/nWtyHDx+ucRdXbVHn2srMzKxybVfp3gNOTk7VKku7du241uXvv/8egOz4UQlJxXXTpk01Hj9qaWnJzXKtjftLG/bs2aNyAqAbN25w3WCre6xrytrampuBd/fu3Qqv8evXr+t8nCsh7yoaQ0oIIe8AHx8fCAQCFBUVYfHixeDz+XB0dOS6ntnb28PIyAgAMH/+fJw5cwZnzpzhZnudPn06OnbsCHNzc+Tm5uL+/fs4d+4c/vrrLwgEAsycOZPL68yZM1i5ciU6d+6M/v37o1WrVrC2tkZhYSEePHiAH3/8kau8TZ48WW65EX9/f5w9exbXr1/HV199hb59+8LExARARbdJe3t7tfd74MCBGDhwIA4fPozDhw8jICAAs2fPhqurK9LT0xEeHo49e/agXbt2Srvt9u7dGzY2NkhPT8cXX3yBx48fY9CgQbCyssLDhw+xdetWnDlzBgEBAbh8+bLSsvj7+3OvZ8+ejS+++AK2trZcN0pHR0fw+XyMGDECCxcuRHFxMSZMmIC4uDj06NEDQqEQt2/fxsaNGxEdHV1lfppo3bo17ty5gzZt2mDBggVo3749iouLcezYMXz33XcQi8Xg8/kIDQ2Vi9u/f398/PHH2Lt3L06fPg0/Pz/MmTMHLVq0wJs3b7B7926uhbVhw4b49ttvtVLm6lDn2srJycHgwYPh6OiIYcOGwc/PDw4ODuDz+UhLS8Phw4e51t4mTZpg4MCB1SqLvr4+/P39cfr0aWRnZwNQXNGUfCYJY25ujtatW1crTz6fj3bt2uHy5cvYvn07fHx84O3tza2h27Bhwzof3/v5559j+vTpGDx4MLp06QI3NzeYmJggIyMDly5dwsaNGwFUtKwrG79d2xo0aICxY8ciNDQUcXFx6Ny5M2bPng0XFxdkZ2fj2LFj2Lx5M0xNTWFnZ4cHDx7opJyEvLMYIVIKCgrY1KlTWUFBga6LQkitEovFLDExUeZPLBbrulg1Mn/+fAZA4d/Zs2dlwhYUFLDx48crDS/95+TkJBN36dKlasUbNmwYKywslCvns2fPWMOGDRXGCQwM1Hi/c3JyWEBAgNJy+Pr6spiYGO59WFiYXBonTpxgAoFAaRpdu3ZlCQkJKtNgjLERI0YoTSM5OZkLt337dtagQQOlYUeOHMlOnz6t9Pypy8HBgQFgQUFBbOvWrYzP5yvMz8DAgP3+++9K0yksLGRDhw5Veb7t7OxYbGyswvhnz55VuS9BQUEMAHNwcFC5P2FhYQqPp4Q611ZycrJa16+9vT2LiYlRWZ6qrFy5UibN69evy4UpLi5mRkZGXJiBAweqTFP6nCpy5MgRxuPxFO7T0qVLuXBVHUsJ6eOl7LpXp7yq/gQCAduxY4dcXHXLKP1MUkXVdZaVlcW8vb2VlrFhw4bs/PnzLDAwUOmzqqpjpe7+EFLXdP2diLrsEkLIO+Krr77C1q1b0blzZzRs2FCua6A0IyMj7NixAzdu3EBISAg8PT0hEonA5/Nhbm4Ob29vTJo0Cfv27cPdu3dl4s6fPx/Hjh3D7Nmz0aFDBzRr1gwCgQACgQCOjo4YOXIkjh49ij///FNmTJuEvb09oqKiMGnSJLi4uCgMowkzMzOcO3cOGzduRLt27WBqagozMzN4e3vjyy+/xOXLl7kxasr07t0bN27cwLhx42BnZwd9fX1YW1sjMDAQW7ZswZkzZ7iWNlV+/fVXfPPNN2jfvj1EIpHSyVEmTpyIixcvYsiQIbC2toa+vj5sbW3Rp08f7NmzB7t371Z5/qpj8uTJuHjxIkaMGAE7OzsYGBjA3t4e48ePR2xsrMoJnQQCAfbv349Dhw5h2LBhXHwLCwv4+fnhyy+/xP379+Ht7a3VMmtKnWvLwcEBcXFxWLt2Lfr27YsPPvgA5ubm4PP5sLKyQmBgINatW4e7d+/KjPesDukWUZFIpDA9AwMDmdb1mq4/2r9/f5w5cwaDBw/mrmVdunDhAn7++WeMHDkSXl5esLa2Bp/Ph1AohK+vL+bNm4c7d+5g/PjxOi2nSCTC5cuXsXLlSnh5eUEgEMDU1BQtWrTA3LlzcfPmTXTp0kWnZSTkXcVjjBZSIv+vsLAQs2bNwoYNG7jufYS8i8rKypCcnCzzmZOTk9YrAYTokqOjIx4/foygoCCEh4frujiEEELqIV1/J6IWUkIIIYQQQgghOkEVUkIIIYQQQgghOkGz7BJCiBYwxpCbm4uioiIIBAKYmZm9NYvUE0IIIYToClVICSGkBl6+fIlr167h4cOHyM3N5T43MzODi4sLOnToABsbGx2WkBBCCCGk/qIKKSGEVINYLEZERAQiIyOhaG643NxcxMbGIi4uDh06dEC3bt3k1uMkhBBCCHnf0bcjQgjRkFgsxu+//45Hjx5VGZYxhqtXryI9PR2jRo2iSimpUykpKbouAiGEEKISTWpECCEaioiIUKsyKi0pKQkRERG1VCJCCCGEkLcTVUgJIUQDL1++RGRkZLXiRkZGIj09XcslIoQQQgh5e1GFlBBCNHDt2jWFY0bVwRirdmWWEEIIIeRdRBVSQghRE2MMDx8+VLit8hIvypZ8efjwYbUrtIQQQggh7xqqkBJCiJpyc3NllnaRVrmSqazSqSoNImvZsmXg8Xjv/HquXbt2BY/HQ9euXXVdFEJqVUJCAsaNG4emTZvCwMCAu7/j4uK0nldKSgqXfnh4uNbTf99IjuWyZctqLY8JEyaAx+PB0dGx1vIg9RNN90gIIWoqKirSWjpCoVAraRFCyNsgOjoanTt3RmFhoa6LQgipZ6iFlBBC1CQQCOpVOoQQWXXRilPf1deWwQULFqCwsBBCoRCbN29GVFQU4uPjER8fD3d3d7XTcXR0BI/Hw4QJE2qvsDoQHh7OnTdaronUVH19DihDLaSEEKImMzMzmJmZKexyy+PxZLrpVn5fOQ1CCHlflJaW4vz58wCAqVOnIiQkRMclIpqqi7kPwsPD34rKE9E+aiElhBA18Xg8uLi4KNym7hhSFxeXd35MJCGESHv9+jVKSkoAAG5ubjouDSGkvqEKKSGEaMDPz6/aFUoej4cOHTpouUSEEFK/FRcXc6/19fV1WBJCSH1EFVJCCNFAo0aNql2p7NChA2xsbLRcIvVVnsHwxYsXmDt3Ltzc3GBsbAx7e3uMGDECt2/flomXkpKCf//733Bzc4ORkREaNWqEsWPHIikpqco8S0pKsHnzZnz44YewtraGgYEBGjdujH79+uHXX39FeXl5jfdLLBZj27Zt6NevH+zs7GBoaAgrKyt06dIFGzZsUGsyqvLycvz+++8YPnw4mjVrBiMjI1haWqJ169YIDg7GiRMnIBaLZeKoOyOktsaGvXnzBqtWrULHjh1hZWUFQ0ND2NnZYfDgwdi/f3+V8Q8cOIAhQ4agSZMmMDQ0hJmZGZydndG5c2csXrwYUVFR1SpX5eOQlZWFJUuWwNPTEyYmJjA3N0eXLl2wa9cutdKLj4/H1KlT4erqCmNjY5iZmcHT0xOzZ89Wevwk4wolli9fzh1zyZ+mYw4rn7fi4mKsW7cOvr6+EIlEEAqF8PPzQ2hoKMrKypSmU15ejoiICMydOxcBAQGwsrKCvr4+zM3N4e3tjblz5+LJkycqy1J5JubExETMnDmTO0aSMvJ4PDg5OXHxJk6cKHccqju+tjr3smSmbFVlUrc8kmPw+PFjAMCOHTvk9q2qmapPnTqFgQMHonHjxjA0NISTkxNCQkLw7NkztcoQFRWFKVOmwM3NDaampjAxMYG7uztmzJiBxMREtdKo7Ny5c+DxeJg4cSL3mZOTk9y+nTt3jtte+Z5LS0vD559/Dk9PT5iZmcmFf/PmDcLCwjBu3Dh4eHjA1NSUO3+9e/fGli1buBZsZVSdr8r3Snl5ObZs2QJ/f39YWFjAxMQErVq1wurVq1FQUKA0j6qeqZXLcP36dYwePZp7ptnb2+OTTz7B3bt3Ve4LAOTn52PFihXw8vKCiYkJLC0t0alTJ2zfvh2MMe68VD6WmoqIiMDo0aPh5OQEIyMjGBsbw9HRER06dMDcuXMRERGhMv79+/fx73//G56enhCJRDAyMoKzszMmTpyImJgYhXFq8zlQaxghUgoKCtjUqVNZQUGBrotCSK0Si8UsMTFR5k8sFqsVt7S0lO3cuZMtW7ZM7b9ffvmFlZaW1vJeqRYUFMQAMAcHBxYXF8caN27MAMj9GRsbs4sXLzLGGDtz5gwTiUQKw1lYWLCEhASl+aWkpLAWLVoojCv569SpE8vIyFAYf+nSpVw4ZR4+fMg8PDxU5uHq6soePHigNI3k5GTm7e2tMg0A7OzZs0qPpyphYWFcGsnJyXLbAwMDGQAWGBioNI2jR48yc3NzleXr378/y83NlYsrFovZxx9/XOX+tWnTRuV+KCN9HO7evcscHR2V5jFjxgyVaa1Zs4Y1aNBAaXxDQ0O2Y8cOuXgODg5V7l9QUJBG+yV93mJiYlibNm1UXsc5OTkK05G+jpX9GRsbs/379ysti/Q18tdffzETExO5NJKTk6vMBwBbunSpRseBserfy+rsu7rlkRwDVX/S95D08QgLC2Off/650njW1tbszp07SvMuLS1lISEhKvPW19dnW7Zs0fjYnj17Vq3zJv38kb7nrl69yqysrFSGV+f+8PHxYWlpaUrLqep8Sd8rCQkJrFu3bkrzad++PcvLy1OYR1XPVOkybNy4kfH5fKX30/nz55Xuy5MnT5iLi4vSMg4YMID9/fffCo+lJmbPnl3lcbe0tFQaf8WKFUr3EQDj8XhsyZIlSo+TJvddTb4TaQNNakQIIRri8/kYPXo0IiIiEBkZqXKyB0k33W7duoHPrx+P3IKCAgwdOhQlJSVYs2YNAgMDoaenhxMnTmDNmjUoKCjAJ598glOnTmHo0KEQCoVYsWIF/Pz8IBaL8eeff2LDhg148+YNJk2ahMjISLk88vLy0K1bNzx69AgAMGTIEAQHB8POzg7JycnYtGkTzp8/j0uXLmHAgAG4ePEi9PT0NNqPtLQ0BAQE4OXLlzAzM8PUqVPRo0cPNGrUCNnZ2fj777/xv//9D4mJiejTpw9iYmIgEolk0nj58iUCAgKQmpoKAOjWrRuCgoLg7u4OHo+H5ORkREREYO/evdU82jV36tQpDBo0CGVlZXB0dERISAj8/PwgFArx/Plz7NmzB7/++iuOHj2KoKAg/PnnnzLxf/jhB678nTp1wuTJk9G8eXOYmpoiMzMTCQkJOH78ODIzM2tUzoKCAgwaNAgZGRlYtGgRevToAVNTU8TGxmL58uV49uwZQkNDMXDgQPTu3Vsu/ubNm7Fw4UIAgLW1NT7//HMEBASgrKwMp0+fxtq1a5Gfn48JEybAysoK/fr14+L+/fffKCkpgZeXFwAgJCQEn376qUz6FhYW1d63adOmITo6GiNHjkRQUBBsbGzw4MEDfPfdd7h+/TouXbqEsWPH4tChQ3JxxWIxbG1tMXToUHTs2BHOzs4QCAR4+vQprly5gs2bNyMvLw9jxoxBTEwMWrRoobQcT548wbhx42BsbIzFixejc+fO0NPTw/Xr12Fqaor4+HikpqZyx3fVqlUYPHiwTBqa9tKoyb386aef4qOPPlJZJnXLExYWhvz8fPTu3RupqakYPHgwVq1aJRPGxMREYdytW7fiypUrCAwMxLRp0+Dm5oasrCzs3LkTO3fuxKtXrxAcHIyrV68qjD9p0iTs3LkTANC3b1+MHTsWbm5u3BqqGzZswO3btzF16lQ0btwYAwcOVGufAKBdu3aIj4/HwYMHsWjRIgDAyZMnYWdnJxNOusVLIi8vD8OHD0dRURG++OIL9OzZE8bGxoiPj4etrS0XrqysDH5+fhgwYAB8fHzQqFEjlJSUIDk5Gb/++itOnDiB2NhYjBo1qkatgUDFpFWRkZEICgrCiBEj0LhxYzx58gTffPMNrl69iqioKKxatQpffvlltfM4efIkrl27hlatWuGzzz6Dl5cXCgsLceDAAfzvf//j/oclJibCwMBAJm5JSQn69euHhw8fAqg4n1OnTkXTpk3x7NkzbNmyBUeOHMGrV69qdByOHDmC7777DgDQqlUrhISEoEWLFhCJRMjOzsa9e/dw6tQppdfckiVLsHLlSgCAv78/goOD4enpCX19fdy/fx+bNm3C1atXsWLFClhZWeFf//oXF7c2ngO1rs6qvuStQC2k5H2hrV8DX758yQ4ePMjWr18v0yK6fv16dvDgQfby5ctaKH31SH59BsCsrKzYw4cP5cKEhobKtBq4urqy9PR0uXDz5s3jwsXExMhtnzt3Lrd90aJFctvLy8vZ2LFjuTCbN2+WC1NVC+mAAQMYANa0aVOWlJSkMExMTAzXmqSoHEOGDOHy+PrrrxWmwRhjeXl5LDMzU+azumghzcvLY40aNWIAWK9evVh+fr7CPLZs2cLlcfr0aZltnTt3ZgCYn5+fylZ6ZS3VVZG+rszNzRW2micmJjKBQMAAsEGDBsltT09PZ8bGxgwAs7OzY0+ePJELI30u7e3tWUlJiVwYKPn1vzqkzxsAtmbNGrkwpaWlrHfv3lyYI0eOyIVJTk5WWFaJp0+fMnt7ewaAjRs3TmEY6dZBOzs79vjxY6XpVW4ZrClt3MvaLJOkta+qFu/KLcZTpkxh5eXlcuEmT56s8lm2b98+bvvWrVsV5lVYWMi1Cjo6OlarN0xVzwlp0vecqakpi4uLUxleVQ8Rxhjbvn270ueHhKp7q/K98ssvv8iFKSoqYi1btuRaBRUdI3VbSAGwfv36seLiYrkwq1at4sIo6nXw7bffcttnzpypMJ+ZM2fK5FWdFtJPPvmE2xdFPVckFD13o6KiuJ4iiu45xhgrKytj48aNYwCYmZkZe/Pmjcx2Te85XbeQUoWUyKAKKXlfaPvhW15ezrKzs9nLly9Zdna2wi8+uib9JeaHH35QGKagoICrOABgJ06cUBju0aNHXJj//e9/MtuKioq47qUeHh5Kj2t2djaztLTkwlWmqkIaHx/PbTt48KDK/Z4/fz73RV7a3bt3GY/HYwDY4MGDVaahSF1USDdu3MgAMIFAUOWPG+3bt2cA2NixY2U+d3V1ZQDY7Nmzq9qlapG+rr7//nul4UaNGsWAiq7elX399ddcGr///rvSNKS/bP7xxx9y22urQtqqVStWVlamMNzTp0+Zvr4+9yW5OjZs2MAAMKFQqPDZIV0h3blzp8q0tFn509a9rOsKqa2tLSsqKlIY7t69e0qfZYwxrqv20KFDVeZ3584dLp1Tp06pvT8S1a2QrlixQuO8FPHx8VFZSVO3Qjps2DClefz4449cuJs3b8ptV7dCqup5mJOTwwwMDJQ+8z744APu/0FhYaHCNAoLC5mdnV2NKqQ9e/ZU67pRZPjw4QyoGEah6rvEmzdvmKGhocIfS962CilNakQIIVrA4/EgFAphY2MDoVBYr5d24fF4GDFihMJtRkZGcHV1BVDRxbFXr14Kwzk5OXHrqUq68klER0cjKysLQMUkFcq64gqFQq4cd+7cQVpamtr7cPDgQQCAsbEx+vfvrzJsly5dAACpqal4+vQp9/mxY8e47tazZ89WO++6JNnPwMDAKrtYSfazchcwSde9w4cP4/Xr17VQygo8Hg9jxoxRur1NmzYAKiZYkVwfEqdPnwYAmJubY/jw4UrTmDx5slycuhAUFIQGDRR/ZWrSpAl3n5w7d07lBEcAkJOTg+TkZNy+fRsJCQlISEiAsbGxzDZlDAwM8PHHH1dzLzRXF/dyXfjoo49gaGiocNsHH3wAU1NTAPLPsufPnyM6OhoAlD4zJVq0aAErKysA8vdgbRo7dqxG4RljePHiBR48eMBdfwkJCVwX4Zs3b9ZaeSTPAED+WGuiZ8+eSp+HZmZm3P8wRefz/v37ACrOp0AgUJiGQCCo8X0mee5euHBBrQkAJUpLS3H8+HEAFdetqu8S5ubm3DCFurzmagNVSAkh5D1jZWWFhg0bKt1ubm4OoOo1UyXhcnNzZT5PSEjgXvv5+aksi/R26XhVuXHjBoCKcYt8Pl9uBkHpvwEDBnDxXrx4wb2OjY0FULEMRX1djkeynydPnlS5jzweD+vWrQMgu49ARWUKAB4+fAgXFxcEBwfj999/V3tmUXVZWVnB0tJS6Xbpa07ZNePj46NyWZBGjRpxM3Bqcr3UVLt27VRub9++PYCK61HRF+3Hjx/jX//6FxwdHSESieDs7IyWLVvCy8sLXl5emDp1KhdW1Y8Grq6uSr9E14a6uJfrgru7u8rtkvHFla9Lyf0HAKNHj67yHpScu8r3YG0xNTWFs7OzWmGPHj2KAQMGQCQSwdbWFh988AF3/Xl5eeHo0aMAVF9/6lB1rFU9A7SVh3Q+qv43SVeOFWnbtm01S1dh/PjxAICMjAy0bNkSo0aNQlhYGDd2VZk7d+5wMxEvWLCgymtOco3W1TVXW6hCSggh7xlJa4wykpYgdcNVbhGSnhynUaNGKtNo3LixwnhVSU9PVzusNOklByRfvBo2bKi09USXSktL5VoS1VF5WYXg4GAsXLgQfD4f2dnZCAsLw5gxY9C0aVO4uLhg7ty5NWqtkFD3egGUXzNVXS/A/18zNZ2ESRNVtU5Ll7tyuY4fPw4PDw9s2rSJW7JElcLCQqXbajIxU3XUxb1cF6r7LNPGc6Y2SX4UVIUxhsmTJ2PAgAE4evRolRVBVdefOlQda1XPAG3lIZ1P5TzevHnDva7qnra2tq5m6Sp0794dmzZtgpGREYqKirBnzx4EBwfD1dUVTZo0wfTp0xW2Rtf3a6621I8pHwkhhLyTquq6LOkyqynJFw0nJyeFM5sqo2imyvravVr6y9SIESOwePHiaqe1evVqTJ06Fbt27cKZM2cQGRmJgoICJCUlYf369fj+++/x/fffY/r06dooerWpcy6qe83URHWv44yMDIwZMwYFBQUwNTXF3Llz0bt3bzRv3hwikYibATQiIgLdu3dXmRYAjWei1qbaupfrM+l7cNeuXWjVqpVa8erqhwN1roft27dj27ZtAABvb2/MmjULfn5+sLe3h7GxMZfG+PHj8csvv7yT51FXZsyYgY8//hi//fYbTp06hcuXLyM7OxvPnz/HTz/9hC1btmDhwoUys0VLX3Nr165Fnz591MpL2QzTbwuqkBJCCNEq6W5ZL168gJubm9KwL1++VBivKpKuoS9fvoS7u3u1ltSRjPfKyMhASUmJ3PIAVZH8Cl9eXq4yXH5+vsZlAyrGMRkbG6OgoABZWVlo2bJltdKRcHBwwMKFC7Fw4UKUlpYiKioKe/fuxU8//YSioiJ8+umn8PPzg4+PT43yqY6GDRsiLS1NrW5nkmtGk+ulpl6+fKnyOpZu1ZAu1969e7lW7v3796Nnz54K40u33NQndXEv12fSXdB5PF6N70Fd2Lp1KwCgefPmuHLlCoyMjBSGq6/XoDZJ/1BQVUtkTZd9kbCxscGsWbMwa9YslJeXIy4uDvv370doaCiysrKwevVqtGvXjluWRfqaKy0tfSuvueqgLruEEEK0Svof6LVr11SGjYqKUhivKpJKU0FBAS5fvqxhCSv4+voCqPinX50JISSTOlXVrVYyiUZ1SPbz8uXLWu2Spa+vj4CAAGzYsAG//fYbgIoWrn379mktD01Izn1sbCxKS0uVhktPT+e6vdblF7Xr16+rJhlHrwAAIABJREFUtd3Y2FhmTN/t27cBVFTQlFVGAdmxijWlzRb/uriXNVWXPRqkf5z5+++/azWv2tovyTU4ePBgpZVRxhhiYmJqJf/6xNPTk3td1T2nzXtSokGDBvD19cWqVatw5swZ7vM//vhDpoySH0drcs3V154/ylCFlBBCiFa1adOGG9u0Y8cOpWOFcnNzuX/EHh4eMgu5V0V6ke9vvvmmWuXs378/909bsoC5JiTdf3Nzc5VWOktKSvDnn39Wq3wAMGjQIAAVrayhoaHVTkcVSVdRoOYTmlRXjx49AFRU7lUdr23btnFdCiVxpEkm/CkuLtZq+VR1ZXz+/Dn3xbFr164y3SjFYjFXHmUt6QUFBdi5c6fWyio96VFNj0Nd3Muaqq1zrIiLiws8PDwAALt378aTJ09qLS9tnjdpkmtQ1Q9ahw4dQmpqqtbyrK+aNGnCtfLv3bsXRUVFCsMVFRVh7969tVoWX19frsVW+rlrbGzMPZPPnTsn80OPJmrreqotVCElhBCiVYaGhtzyHLdv38by5cvlwjDGMHPmTO4f8cyZMzXKo127dtxSG8eOHcPSpUtVhk9JScHvv/8u85mbmxuGDh0KoGJ5lbVr1yqNn5+fL9elLTAwkHu9fv16uTiMMXz22Wc1+qI3ffp0rmvx4sWLueUAlLl8+TIuXLgg89mvv/7KfSlVRPpXeEVjbOvCxIkTuYlK5syZI7M8j8TNmzexZs0aAIC9vT2GDBkiF0ZSEdJkmQV1xMXFKbw+xGIxpkyZgpKSEgBASEiIzHbJ8hP5+fkKW5/LysowefJkrVYGLC0tuRaWmh6HuriXNVVb51iZRYsWAaiopAwbNkxlV87i4mJs3rxZaUVHFelKvDb3TXINHj58WGG33KSkJHz66aday6++mzZtGoCKZcDmzZunMMy8efNqfE/u2bNH5QRRN27c4M5H5efuF198wf1YOmrUKJXXQ1lZGX777Te5WdO1+RyoCzSGVA35+fl4+PAhsrKyUFhYCJFIBGtrazg7Oytdl4wQQt5nS5Yswf79+/Ho0SOsXLkSCQkJCA4Ohp2dHZKTk7Fp0yacO3cOANCxY0eZZS/UFRYWhrZt2yItLQ0rVqzAyZMnERwcDC8vLwgEAmRkZODWrVs4ceIEIiIiMGTIEIwePVomjc2bNyMyMhKpqamYP38+Tpw4gQkTJsDd3R08Hg8pKSk4d+4cdu/ejX379qFr165cXB8fH3To0AGRkZHYunUrSkpKEBQUBJFIhMTERPz44484d+4cOnbsWO014oRCIX7//Xf07dsXxcXFGDBgAIYPH47hw4ejefPmAIC0tDRER0fjwIEDuHXrFjZu3MitSQoAn3zyCebOnYthw4bB398fzZs3h0AgwMuXL3Hq1Cn88MMPACqWkBg3bly1yllT1tbWWLt2LWbMmIHU1FS0bdsW//3vf+Hv74+ysjKcPn0aa9euRV5eHng8HrZs2aJweRh/f38kJyfj0KFD+OmnnxAQEMC1FEjWCa6Otm3b4vPPP0dcXBzGjx8PGxsbJCYm4ttvv+VaMAYOHCizxBBQMRnVwoULUVxcjAkTJiAuLg49evSAUCjE7du3sXHjRkRHRyMgIKDaXc8r4/P5aNeuHS5fvozt27fDx8cH3t7e3PFq2LChRmM86+Je1oS/vz/Onj2L69ev46uvvkLfvn25CV2MjIxgb2+v1fxGjx6NkydPYseOHYiOjoaHhwemTZuGwMBAWFtbIz8/H0lJSbh48SL279+PzMxMbskPTfj4+EAgEKCoqAiLFy8Gn8+Ho6Mj9z3T3t5eaZdbVcaPH4958+bh+fPn8Pf3x/z58+Hp6YmioiJERERgw4YNKC4uhq+v73vRbXfmzJkICwtDQkICNm3ahEePHmHatGlo0qQJnj17hi1btuDo0aNo3749d29Xp/vr559/junTp2Pw4MHo0qUL3NzcYGJigoyMDFy6dAkbN24EUDEx1ZQpU2TiBgQEYMmSJVi+fDmSk5Ph7e2NSZMmoVevXrC1tUVxcTFSUlJw9epV7Nu3D6mpqYiPj0eTJk24NLT9HKh1jCj14sULtnnzZvbpp5+yqVOnyv3NmzePHTp0iJWWltZaGdatW6cwb3X+7ty5o3F+BQUFbOrUqaygoKAW9oaQ+kMsFrPExESZP7FYrOti1aqgoCAGgDk4OKgMFxgYyACwwMBAleEcHBwYABYUFKRwe3JyMnN3d2cAlP4FBASwjIwMhfGXLl3KhVMmJSWFtWvXTmUekr+JEycqTCMpKYm1bNmyyvhnz56Vi3v37l1mY2OjNM5//vMfFhYWxr1PTk6WS0Od433mzBnWuHFjtfZzx44dMnHViWNubs5OnjypNH9V1L2uqjoOjDG2evVq1qBBA6XlNDQ0lNs/abGxsczQ0FBhXGXXqTrljYmJYT4+Piqv45ycHIXpbN++XeU+jRw5kp0+fVrldabuPSlx5MgRxuPxFOa3dOlSjY4DYzW/l5OTk7lwYWFhGucv7dmzZ6xhw4YKyyB9fDTJs6pnmVgsZvPnz2d6enpV3ksmJibV/g41f/58tZ4/6t5zjDFWUlLCevXqpTRdIyMj9scff1SZpqrrR517m7Gqz0lNyiCtqvvl8ePHrHnz5kqPSa9evdjx48e595GRkSrzU0RyTan6EwgEKp9n3333ndLnmfSfgYEBS0xMlIuvyXNA19+JqHlPicjISKxevRpxcXFKuzplZ2fjyJEj+Oqrr3Q27oYQQuorR0dH3Lx5E5s2bUJgYCAsLS2hr6+PRo0aoU+fPvjll19w4cKFGv1K6+DggGvXruHAgQMYNWoUnJycYGxsDH19fVhbW8Pf3x9z5szB+fPnuaUPKnN2dkZcXBzCw8PRv39/2NrawsDAAFZWVmjdujWmTJmC06dPy7Q6Sri7uyMmJgYhISFwcHCAgYEBrK2t0adPHxw9elRhV97q6NatG5KSkrBp0yb06dOHK6NAIEDTpk3Rq1cvrF69Gvfu3ZNrnbl37x42btyIIUOGwMPDA5aWluDz+bCwsECHDh2wbNky3L9/n+sCrUsLFy5EbGwspkyZgubNm8PIyAgmJiZo0aIFPvvsM4X7J83b2xtXr17F6NGj0axZM62tL2thYYErV67gyy+/hLe3N8zMzGBqaop27dph48aNOH/+PDfJVWUTJ07ExYsXMWTIEFhbW0NfXx+2trbo06cP9uzZg927d2t9OZf+/fvjzJkzGDx4MOzs7BS2JmuiLu5lddnb2yMqKgqTJk2Ci4uLzFi52qKnp4evv/4ad+7cwZw5c+Dj4wMLCwvo6enBzMwMnp6eGDt2LHbs2IG0tLRqtWQCwFdffYWtW7eic+fOaNiwoVauC319fRw9ehTff/892rZtC2NjYxgZGcHFxQXTp09HTEwMPv744xrn8zZp1qwZbt68ieXLl6Nly5YwMjKCubk5OnTogM2bN+P48eMy3a5FIpHGeVy4cAE///wzRo4cCS8vL1hbW4PP50MoFMLX1xfz5s3DnTt3VD7PZs2ahaSkJCxevBgdOnSAlZUV+Hw+TExM4ObmhuHDh+PHH3/E8+fP4eLiIhdf28+B2sRjjBYcqiw+Ph6hoaEyExjY2NjA3d0dxsbGePXqFW7duiUzE6CtrS0+//zzaj+ElFm/fj0ePHjAvdeki/Bnn30Gd3d3jfIrLCzErFmzsGHDBq3vCyH1SVlZGZKTk2U+c3Jy0uk6f4SQ+iM8PBwTJ04EACQnJ8PR0VG3BSKE1JlVq1ZxXadzc3Pr5IcPXdL1dyIaQ1pJdnY2fv75Z64yyuPxMHz4cHTv3l2mMpibm4stW7ZwlcW0tDTs2rWLG/xfG9zc3DBnzpxaS58QQgghhJD3GWMMe/bsAVDR6+Jdr4zWB9Rlt5KjR4/KNNMPHDgQPXv2lGuZNDMzw7///W+ZWdFu3LhRq1OCE0IIIYQQQqovJSVF5czjS5YsQUJCAgAgKCioror1XqMKqZScnByZWe4k44CU0dfXx6hRo7j3jDEcO3asVstICCGEEEIIqZ7w8HA0b94cCxYswJEjRxATE4Nr164hPDwcH374IVatWgWgYk3dyjPgktpBXXalVJ7AqHPnzlX2nXZ3d0fjxo3x4sULAEBCQgKKi4u1NpECIYQQQgghRHuePHmCr776Sul2d3d3HD16lL7P1xFqIZVy69Ytmfe+vr5qxZMOV1paijt37mi1XIQQQgghhJCamzRpEr799lv07t0bzZs3h1Ao5GaN7tWrF3744QfcvHmTJjKrQ9RCKuXhw4fca6FQCGtra7XiSRYnl0hMTISPj49Wy0YIqX3l5eU0yy4hBAAwYcIETJgwQdfFIIRoWdOmTTF79mzMnj1b10WpN8rKyuQ+4/F4dZY/tZD+IysrC4WFhdz7pk2bqh23cti0tDStlYsQUjsaNGgg97AtKCjQUWkIIYQQQnQjJydH5j2Px6vTCim1kP5DMgZUQpPFnYVCIfh8Pjf+9OXLl1otm0RmZibCw8ORkpKC7OxslJWVwdTUFObm5nB1dYWXl5fChXEJIfJ4PB6MjY2Rn5/Pffb69WswxmBiYqLRmr+EEEIIIW+T8vJylJaWIjc3F7m5uTLbTE1NqUKqC1lZWTLvLSws1I7L4/Fgbm6O169fAwDevHmj1bJJvH79mstDori4GBkZGUhKSsKJEyfQvHlzjBgxgvq9E6IGU1NTmQopY0zhfUYIIYQQ8r4QiUR1mh81AfyjuLhY5r2ms2pJL5or+cVBF5KSkrB27VpcunRJJ/kT8jYxMTGBkZGRrotBCCGEEFIvmJmZ1fnswtRC+o+ioiKZ9/r6+hrF5/NlD2VxcbHGaShjYmKC1q1bw8PDA02bNoVIJIKBgQEKCgqQmpqK+Ph4XLx4kdsHsViMX3/9FSYmJjS5EiEqNGjQALa2tkhLS5MZQ04IIYQQ8r6xtLSEubl5nXbXBahCypFefxSQr2BWpXL4kpKSGpcJAAYOHAgnJyeFlVszMzN88MEH+OCDD9CrVy9s3boVDx48AFDR9TA8PByurq4wNTXVSlkIeRdJKqX5+fnIy8tDQUEBGGO6LhYhhBBCSK0TCAQwNjaGiYmJztZdpQrpPypXKCtXUKtSObyBgUGNywQAbm5uaoUTCoX417/+hW+++QZPnz4FUNHqe/LkSQwfPlwrZSHkXdWgQQOYmZnBzMwMjDHujxBCCCHkXaVoxQFdoArpP6THgALQeAxo5QqpLn5hMDAwwJgxY/D1119zn12/fp0qpIRooK6nOieEEEIIeZ/RpEb/qFyBrDzJUVWkx6A2aNBAa+NHNeXs7AxbW1vu/Zs3b2ptGRpCCCGEEEIIqQlqIf2Hubm5zHtNlm5hjMksG6PJkjG1wdnZGWlpadz7zMxMNGrUSGWc0tJSiMVibmKXrKwsFBUVQU9PT2eVa0IIIYQQQojuCQSCWutBRhXSf0i3KgJARkaG2nFzcnJkuuw2btxYa+WqDjMzM5n3eXl5VcY5ceIEjhw5wr1ftmyZtotFCCGEEEIIeQtt2LCh1pbKowrpP0QiEYyMjLgWQsnEQOp48uSJzHtdV0grz/CrTgtnnz590KNHD5SXlyMnJweGhobg8XjUQkoIIYQQQsh7rvJ8O9pEFVIpLi4uiI+PBwDk5ubi1atXsLa2rjJeUlKSzHtXV9daKZ+6Xr9+LfO+coupIvr6+lzF08TEpFbKRQghhBBCCCHSaFIjKa1bt5Z5Hx0drVa8mJgY7rW+vj48PDy0Wi5NlJSUcGuRAhUTLNnZ2emsPIQQQgghhBCiDFVIpbRu3VpmPdJLly6hrKxMZZx79+7JzGLbsmVLnS0qCwCnT5+WmfHX2dm51vp7E0IIIYQQQkhNUJddKUKhEJ06dcK5c+cAAK9evcKJEyfQv39/heFLS0uxe/du7j2Px0O/fv2Upv/69Wt88cUX3HtLS0usWbNGafjnz5/D3t5e7fLfuXNHZmIiAOjevbva8QkhhBBCCCGkLlGFtJK+ffsiMjKSa2U8fPgwDAwM0L17dzRo8P8Nyrm5udiyZYvM8ipt27ZFs2bNtFaWdevWwdnZGZ06dULLli2VTi5UUFCA06dP4/jx4ygvL+c+d3Nzg6+vr9bKQwghhBBCCCHaRBXSSszNzTF58mSEhoaCMQbGGPbt24cLFy7A3d0dJiYmSE9Px61bt1BaWsrFs7W1xdixY7VaFsYYEhISkJCQAAMDA9jb28POzg4mJiYwMDBAYWEhUlNTkZSUJDezbqNGjTB9+nStlocQQgghhBBCtIkqpAp4eXlhwoQJ2LVrF1fRS09PR3p6usLwTZs2xfTp02t1rGZJSQmSk5ORnJxcZVhfX1+MGzeOZsslhBBCCCGE1GtUIVWiQ4cOcHR0xIEDBxAfH69wciORSIROnTqhX79+MpMhaUvfvn1x+/ZtpKSkoLi4WGVYPp8PT09PdOvWDe7u7lovCyGEEEIIIYRoG48xxnRdiPouLy8PSUlJePPmDYqKiiAUCmFlZQUXFxeZcaW1pby8HK9evUJ6ejrevHmDwsJCiMViGBoawsTEBI0aNUKzZs1qpVJMCCGEEEIIIbWFKqSEEEIIIYQQQnSC1iElhBBCCCGEEKITVCElhBBCCCGEEKITVCElhBBCCCGEEKITVCElhBBCCCGEEKITVCElhBBCCCGEEKITVCElhBBCCCGEEKITVCElhBBCCCGEEKITVCElhBBCCCGEEKITVCElhBBCCCGEEKITVCElhBBCCCGEEKITVCElhBBCCCGEEKITVCElhBBCCCGEEKITVCElhBBCCCGEEKITfF0XgLw7MjMzERkZiVu3biEzMxP5+fkwMzODlZUVfHx80L59e5iZmemsfNu2bUNUVJTMZ6tXr4aVlZVG6Wh7P0tLS3Hu3DncuHEDL168QFlZGSwtLeHl5YVevXpBKBSqnVZcXBx++OEHAMCAAQMwcOBAjfaNEKI+bTxT6uNzs7y8HPfu3UNsbCwePXqEnJwcFBQUwNjYGCKRCDY2NnBzc4Obmxvs7OzUTpeenYS8/Wrj+VCfnoOlpaWIi4vDrVu38PjxY+Tk5KC4uBiGhoYQCoVo1qwZWrZsCV9fXxgYGGicPj0HFeMxxlid5UbeWREREdi/f///tXff8VFU+//4X7sphPSQQgqQ0EsIIUhTEhKKhKYIghQDCEiJWJB7vZ8reil6uX68Kooigl4/gEAEUYoi4gUMJPReQwkkEEICKSRhSUjbzPcPfpnfzmbL7GaX3cjr+Xjk8dizOXPmzOzue/fMnIKqqiq9edzc3DBhwgR07979EdbsoTNnzmD58uV1njf1x6Olj7OkpARLly7FrVu39Jb12muvoWXLlkbLKi8vx8KFC1FUVISAgADMnz8fTk5ORrcjItNZIqbYY9y8du0akpKSkJ2dLSv/8uXL4eDgYDQfYydRw2eN+GBPcfD8+fNYv3497t69azSvt7c3JkyYgMjISNnlMw7q57Bw4cKFj2xv9Ke0bds2bN26FTU1NeJzAQEBCAkJgYODA0pLSwE8vIpz8uRJNGnSBC1atHhk9SstLcUXX3yBioqKOv8bMGAAXF1dZZVj6eOsqanBsmXLcOPGDQCAUqlEaGgofH19oVKpUFNTg6qqKpw5cwa9e/dGo0aNDNZv8+bNSEtLAwBMnz4dTZs2lXVcRGQaS8QUe4ybKSkpWLlyJe7duyc+5+zsjKCgIAQFBcHLywtqtVpy3MOGDYNSaXj0D2MnUcNnjfhgT3Hw6NGj+Prrr1FWViY+5+TkhObNmyMoKAiurq4oKysT61peXo7jx4+jSZMmaN68udHyGQcNY5ddqpeTJ09ix44dYjooKAhTpkxBaGio+NyNGzewatUq5ObmAgDWrVuH4OBgWVdsLOGHH35ASUkJAKBTp07iB84U1jjO06dP4+rVqwAAPz8/vP7662IAKCoqwrJly5CdnQ2VSoWdO3fihRde0Fu/rKwsJCcnAwB69eqFjh07mnyMRCRPfWOKPcbN1NRUJCUlobbTVGBgIEaMGIGIiIg6V8mLiopw9uxZHDhwAAqFwmC5jJ1EDZ814oM9xcG7d+9i3bp1YmPRyckJo0aNQnR0tKRbbmVlJVJSUrB161ZUVVVBEAQkJSWhbdu28Pf3f6TH+meLg5zUiMymVqvx008/iWkfHx+89dZbkg8YAISGhuKtt96Ct7c3gIdXdX788cdHUsdz587h8OHDAICIiAj06NHD5DKsdZzHjh0TH48ZM0ZyNcrHxwcJCQliWnucmqaamhqsX78eNTU1cHV1xejRo+UfHBGZpL4xxR7j5u3bt7Fx40bxx2ZERATeffdddOvWTWeXLR8fH8TGxmLevHkG734wdhI1fNaID/YWB1NSUiR3dqdOnYr+/fvXGSPq7OyMgQMH4qWXXhKfq6qqQmpqqt6yGQflYYOUzHb06FEUFBSI6TFjxsDNzU1nXjc3N4wZM0ZMX716FVeuXLFq/R48eIB169YBAFxcXDBhwgSzyrHWcV6/fh0A4OrqqnMMQsuWLcUJAVQqlaQOmvbu3SuWNWrUKJMGsBORfJaIKfYYN7/77jtxTFPTpk0xc+ZMi4wdYuwkavisER/sLQ6mp6eLj4OCgtCtWzeD+bt3746goCBJnfRhHJSHDVIy24kTJ8TH3t7e6Nq1q8H8UVFR4pUf7e2tYdOmTSguLgYAjBw5Ek2aNDGrHGsdp0qlAgD4+vrq7dai2QWkNr+m4uJibNu2DQDQunVrREdHG6wbEZnPEjHF3uJmRkYGrl27JqZHjx5tsYksGDuJGjZrxQd7i4Oa42KbNWsma5uQkBDx8f379/XmYxyUhw1SMktlZSUuXrwopsPDw43OpObg4IBOnTqJ6bNnz1qtfhcuXMCBAwcAAG3atEFsbKxZ5dj7cW7cuBHl5eVQKpV48cUXjY7nIiLzWCKm2GM8SUlJER83adIEnTt3tki59nismhg7iYyzRnywx9jg4uIiPjY0A64mzXz6JrKzx2PVZE9xkA1SMktubi6qq6vFdOvWrWVtp5mvdv0lSysvL8fatWsBAI6Ojpg4caLZHzJrHmftOlOFhYXQt/pSYWFhnfy1zp07h5MnTwIAnn76acnVOiKyHEvFFHuMmxcuXBAfR0REGJ0xVy7GTqKGzxrxwR7jYKtWrcTH165dk9RPl6qqKmRkZIjptm3b6szHOCgfZ9kls9TOAlYrICBA1nba+XJzc9GmTRuL1QsAfvzxRxQVFQF4OOV4YGCg2WVZ8zhDQ0Nx9+5dlJWV4ezZs3XGAGRlZYlrfbm5uUnWNqysrMT3338P4GF3jeHDh8s7ICIymaViir3FzYKCAklXtdofZXfv3sWBAwdw9uxZFBYWorKyEm5ubggODkbHjh3Rp08fvWOgNOto6Bj0Yewksg/Wig/2FgcBIDY2FikpKaipqYFKpcLWrVsNTu6zZcsWsQtso0aN0K9fP535GAfl4x1SMovmVRcAssdS+fr6StL6Blmb6+LFi+JsZ82aNUN8fHy9yrPmcWrOzrlp0ybk5+eL6ZKSEvGOjHZeAPjll1/Euo0fP77OTHBEZBmWjCn2Fje1F7cPDAzE3r17sXDhQmzfvh1ZWVkoLS1FVVUViouLkZaWhp9++gnz5s2TdOXThbGTqGGzVnywtzgIAMHBwXjxxRfFO8C7du3CF198gQsXLqC0tBQ1NTUoLS3F+fPnsXTpUuzZswfAw66+M2bM0HsMjIPy8Q4pmeXBgweStJyF4AGgcePGkrSuheXNpdmtTqlUYuLEiUb76htjzeOMiopCq1atkJGRgfz8fMyfPx+hoaFQKpXIysoSxye4ublhyJAh4na3bt3C7t27AQDdunVDRESEScdERPJYOqbYW9zUnojj+PHj2LVrl5j29PREQEAABEFAbm6uuGB8eXk51q9fj7t37+K5557TWTZjJ1HDZq34YG9xsFZ0dDSaNGmCjRs34vbt2zh//jzOnz+vM69SqUTnzp0xcuRIcSZbXRgH5WODlMyi/eGQO+uadr7y8nKL1Wnz5s3iFZ8BAwYgLCys3mVa8ziVSiVmzpyJpUuXIicnBzU1NcjMzJTkady4MRITE8UZ1wRBEBdvdnFxwdixY8W8giDgyJEjOHjwILKzs1FRUQFvb2+Eh4dj8ODBZs8yTPS4snRMsbe4WfsDslbtj01fX19MmDAB4eHh4lhZtVqNo0ePYsOGDeL+f/vtN4SGhiIqKqpO2YydRA2bteKDvcVBTZ06dcIbb7yBH374AadOndKbr0OHDujXr5/BxijAOGgKNkjJLGq1WpKWO9Bd++6Cdjnmunz5sthFxN/fH88++6xFyrX2cXp7e+Ptt99GcnIyjh8/jjt37qCmpkaczS4+Ph5eXl5i/pSUFHEg/YgRI8QgU1VVha+//rrObGwFBQXYt28fDh8+jFmzZklmbiMi/awRU+wtbuqaTdLDwwNvvfUWfHx86tThySefRGBgID766COxDps3b0ZkZGSdY2HsJGrYrBUf7C0O1qqoqMDmzZvFsaTAw0nsgoKC4OrqivLycuTm5qKyshJpaWlIS0tD27ZtMXXqVL2NNcZB+dggJbM0atRIkq6urpbVB107wFmi33plZSW+++47cZaxiRMnWqw//KM4TmdnZ8THxxsdm3bv3j1s3boVANCiRQvExcWJ//vhhx/EQNKiRQsMGjQInp6eSEtLw3//+19UVFRgxYoVmD9/vmRgOxHVZa2YYk9xU1d9gIfrq2r/2NTUsmVLxMXFiWOo8vLycPnyZXTs2NFg2YydRA2LteKDvcVB4GFj9NNPPxXvMDo7O2PEiBHo27evZD/V1dU4duwYNm3ahNLSUqSnp+Pjjz/G//zP/0gafrUYB+Vjg5TMov0hq6yslPUhq6yslKQ1137Kzs7GTz/9ZLSM559/XrJw8ebNm8UB39HR0Wgo8psbAAAgAElEQVTfvr3RMuSyxnGa64cffkBZWRmUSiUSEhLEK2137twRJ10JCAjAX//6V7He7du3h5eXFzZu3IiKigr88ssvmDJlSr3rQvRnZq2YYq14Ym7s1C7H0dERPXv2NFpOnz59xB+cAGQ1SBk7iRoWa8UHe4uDwMOZ1Gsbo46OjnjjjTd0zuDr6OiIJ598Eq1atcL//u//oqysDIWFhUhKSkJiYmKd/IyD8rFBSmZxd3eXpEtKSuo8p0tJSYneckpLS5GWlma0jMGDB4uPc3JysHfvXgCAl5cXnn/+eaPbm8Iax2mOtLQ0HDt2DAAQFxeH0NBQ8X9HjhwR7+T079+/TgCMiYnBjh07oFKpcPz4cSQkJMgex0D0uLFmTLFWPDEnduoqJzg4WFZsCAoKgpOTk3gVX9cMkIydRA2bteKDvcXBkpIS7N+/X0z36dPH6HIyTZs2xeDBg7F582YAwJkzZ5Cfnw9/f3+DdWQc1I8NUjKL9jp8hYWFshbV1Z4Cuz5rhAKASqUSP0glJSV48803Tdr+nXfeER83btwYn332mcH62eI4q6qqkJSUBODheIERI0ZI/q+5OLP2XQrg4eD4du3a4cSJE6iursbNmzcli0AT0f/PmjHFHuKJpqCgIEla7o8epVIJNzc3FBcXA4DORdvt4VgZO4nMZ634YA+xQdPFixfFMaMAdE7SpkvXrl3FBqkgCLhy5UqdBqk9HGtDiYNskJJZtGcWy8rKQpcuXYxul5WVJT52dHSULP7bvn17rFy50nKVtABrHKepfv31V3F9qbFjx9bpuqF5Ja12gLo2zedrvySI6NGyVjwxN3b6+/tL7mRUV1fL3lZzjJOuq+WMnUQNm7Xig73FwaKiIkla7myy2vnu3btXJw/joHxskJJZmjRpAj8/P7ErxpUrV2Rtl56eLj5u3bq17BnH9HFwcICbm5vs/NXV1ZJpuF1dXcVpy7XXfQJsf5w5OTn473//CwCIiIhAt27d6uQx5UvCnPxEjxNrxhRbxxNtSqUS7du3F9fa01xY3ZDS0lLJkhC6JvOw9bEydhLVj7Xig61jgzZHR2lTSHv8pj7a+XSNDbX1sTakOMgGKZktKipKXJfqypUruHv3rsErS3fv3pV8GHV9MEzVpk0bLFmyRHb+gwcPYs2aNWL6nXfeMTpjmK2OUxAErF+/Hmq1Gs7Ozhg3bpzOfJo/eouKiup0swGkV7R0NbyJ6CFrxxR7iJuaunXrJv7gLCoqQk5OjtG19dLS0sRuzQD0dt9i7CRq2KwVH+wpDmrfFbxx4waaN29udLvr169L0vpmH2YclMcylxfosfTUU0+JdwIEQcCvv/5qMP/27dvFINWoUSN0797d6nW0BFsd54EDB3D16lUAwPDhw/X+yNUMHhcvXqzz/6qqKklw0xVsiOjRsLe42a1bN8nYsO3btxvMr1arsXPnTjHt5OSEzp0768zL2EnUsFkrPthTHGzXrp1YFwDYs2eP0TVOBUEQG5kAoFAo0K5dO515GQflYYOUzBYcHIxevXqJ6f3794tTR2tLSUnBgQMHxPTTTz9d71nDHhVbHKdKpRIHywcHB2PgwIF682ouVLxnzx5J90EASE1NhUqlAvBwZjiupUdkO/YWNxs3bozhw4eL6RMnTuj9waRWq7FmzRpkZ2eLz8XFxcHDw0NnfsZOoobNWvHBnuKgl5cXIiIixHROTg6+/fZbvV131Wo1NmzYIGm8RUVF6a0T46A8DgsXLlxolZLpsdC6dWucOnVKHC9w9uxZcTD2/fv3kZGRga1bt2L37t3iNmFhYZg4cSIcHBweeX1v3ryJM2fOiOkBAwbA1dXV6HaP+jiTkpKQmZkJhUKBxMRE+Pr66s3btGlTHDp0COXl5SgrK8OFCxfQuHFj3L9/H6mpqZKrbc8995xkum8iqh9zYoq9xc3Q0FBkZWUhLy8PwMN1Ay9cuAC1Wo2Kigrk5+fj9OnTWLNmjeRKeYsWLTB16lSDdWLsJGrYrBUf7CkOhoaG4siRI+JkTLm5uTh06BDKyspQWVkJlUqFnJwcHD9+HOvWrcOFCxfEbd3c3DBz5kyDcZ9x0DiFoNnRm8gMt2/fxtKlS3H37l2jeZs1a4Y33ngDnp6ej6BmdWmP91q8eLHsqz2P6jgvX74sjmGLiYlBQkKCrG0+//xzg4PNIyIiMHv2bEnXFCKqH3Njir3FzcrKSnz11Vey1vEDHo61nTlzpqw6MXYSNWzWig/2FAczMjKwYsWKOmuAGuLt7Y3ExESEhYUZzcs4aBjvkFK9ubu7o0+fPnjw4AFu376ts++9m5sbnn76aUydOtWmE0OYe4cUeDTHWVVVhWXLlqG0tBQeHh5ITEzUOXObNj8/P7Rp0wZXr16VzG4HPJwpr2/fvpg0aZJN7koT/ZmZG1PsLW46ODigd+/e8PHxwe3bt3WuLQo8nLhjxIgRGD9+vOw6MXYSNWzWig/2FAd9fHzw1FNPQRAE5OXlGZxt18PDA/3798e0adPqrD2qD+OgYbxDShZVWVmJK1euoLCwEGVlZXB3d4e/vz/atm37p/pCt9Zx3rp1CydPngTwcKB9+/btTdperVbj6tWruHXrFiorK+Hl5YWOHTvqXVuKiGzPHuPmzZs3cevWLfFugYeHB0JDQxEcHFyvK+SMnUQNnzXigz3FwZqaGuTk5ODmzZsoLS1FRUUFGjVqBHd3dzRv3hxBQUH1WnaGcbAuNkiJiIiIiIjIJjjLLhEREREREdkEG6RERERERERkE2yQEhERERERkU2wQUpEREREREQ2wQYpERERERER2QQbpERERERERGQTbJASERERERGRTbBBSkRERERERDbBBikRERERERHZBBukREREREREZBNskBIREREREZFNsEFKRERERERENsEGKREREREREdkEG6RERERERERkE2yQEhERERERkU2wQUpEREREREQ2wQYpERERERER2QQbpERERERERGQTbJASERERERGRTbBBSkRERERERDbBBikRERERERHZBBukREREREREZBNskBIREREREZFNsEFKRERERERENsEGKREREREREdkEG6RERERERERkE2yQEhERERERkU2wQUpERET0JxUWFgaFQgGFQoGXXnrJ4uVfv35dLF+hUGD16tUW38efRVxcnHie4uLibF0dIrvBBikRyaL9o8Pcv65du9r6UIiIiIjITjjaugJERERERA3BZ599huLiYgBA165d8dxzz1llPwsXLhQfx8XF8Y4q/amxQUpEZnNwcDB5G0dHhh0iImqYPvvsM9y4cQMAMHnyZKs1SBctWiRJs0FKf2b8ZUhEZomNjcXevXttXQ0iIjLg+vXrtq4C/X/4nUmkG8eQEhERERERkU2wQUpEREREREQ2wQYpERERERER2QTHkBJRg6BWq7F//35cu3YNd+7cgaenJ1q1aoXY2Fi4urraunqPXEVFBfbt24cbN24gPz8f3t7e6NGjB3r06GFwuzt37iA1NRXXr19HdXU1goKCEBcXh9DQ0HrVp6qqCocPH8a1a9eQn58PhUIBf39/REREICoqCgqFol7ly3X//n2cPHkSly5dQnFxMSorK+Hq6go/Pz+0bNkSXbp0gZeXl1ll5+fn4+DBg8jNzcXdu3fh6emJwMBAREdHIzAwsF71Tk9Px5EjR5CTkwNXV1eEhITgqaeeQtOmTetV7qN279497N+/H7du3UJBQQFcXV3RtGlT9O7dG2FhYRbbT3Z2Ng4fPozs7GxUVVUhICAAvXv3Rvv27etddmZmJo4fP478/HwUFRWhcePGCAwMRHh4OCIiIqBUmn4t/1GdF0srLCxEcnIysrOzoVarERISgsjISHTs2NGi+6l9PfPy8lBcXAwfHx+EhIQgJiYGPj4+FtvP2bNnce7cOdy6dQvOzs4IDg5GXFwcAgICLLYPIjKDQEQkQ2ZmpgBA/IuNjbX4PkJDQ8XyJ0+eLAiCIKjVauHDDz8UgoODJfuv/XNxcRHeeust4f79+wbLnjx5ss7tTfmrVVZWJjRp0kR8fuDAgSYf66pVqyRl//jjj7LOx71794S5c+cKPj4+OuvYpUsX4eDBg3XKun79ujBmzBjBwcFB53bDhw8XsrKyTD6OjIwMYdKkSYKHh4fe8xYQECD861//EkpLS00uX6709HRh/PjxgouLi8HXUKFQCBEREcL7778vu+xt27YJvXr1EhQKhd5ye/fuLfz+++8m1/vQoUNCz549dZbp6OgojBgxQsjMzBQEoe57pvZ5bdqf1VWrVsmuj673nBwpKSnCgAEDBEdHR73nKDw8XNiwYYNQU1NjtDx9x3rhwgVh8ODBglKp1LmPbt26CXv37pVd71qlpaXCxx9/LLRu3drg+6dJkybC5MmThePHj9vkvJjDnNc0OztbGDNmjN569+zZU9i9e7cgCOa/39RqtbB69Wqhc+fOes+Ng4ODMGjQIOHIkSOyytSM86GhoeLz27ZtEyIjI/XGhFGjRgkZGRl6y12wYIHJ3xe6viNjY2MN/t+c76na1zQ7O1vyer388suyzpmh45T7PieqLzZIiUgWWzRIi4qKhLi4OFlfyr179xZKSkr0lm3JBqkgCMJf/vIXyQ+a9PR0k461V69e4vZBQUFCVVWV0fORnZ0tdOjQwWg9GzVqJOzcuVMsJyUlRfD29ja6XfPmzfU2cnT59NNPBWdnZ9nnr3379sLVq1dNOk9ybNmyRWjUqJFJr2WjRo2MlltUVCQMGjTIpHJnzZql87XU5dNPP9XbsNL88/HxEfbs2WOXDdLy8nJh4sSJJp2jESNGGL04oetYk5KSBFdXV6PlOzo6CuvXr5d9zAcPHtR7wcvQMdjivJjD1Nc0NTVV8PT0NFpfpVIpfPTRR2a9327evCl069ZN9rlRKBTCP//5T6Pl6mqQzpkzR9Y+AgIChLNnz+ostyE0SAVBEJ5//nnxeTc3N4Pfidqqq6uFZs2aidt3795d9rZE9cUuu0Rkl6qrq/HCCy+I0+S3adMGcXFxCAwMxIMHD3DkyBHs379fzH/48GHMnTsX//nPf3SW5+DgYPK6qWq1Wu//EhMTsWTJEggPL+xh5cqV+Oijj2SVe+bMGRw5ckRMT5s2zej6rBUVFRg2bBguXboEAOjWrRv69OkDb29v3L59Gzt27MCtW7fEvBMmTMCVK1eQl5eHYcOGQaVSwcnJCX379kVkZCRcXV1x9epVbN++Hffv3wcA3Lx5E1OmTEFycrLRY3jrrbfw8ccfS57r0KEDnnzySQQFBQF4uNzE7t27kZeXBwC4fPkyoqOjcfLkSTFPfV2+fBnjxo1DRUWF+FxQUBBiYmIQFhYGV1dXlJWVIS8vD+fPn8eZM2dQVVVltNz8/HzExsbi4sWL4nPOzs7o06cPIiIi4O3tjfv37+PMmTPYt28fqqurAQArVqxAWVkZ1qxZY7D8b7/9Fm+++abkOX9/fwwZMgQtWrTAvXv3cOjQIRw7dgxFRUUYM2YM5syZY8qpsbry8nIMGjQIqamp4nNKpRK9evVCVFQUfH19UV5ejrS0NPzxxx948OABAGDbtm0YPnw4du3aJfszuWfPHsyaNQvV1dVwcXFBv3790LFjR7i5ueHGjRvYsWMHCgoKADyMHS+//DK6d++Odu3aGSz3l19+wZgxYyTvH6VSiSeeeALdu3eHn58fysvLcevWLRw7dgzp6el2dV4s7fTp0xg6dChUKpX4nIuLC+Lj49GhQweo1WpcuHABu3fvRlVVFf72t7/BxcXFpH1cuXIF/fr1Q05Ojvicm5sboqOj0alTJ3h4eKCkpATHjh3DoUOHxBj77rvvorKyss76nIYsWrQIn332GQDAz88PAwcORFhYGBQKBdLS0rBz507xtc/Ly8O4ceNw8uRJNGrUSFKOUqkUXxPN7wWFQqG3C7c5r6Hm95Q5+5k9ezZ++uknAEBpaSnWrl2L2bNny9r3L7/8guzsbDE9a9Ysk+tPZDbbtoeJqKF41HdIa++8+fr6Cps3b9aZf9euXZIr+QqFwmJ34FasWCE53sjIyDp5hgwZIv7fz89PKC8vl1X2rFmzJHcZbty4oTOf5vlwcnISAAjNmjXT2SXxwYMHQkJCgqTOf//734WoqCgBgNC3b1+d5+bWrVtCly5dJNvt2rXLYP3Xr19f59zs379fZ97y8nJh8eLFkq7C/fr1k3GW5JkyZYrkXC5btkyorq7Wm7+kpERISkoSBgwYoDdPTU2NEB8fL3lfJSYmCnfu3NGZPyMjQ+jXr5/knKxZs0Zv+ZmZmYKbm5sk/9tvvy1UVFTUyZucnCzevdO+C2zrO6Qvv/yyZD+jR4/WW6c7d+4IY8eOleRftGiR3rK175DWxoNx48YJt2/frpNfpVLVKX/ixIkG63/p0qU6dwJHjBhhMIZcvnxZePvtt4WXXnpJbx5rnhdzyH1Nq6qq6nRrHTp0qM7znZmZKfTp00fn+9LQ++3BgweSeOPk5CT84x//EIqLi3XmP3PmjKROSqVSSE5O1lu+5h1GJycnQaFQCEqlUli8eLHO+JyRkSGEh4dL6v/tt9/qLV8QzO/WLgjG75Bq0qzTggULZO+jU6dO4nadO3eWvd3gwYPF7by8vKw6xIJIGxukRCSL9o9c4OH4HlP/Tp8+rXcfml/0AAR3d3fh/PnzBuv13Xffmf3Frc+WLVskDajQ0FAhJyenTr7t27dL9r1u3TqjZatUKsl4y+HDh+vNq30+vL29DY5zKi8vF8LCwiQ/3gAIvXr1MthYTktLk3QdNfQjq7i4WPIjPjo6WtYPl5UrV0qOZc+ePUa3kaN58+ZimdOmTbNImdqNoc8//9zoNpWVlULfvn3FbVq2bKm3YTxhwgRJ+e+++67BstPS0nSO0bVlgzQ5OVmyj7lz58oqX/PYPTw8hKKiIp35tF8DAMLUqVMNll1RUSHp0t64cWOhrKxMb/7+/ftLyn/ttddkj+PUl8/a58Uccl/Tr7/+WlL3/v37C5WVlXrzq1QqoWvXrnVeJ0PvN82ur0qlUti6davR+hcXF0te15iYGL15dXV5NXRxSBAejj/XbFQbayjae4N02bJlkm31XSzUlJmZKfkOePXVV2Xvj8gS2CAlIll0NUjN+Tt16pTefWg3wJYsWWK0XtXV1UJgYKC4zaBBg+p1nKmpqZKJcXx9fYVLly7pzKtWq4WWLVtKGmfGaN953b59u9682ufjyy+/NFr+woULJdsoFAqjjXpBkP5Qateund58H3zwgZjPxcVFuH79utGya/Xu3VvcdvTo0bK3M0RzDOtXX31V7/JqamqEjh07imUOGTJE9rZpaWmSiY90vbb5+fmSH7/t27c3+KO/1scff2xXDVLNO8gRERGyx83m5+dL7g4vW7ZMZz7tBmlgYKDBxmUt7R/juib4EgRBOHLkiCRfz549ZR+DIdY+L+aQ+5pqjul0dnaW1dvk2LFjshukpaWlgq+vr5gvMTFR9jH89ttvkn3oi2naDdJnn31WVvmjR48Wt3F1dTXYy8LeG6T37t2TXMBKSEgwus3f//53WeeXyFq4DikR2SVnZ2dMnz7daD4HBwf06dNHTGuO+TNVWloann32WZSXlwMAXF1dsX37dr1LSSiVSsk4m/379+PChQsG97Fy5UrxcWhoKIYMGSKrbi4uLnjppZeM5uvVq5ckHRMTg/DwcKPb9e7dW3ycnp6ud5zld999Jz4eMWKEScvFvPDCC+LjvXv3QhAE2dvq4+7uLj4+depUvcs7duyY5D30+uuvy962Y8eOiIiIENO6xuLu2LFDMl4xMTERTk5ORsueMWMGGjduLLsu1nTnzh38/vvvYvqVV14xOga6lp+fHwYMGCCm5YxXBoCpU6fKOv7Y2FhJWl88+P777yXp+fPnyz4GfWxxXiwlKysLJ0+eFNNDhw5F69atjW7XvXt3Sfw1ZPv27SgsLBTTpny2Bg0aJFn+Re75efXVV2Xl03zflJWV4caNG7LrZm88PDyQkJAgpn/88UfJeddWVVWFVatWiWm53xlElsQGKRGZJTY2VpxswpS/rl27yir/iSeekDQ2DNFcx6+oqMicw8HNmzcRHx8vbu/g4ICNGzdKGmq6TJs2TTKpx4oVK/TmPXr0qKTRNGPGDNlrGnbr1k3WeqvNmjWTpKOjo2WVr7mdIAi4d+9enTz5+fmSH/gxMTGyyq7Vtm1b8XFBQYE4CVN99OzZU3z8n//8B1988QUqKyvNLm/fvn3iY4VCIfv81dI8xtOnT9f5/+HDhyXp4cOHyyrXw8MDcXFxJtXFWlJSUiTp+rwPdJ0jXbQbmvpor+mpLx7UTpYGAF5eXrIvDBlii/NiKea+LwHg2WeflZVP87Pl5+eHDh06yN6HUqlEq1atxLSc86N9sdIQue+bhkJzIqPy8nKsXr1ab94tW7bgzp07YpqTGZEtsEFKRHYpJCREdl43Nzfxce2MsaYoKirC4MGDJTMMrly5UtaPMl9fX4wdO1ZMr127FmVlZTrzajZWnZycMG3aNNl1lHs+NM9FfbbTdR6170C+/vrrcHR0lP03YsQIyfaGrtrLpTnzbE1NDV5//XWEhIRgypQpWLt2LTIyMkwqT/MYBUGAt7e3Sce4efNmg8en2aB3d3eX/Mg2Ru7FHGvTfh9ERkaadI6WLFkibiv3PWDJ9zEASU+Gnj17yr4wZIgtzoulaN9JjoyMlL2t3Pel5vkpKCgw6dw4OjrixIkT4vZyzo+Pj4+si3iA/PdNQxEeHi65iLNy5Uq9PVI0v5f8/Pzw/PPPW71+RNrYICUiu6T9A8EQhUJh9n4ePHiAZ555BmlpaeJz77//vkmNRc2r0SUlJXW6A9Y+v3HjRjH93HPPoWnTprL3IfeHlfa5MHc7XT9eapfVqFVTUwO1Wi37r6amRrJ9SUmJrLoZEh8fjw8//FDSoCgoKMDq1asxadIktG7dGkFBQRg/fjySkpJQWlpqsDztYzTl+NRqteS86To+zTsv/v7+Jr13TXm/WJO1z5EucuOBnPfxvXv3JF3Sg4ODZZVtjC3Oi6Vo3xEMCAiQva3c92V9z48mOeenPt8hlhhOYGua30vp6en4448/6uS5cuWKpPvzlClT6ix5Q/QosEFKRI8ttVqN8ePH48CBA+Jzs2bNwrvvvmtSOT169ECPHj3EtK5uu999953kzmlD7BZVXFxs0fK0G6jm+tvf/oaDBw9i2LBhOtf+u337NjZs2IAXX3wRzZs3xwcffCCuG6rNkseo6/g077yY8oPZnPzWYslzZIsf/pprbAKQPTTAmIZ8XrTvCJryXpOb19qfLZIaOXKk5GKLru8lzTkNFAoFZs6c+UjqRqSNDVIiemy98sor2LZtm5geOXIkvvzyS7PK0rwaffz4cUn3MkD6xd+uXTv079/frP3Ykvbd1l27dpk1jrj2z5JjInv16oXt27cjNzcX33//PWbPno2oqKg6XTGLioowb948DB06VOdYU81jDAkJqdfxXb9+vU75mo0fY3drtZma31o0z5FSqURFRUW9ztOj5unpKUlbqntmQz4v2o1yU95rcvNqnp8+ffrU69xojgEm3RwdHTFjxgwxvW3bNty+fVtMl5eXY82aNWJ64MCBsiayIrIGNkiJ6LG0cOFCfP3112I6JiYGSUlJZo8lGzt2LHx9fcW05tXo1NRUyZi1hnoV2s/PT5K+du2ajWqin7+/P8aNG4dly5bh5MmTKCoqwqZNmzBy5EjJa7tr1y78+9//rrO95jHm5OSIMy5biuZMofn5+SY1PDQnHjGkPt0P5TTONM9RTU0NMjMzZZdvDzw8PODs7CymLTG5FtCwz4vm+xIA8vLyZG8r932peX7sMXb8Gc2YMUOcxbuqqgrffvut+L9NmzZJxuI2xF479OfBBikRPXa+/vprLFq0SEyHh4dj27ZtktlyTeXi4iIZd/r999+LM9VqNk7lLt9ijzp16iRJa86aaa88PT0xevRobN68GTt27JAsw/HNN9/Uya95jIIg1Jk5tb46duwoPr5//75Jky6dOXNGVj7tLpT6JtnS9uDBA1ndKhvi+0Bb586dxcfHjh2zSBfQhnxeNN+XgPz3mil5Nc/P7du3ceXKFdn7IPMEBQVh5MiRYvqbb74R3+ua30vBwcGyZ0smsgY2SInosbJt2za88sorYrpZs2bYuXNnnTsE5pg1a5Z4F660tBRr165FYWEhfvrpJzHPCy+8gCZNmtR7X7bQqlUrtGzZUkzv2LHjkU++Uh/x8fGSmX6zsrLqNMA014IEgKSkJIvWQXsZoV9//VXWdiqVSnY3RS8vL8ldUrlrKh44cKDO5DG6DBw4UJK29Dl6FDS7i5eUlGDHjh31LrMhnxdz35cA8PPPP8vKZ+3P1qOiuW6wnM+LuTQvntVnP5rfdzdu3MBvv/2G8+fP4+DBg+LzL7/8cr3X4SWqDzZIieixsX//fowbN078cvfx8cHOnTvrrN1prpYtW0rWM1y5ciVWrVqFiooK8bmG3i1q/Pjx4uOSkhJ88MEHNqyN6bTHSGmPI+3Tpw9CQ0PF9Pr163H+/HmL7X/o0KGS7qJfffWV3gmWNH3zzTey73Q6OTlJ1rSUe6du+fLlsvK1aNECTz31lKT8nTt3ytrWXkyYMEGSfv/992W9DoY05PPSokULREVFielff/1V1t37EydOSCaFM+SZZ56Bh4eHmF66dKlkTGNDoXkM1lyv1FL7iY2NlfQIWLFiheTuqIODA6ZPn252+USWwAYpET0W0tLS8Oyzz4pjAl1cXPDzzz8jPDzcovvRnNzo3Llz+Oc//ymmu3TpgieffNKi+3vU5s6dK/mh9O9//1uynI0cd+7csUgjT6VS1Vk/0ZjU1FTxsaurK/z9/SX/d3R0xLx588R0dXU1Ro4ciZycHJP2s1DnUL8AAAqISURBVH//fsmFiFra6/xdunRJ8h7R5fLly5Iu5nJoNoyOHj2KQ4cOGcy/YcMGbNmyRXb5CxYskKQnTpwoWTpJjnPnziE/P9+kbSzliSeewNNPPy2mjx49ir/85S+yx9vqy9eQz4vmxbLKykrMnDnTYCO9tLQUM2bMkH3OmjRpgldffVVMFxcXY9SoUSb3stizZ49J+S0tLCxMfHzy5EmrzfiruZ9jx47VqyzNu6Q7duyQTGY0bNgwi12UJTIXG6RE9KenUqkQHx8vXmVWKpVISkpCdHS0xfc1ePBgyV04zR9bDf3uKAD4+vpKZiIWBAHjx4/HnDlzDE5uolar8ccff2D69OkICwvD7t27612XwsJChIeHY/DgwVi/fr04ZlcXlUqFxMREScNs5MiROtcBnTZtmqSxcvXqVXTr1g3r16832HWuqKgI//d//4eYmBjExMTgwYMHOvMtXrxYMs5z0aJFePfdd3XO+puSkoKBAwfi3r17Jq0POGnSJEl67Nixkom1alVWVuKTTz7BxIkToVAoJN0RDRk0aBCmTp0qpgsKCtC7d28sW7bM4ERQpaWl2LhxI4YOHYouXbpYbEIhcyxfvlwy4+7nn3+OUaNGGZxw5+rVq3jnnXckx66pIZ+XKVOmIDIyUkzv3r0bI0eO1DnB0Y0bNzBkyBCcPHnSpPflO++8gy5duojpQ4cOoUePHti+fbvBhm1ubi6++OILREZGSi7o2ILmRcXc3FzMnj3bKhNYae7n6NGjWLRoEXJzc80qa+LEieJ7vaamRjJ52Z/he4kaPnYYJyKz7Nu3z+wxJ9euXZN0i7S2wsJCZGdni+mamhqMGTPG5HLkdOlTKBRITEzEX//6V8nz7u7uSEhIMHmf9mjixIlIT0/H+++/D+Bho3Tp0qVYvnw5evbsia5du8LX1xeVlZUoKirCpUuXcOrUKYMNRnMJgoDff/8dv//+O5ycnBAeHo7IyEgEBATAzc0NpaWluHz5MpKTkyXrT3p6emLx4sU6y3RwcMDGjRsRFxeHs2fPAnh4VzchIQFz5sxBbGwswsLC4O7uDpVKhby8PJw+fRoXL16UNdarZcuWWLp0KV5++WXxucWLF+Obb77BkCFD0Lx5c6hUKhw6dAhHjx4F8PDu0pw5czB//nxZ56Vfv354+umnsWvXLgDAzZs30bVrVwwaNAhdunSBUqnEjRs3sHv3bvFCwvz587FmzRrZY06/+uorZGVliRcXVCoVXnvtNbzzzjuIiYlBu3bt4OnpidLSUhQUFODcuXM4d+6czoa3LbRp0wbr1q3D6NGjxTpt3boVP//8M3r06IHu3bvD19cXFRUVuHXrFk6cOCHekdcci6ytoZ4XJycnrF69GjExMWKDZfv27QgLC8PgwYPRvn17qNVqXLhwAbt27UJVVRUA4OOPP8Zrr70max9ubm7Ytm0b+vbti5s3bwIA0tPT8cwzzyAkJAR9+/ZFs2bN4OrqipKSEuTm5uLUqVNIT08XG6xeXl5WOHr5EhISsGDBAvGCU20XWEdHR0njPCYmBr/99pvZ+5k2bRpWrFgh3oFduHAhFi5cCCcnJ0m3/4SEBJ1rjGpyd3fHpEmTsGzZMsnzYWFhiI+PN7uORBYjEBHJkJmZKQCwyF9mZqbOfYSGhop5Jk+eLLtuCxYskJRvrbrLdffuXaFx48aSbWfMmCF7+1rmnA/tY121apWs7VatWiXrNdK0du1awc3NzaxzuXz5cln1MsTc17Vp06bC0aNHjZZ///59YcKECWbtw9HRUbh3757B8pcsWSIolUqjZXl7ewu7d+82+TXKzs4W2rVrJ6u+r7zyilBTU2Pye66qqkp48803BYVCYfI5UigUwvnz53WWa877sZbmdgsWLDCaPzU1VWjatKlJdR8xYoRNzos5TH1NU1JSBE9PT6P1VCqVwocffmhWzMnLyxMGDhxo1mcrICBAb7mTJ08W84WGhso+R8nJyZJ9JCcnG8y/YcMGwcXFxWA9Y2Nj62wXGxtr8P/aPvnkE6MxQu53Q1paWp1t//Wvf8nalsja2GWXiMjCfHx8MHToUMlzf8ZuUQkJCbh+/TrmzZuHkJAQo/nbtm2L1157DYcPH0ZiYmK99x8SEoJNmzZh0qRJssZABQcH4+2338bly5fRo0cPo/nd3Nywfv16HDp0CM899xwaN25sML+zszNiY2PxySefIDs7WzLWVpc333wT+/fvR8+ePXX+38HBAcOGDcPx48frzFAqR0hICA4cOIBp06bBwcFBZ54OHTpg48aN+PLLL3V2XzbG0dERS5Yswfnz5yXdAvVxcHBAz5498d577yEjI8PiY7jNER0djfT0dLz33ntG30f+/v6YPn063nvvPYP5GvJ5iYmJwYULFzB69Gi9vWCioqKwc+dO/O1vfzNrH/7+/ti1axd+++03DBgwwGhXcVdXV8THx2PlypV2sVxMbRf4efPmITo6GgEBASZ1XZZr7ty5OHXqFObMmYOePXvC19dXcnfUFB07dsQTTzwhpp2cnPR2PSd61BSCYMKK2UREZFR1dTVCQ0PFiXB69eqFw4cP27hW1nf58mWcPn0aBQUFKC4uRqNGjeDl5YVWrVohPDwcgYGBVt1/dnY20tLScP36dRQXF6OyshLu7u5o2rQpIiIi0KlTJ3FZHnNUVFTgyJEjyMjIQGFhIcrLy+Hu7g5/f3+0a9cO4eHhRhut+ly5cgWHDx9Gbm4uXF1dERwcjD59+kjO2erVqzFlyhQxnZmZKZn4xJCioiLs3bsXWVlZePDgAYKDg9GpUyd0797drPrqo1arceLECVy+fBmFhYW4f/8+3Nzc4Ovri7Zt2yI8PNxo48zW0tLScPr0aeTn50OlUsHNzQ3BwcEIDw9HeHi4WQ33hnpeCgoKkJycjJs3b6KmpgYhISGIjIyss+ZqfZWWluLQoUPIyspCYWEhqqqq4O7ujsDAQLRv3x4dO3Y0uyFGDxUVFSE4OFgcyzx27Fhs2LDBxrUieogNUiIiC9uyZQtGjRolpletWoWXXnrJdhWiP4X6NEiJ6PH26aefYu7cuWI6OTlZsh4vkS2xyy4RkYUtXbpUfOzn54dx48bZsDZERPQ4U6vVktnRw8PD2Rglu8IGKRGRBe3cuRP79u0T0zNnzoSLi4sNa0RERI+zb775RrKc0Zw5c2xYG6K6uOwLEZEFlJaW4tdff8Xs2bPF5zw9PSVdpIiIiB6VkpISbNiwQfI91LJlS0yePNmGtSKqiw1SIiIzpaamYsiQIQAeNki1LV68GE2aNHnU1SIiosfU+vXrMXPmTAiCgLKysjr/X7p0qdFZjYkeNTZIiYjMpFardTZEAWDSpEmSu6VERETWVlVVpfd76R//+AeeeeaZR1wjIuPYICUisgCFQgFvb29ERUVh+vTpnMiIiIhsSqlUwtfXF7169cKrr76K+Ph4W1eJSCcu+0JEREREREQ2wVl2iYiIiIiIyCbYICUiIiIiIiKbYIOUiIiIiIiIbIINUiIiIiIiIrIJNkiJiIiIiIjIJtggJSIiIiIiIptgg5SIiIiIiIhsgg1SIiIiIiIisgk2SImIiIiIiMgm/h8guYr7RR8JGgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -936,20 +1017,20 @@ " print(split, acc)\n", " print(\"no sub\", split, acc_no_sub)\n", " if i ==0:\n", - " plt.scatter(i, acc, c='black', marker='^', linewidths= 8,\n", - " label =\"all metabolites\")\n", - " plt.scatter(i, acc_sub, c='green', marker='^', linewidths= 8,\n", - " label =\"metabolites in \\n training set\")\n", - " plt.scatter(i, acc_no_sub, c='grey', marker='^', linewidths= 8,\n", - " label =\"metabolites not in \\n training set\")\n", + " plt.scatter(i+0.01, acc, c='black', marker='o', linewidths= 8,\n", + " label =\"all test data points\")\n", + " plt.scatter(i, acc_sub, c='green', marker='o', linewidths= 8,\n", + " label =\"test data points with small\\n molecules from the training set\")\n", + " plt.scatter(i, acc_no_sub, c='grey', marker='o', linewidths= 8,\n", + " label =\"test data points with small\\n molecules not part of the training set\")\n", " ax.annotate(len(y_pred), (i+0.04, acc-0.02), fontsize=17, c= \"black\", weight = \"bold\")\n", " ax.annotate(len(y_pred_sub), (i+0.04, acc_sub+0.015), fontsize=17, c='green', weight = \"bold\")\n", " ax.annotate(len(y_pred_no_sub), (i+0.04, acc_no_sub-0.01), fontsize=17, c='grey', weight = \"bold\")\n", "\n", " else:\n", - " plt.scatter(i, acc, c='black', marker='^', linewidths= 8)\n", - " plt.scatter(i, acc_sub, c='green', marker='^', linewidths= 8)\n", - " plt.scatter(i, acc_no_sub, c='grey', marker='^', linewidths= 8)\n", + " plt.scatter(i+0.015, acc, c='black', marker='o', linewidths= 8)\n", + " plt.scatter(i, acc_sub, c='green', marker='o', linewidths= 8)\n", + " plt.scatter(i, acc_no_sub, c='grey', marker='o', linewidths= 8)\n", " ax.annotate(len(y_pred), (i+0.04, acc-0.02), fontsize=17, c = \"black\", weight = \"bold\")\n", " ax.annotate(len(y_pred_sub), (i+0.04, acc_sub+0.015), fontsize=17, c='green', weight = \"bold\")\n", " ax.annotate(len(y_pred_no_sub), (i+0.04, acc_no_sub-0.01), fontsize=17, c='grey', weight = \"bold\")\n", @@ -963,7 +1044,8 @@ "\n", "plt.ylim((0.5,1))\n", "plt.xlim((-0.2, 2.2))\n", - "plt.legend(loc = \"lower right\", fontsize=20)\n", + "leg = plt.legend(loc = \"lower right\", fontsize=20, frameon=True)\n", + "leg.get_frame().set_linewidth(3.0)\n", "plt.ylabel('Accuracy')\n", "plt.xlabel('Enzyme sequence identity')\n", "ax.yaxis.set_label_coords(-0.11, 0.5)\n", @@ -974,79 +1056,24 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "splits = [\"<40%\", \"40-60%\", \"60-80%\"]\n", - "colors = [\"black\", \"steelblue\", \"firebrick\"]\n", - "\n", - "for i, split in enumerate(splits):\n", - " \n", - " help_df = df_test.loc[df_test[\"identity\"]== split]\n", - " y_true = np.array(help_df[\"Binding\"])\n", - " y_pred = np.array(help_df[\"pred\"])\n", - " \n", - " \n", - " fpr_esm1b_ecfp, tpr_esm1b_ecfp, threshold = metrics.roc_curve(y_true, y_pred)\n", - " roc_auc_esm1b_ecfp = metrics.auc(fpr_esm1b_ecfp, tpr_esm1b_ecfp)\n", - " \n", - " plt.plot(fpr_esm1b_ecfp, tpr_esm1b_ecfp, colors[i],\n", - " label = 'Sequence identity level = %s' % split, linewidth=3.0)\n", - " \n", - "\n", - "\n", - "\n", - "plt.legend(loc = \"lower right\", fontsize=18)\n", - "plt.plot([0, 1], [0, 1],'--')\n", - "eps = 0.01\n", - "plt.xlim([0-eps, 1+eps])\n", - "plt.ylim([0-eps, 1+eps])\n", - "plt.ylabel('True Positive Rate')\n", - "plt.xlabel('False Positive Rate')\n", - "ax.yaxis.set_label_coords(-0.16, 0.5)\n", - "ax.xaxis.set_label_coords(0.5,-0.1)\n", - "\n", - "ax.locator_params(axis=\"y\", nbins=5)\n", - "ax.locator_params(axis=\"x\", nbins=5)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "<40% 0.9217156354983203\n", - "no sub <40% 0.5741492146596859\n", - "40-60% 0.9654536403710642\n", - "no sub 40-60% 0.6035042180402337\n", - "60-80% 0.9792075412761249\n", - "no sub 60-80% 0.6648936170212766\n" + "<40% 0.9242621631956458\n", + "no sub <40% 0.5850785340314136\n", + "40-60% 0.9737931865907773\n", + "no sub 40-60% 0.758349086326402\n", + "60-80% 0.9873770657502401\n", + "no sub 60-80% 0.7819148936170213\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1119,24 +1146,24 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "<40% 0.6769373885503904\n", - "no sub <40% -0.007061365100885804\n", - "40-60% 0.8062955638617622\n", - "no sub 40-60% -0.024677523532726422\n", - "60-80% 0.8525708082530685\n", - "no sub 60-80% 0.06316139407998893\n" + "<40% 0.704894184491135\n", + "no sub <40% 0.15710885673939606\n", + "40-60% 0.8387741340994049\n", + "no sub 40-60% 0.18898223650461363\n", + "60-80% 0.8811890319936004\n", + "no sub 60-80% 0.2711997843240443\n" ] }, { "data": { - "image/png": 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UlIQxY8Yor/fee6/E8hqNBj169FDOc3JysHbt2mLL63Q6rFq1ymiqY+/evW3vOBERERERWUW1IMrPzw8tW7YEIK9pev3115GZmWlVG5mZmXj99dcBAJIkoUmTJg/sSBQAdO7c2Wi64r59+7B27doim+nm5ubiq6++QkxMjHKtVq1aNmdBJCIiIiIi66ma33ry5Ml45plnIEkSTp48iSeffBL/+9//jKb6FefWrVsYPHgwTp48adSempKTk/H++++bvWeYGCM5ORnjxo0zW27ixIl49NFHVemPs7Mzxo8fj3nz5iE/Px8AsHXrVkRFRaFBgwbw8vJCSkoKTpw4YTTVz9vbG6NHj1alD0REREREZB3VRqIAYODAgWjbtq0y5Wzfvn2oX78+pkyZgujoaKNABZADl+joaLz99tuoX78+9u/fD0mSIEkSWrZsiaFDh6rZPQghoNPpzL5MFVfO1syDpmrUqIGxY8fCw8NDuZaamoq9e/di48aNOHjwoFEAFRAQgNdff/2BTbhBRERERFTRqb7T6i+//IIWLVrg9u3bAOSAYN68eZg3bx5cXV0RHBwMT09PZGZmIj4+XtkfSQgBSZIghEC1atXw22+/qd21Cqt+/fqYPn061q1bh6NHjyqjUobc3d3Rpk0b9O3bF25ubuXQSyIiIiIiAgBJqD20Ankvo4EDB+L48eOQJAlAyXtHGZZp1qwZfv75ZzzyyCNqd+u+kJubiwsXLiAlJQVZWVnw8vKCv78/wsLCVNkYNycnB5GRkVi4cCGDMSIiIiKiMlB9JAqQkx4cOHAA8+bNw+LFi5GUlGR0Xz/ipCeEQFBQEN544w28+eabcHZ2tke37guurq5o2LBheXeDiIiIiIiKYZcgCpCDgalTp2Ly5MnYunUrdu7ciVOnTiE5ORkZGRnw9vaGv78/GjZsiM6dO6Nbt25wdXW1V3eIiIiIiIhUYbcgSs/NzQ19+/ZF37597f0oIiIiIiIiu1M1Ox8REREREdGDjkEUERERERGRFRhEERERERERWYFBFBERERERkRUsTizx3XffGZ2/8MILpZZRg7nnEBERERERlReLN9t1cHBQNsUFAK1WW2oZNZh7DpUdN9slIiIiIrKN1dP5LIy5bHavnkNERERERGQNq/aJsiSwYfBDREREREQPMouDqMuXL6tShoiIiIiI6H5mcRBVs2ZNVcoQERERERHdz5jinIiIiIiIyAoWj0RVqlQJAODk5IT4+Hi7dYiIiIiIiKgisziISk1NlSs4Fl9Fo9EoZfLy8mzrGRERERERUQVkVXa+0ugz8zFDHxERERERPagsXhOlH2VigERERERERA8zi4MoPz8/AIBOp0NCQoLdOkRERERERFSRWRxEhYWFKcffffedXTpDRERERERU0Vm8Jqpr1644ePAgAODdd9/F2bNn0a1bNwQHBxdJNiGEwJ49e1SZ+texY0eb2yAiIiIiIlKLJCyMdG7fvo1HH30U2dnZEEJAkqQiZfRNmbtXps5JEu7evatKWyTLyclBZGQkFi5cCDc3t/LuDhERERHRfcfi6XxVqlTBDz/8AFdXV6PrQgjlZe5aWV6GbRAREREREVUkFgdRANCvXz8cPXoUL774Ivz8/OwW5DB4IiIiIiKiisrqfaLq1q2Lr7/+GgBw/fp1JCYmIjMzEzqdDl27dgUgp0Pftm2buj0lIiIiIiKqAGzabLd69eqoXr260TVJkiBJEjp16mRTx4iIiIiIiCoiq6bzWYJT8YiIiIiI6EFm8UjUiRMnAMgjTQ0bNjRbZsSIEQDk6XxEREREREQPIouDqCZNmkCSJDg6OiIvL89smZdeegmAeinOiYiIiIiIKhqr1kSVlna8c+fOpQZaRERERERE9zO7rIniuigiIiIiInpQWRxEubi4AAB0Op3dOkNERERERFTRWRxEVapUCYA80nThwgW7dYiIiIiIiKgisziIatSokXI8Z84cTtkjIiIiIqKHksWJJZ566ils3rwZAPDDDz9g//796Nq1K4KDg+HoaNyMTqfDrFmzVOngtGnTVGmHiIiIiIhIDZKwcEgpJycHDRs2xOXLlwHI0/pMU5nrm1IzxblWq1WtLZL/HiMjI7Fw4UK4ubmVd3eIiIiIiO47Fk/nc3Nzw4YNG/Doo48qwZI9p/RxuiAREREREVVEVqU4r1u3Lo4fP46VK1fi6aefxiOPPAIPD48iac3157a8iIiIiIiIKiKrNtsFAGdnZ7zwwgt44YUXitxzcJBjMkdHR+Tn59veOyIiIiIiogpG9c121VwPRUREREREVNGoHkRxKh4RERERET3IrJ7OV5JvvvkGQOG0PiIiIiIiogeNqkHUiBEj1GyOiIiIiIiowuGQERERERERkRVUHYkyJz09HXv37kV0dDSSk5ORlpYGIQS++uorez+aiIiIiIhIdXYLoi5cuIAPPvgAq1evxt27d5XrQghIkmQ2iNq1axcmTpwIAHB1dcWOHTvg4uJiry4SERERERFZzS5B1LfffotXX30VOTk5StBkSda+Dh06ID09HZcuXYIkSVi3bh2GDh1qjy4SERERERGViepror799lu89NJLyM7ONrru7+9femccHDBq1Cjl/JdfflG7e0RERERERDZRNYg6d+4cRo8eDUDedDcwMBBLly5FYmIiEhMTodFoSm1j8ODBAORpfzt27OC+U0REREREVKGoOp1v2rRpKCgogCRJqFmzJnbv3o1q1apZ1cYjjzyC0NBQXLlyBampqTh37hzCw8PV7CYREREREVGZqTYSlZWVhd9//105/+6776wOoPSaNWumHJ87d87mvhEREREREalFtSBq7969yM/PhyRJaNKkCSIiIsrclmHwdfPmTTW6R0REREREpArVgqirV68qx+3bt7epLV9fX+U4IyPDpraIiIiIiIjUpFoQlZycrBxbkomvJHl5ecqxg4PqCQSJiIiIiIjKTLUIxcvLSznOysqyqa24uDjl2NaAjIiIiIiISE2qBVFBQUHK8YULF2xqa+/evcpx1apVbWqLiIiIiIhITaoFUfqMekII7NmzB/n5+WVqZ+/evbh48aLcOQcHtGvXTq0uEhERERGRJXJzgffeA3r0AEJDAS8vwMkJCAgA2rcHPvwQSE8vWk+rBZYvByIiAD8/wM0NqFMHeOMN4PbtouU7dwYkqfRXaGjRunv3AsOGAbVqyc/R9y8iAvjvf4HsbJU/lEKSUHE327CwMFy6dAmSJOHTTz/Fa6+9ZnTfyckJWq0WkiRBq9UWqV9QUICIiAhERUVBkiS0bt0a+/fvV6t7BCAnJweRkZFYuHAh3Nzcyrs7RERERFQRJSUBgYEll3n0UeDgQTlYAuTAq18/YMsW8+UrVQI2bwZatCi81rkzsGtX6f0JDQUuXy48X74cGDcOKCmUadIE2LcPcHcvvX0rqbrZ7muvvYZJkyZBCIH33nsPrVu3RsuWLS2qm5OTg+eeew5RUVHKtYkTJ6rZPSIiIiIislRICNCuHVCzphwAJSUBv/wC6LNynz8PfPEF8M478vn77xcGUBoNMHIkUKUKsHIlcO0acOcO8MwzwKlTgIeHXG7cOOCpp4o+OysLmDGj8Lxnz8LjggL5mfoAyttbfpafH7B2LXDypHw9Olo+f+EFlT6QQqoGUePHj8fixYtx5coVZGZmomvXrpgxYwZGjx5tlHjCUHZ2NtauXYs5c+bg4sWLkCQJANC0aVM888wzanaPiIiIiIgsERAA3LhR9Pqbb8rBld6VK/KfKSnAZ58VXn/nHeCDD+TjYcOAxx6Tg54rV4DvvwfGjpXvDR5s/vmLFxceOzgAkyYVnicnA2lphedTpwKTJ8vHI0YYT/1LTCzhTZadqvnDnZ2d8fvvv8PLywuSJCErKwtvv/02goOD0bp1a+h0OqXsCy+8gPbt28Pf3x8vvfQSYmNjAchrqvz8/LB27Vo1u0ZERERERGWl1QI3b8rT6AzVry//uWWLPJ1Pb+DAwuO6dYEGDQrP//ij5GfpdMDChYXnffvK66r0goIAwwze27fL661ycuSRJz2NBujWreRnlZGqI1EA0KBBA/z1118YPHgwbt68CQDIzc3F4cOHAQCSJEEIgVWrVgGQgybD6yEhIfjtt98Qam7xGBERERER3Ts7dwJdupi/17EjMGqUfHzihPG9WrWKnuun2ZmWNbVuHXDpUuH5W28Z33dwAD7/HHjuOXlq36ZNgGlG72rV5ECsceOSn1VGdtnJtl27djh27BheeOEFODrKcZph/gr9lD1DkiRh6NChOHz4MJo3b26PbhERERERkRqGDQM2bABcXeXz5GTj+97exueGS3uSkkpu+5NPCo/btpXXZZl69llgxw6gcuWi9zQaeSSsY8eSn2MD1Uei9AICArBy5UrMmTMHq1evxq5du3Ds2DEkJycjPz8fDg4O8PPzQ7169fD4449jyJAhePTRR+3VHSIiIiIislbt2sC8eUBenpxQYt06OWD68Ufg6FF5FKhmzaJZ8ko6NzOgoti/X874p6df62Tq55/l9U85OXJ68xdflLMJ/vILcPo08Omn8rTBqCjjqX8qUTXFuaVycnLg6upqdkSK7IspzomIiIiozBIS5NTh+j2f+vcHfv1V3lPqww8Ly6WkAL6+hed9+wJ//ikfV60qr68yZ8AAuT0ACAsDzp2Tp++Z9uGRRwr3gVq5Ug6oADnhRGgokJoqn//nP8CcOWV5pyWyy3S+0ri5uTGAIiIiIiK63wQFAW3aFJ7v3Cn/2aiRcbmLF4s/Ny1rWOb33wvPJ04sGkABwIEDxhvptmpVeOzjY5yEIjra/LNsVC5BFBERERERVWBbt5pPD56UBBw6VHiuHxjp0QNwcSm8bpgl7/Rp4MyZwvO+fc0/c8ECOTMfIE/Be/FF8+W0WuPzfxPYAQDS04F/s34DkKf62YHd1kQREREREdF96rPPgI0b5eCocWPA3V2egvfLL0B8fGE5/Ua5lSrJG+fqU5PPmydvrlu5MvD114Xla9QAnn++6PPu3AG++abwfPx4+ZnmtG0LODnJmfkA+bn//CMHXuvWyVMJ9Xr0sPqtW+KeBFG3b9/G/v37ERMTg5SUFGRkZMDLywt+fn4IDw9Hu3btUKVKlXvRFSIiIiIiskR+PrB+vfwyp0kT40x6//d/chrz7dvl0aIvvjAu7+srJ4Tw9Cza1tKlhVP0XF2B114rvl9VqsjP0qc+z8oCliwpWq5bN+Cll4pvxwZ2C6KEEPjxxx/x6aef4siRI6WWb9myJSIjIzF48GCulyIiIiIiKk+vviqPIh06BNy6JY8UOToCwcHymqann5b3aXJyKqzj5iZn61uxAvjuO3kaX16evGdTr17AlClASEjRZ+XnGwdBzz8vr70qyeTJQMuW8ua/Bw/KfdRqAT8/oGFDYOhQOYDSaNT5PEzYJTvftWvXMHz4cOzfvx+A8Ya6pkz3j4qIiMD333+PGjVqqN0tArPzERERET00rl6VkzDExsqZ7tq2ldORk81UH4m6cuUK2rVrh/j4eAghjAKnkuI1SZIghMCePXvQtm1b7N+/HzX5l0xEREREZJ2sLGDGDDlRg2ESBo0GmDQJmD4d8PAot+49CFQNovLz89GrVy/ExcUZBU+PP/44BgwYgGbNmqFy5crw9PREZmYm4uLicPToUfz666/4+++/lfK3b99Gr169EB0dDWdnZzW7SERERET04BJCng6n32vJkFYrJ3y4eFHOnsclNGWmaorzxYsX49y5c8qoUuPGjREVFYWtW7di3LhxaN26NWrWrAl/f3/UrFkTrVu3xrhx47BlyxZERUWhcePGymjVuXPnsHjxYjW7R0RERET0YFu2zHwAZWjdOrkclZmqa6Jq1KiBm//uPty6dWts2bIFnuaybxQjKysL3bt3x8GDBwEA1apVw7Vr19TqHoFrooiIiIgeWFlZQK1aQEJC6WWDgoDLl4tPI04lUm0k6tSpU7hx4waEENBoNPj++++tCqAAwMPDA99//z0cHeVZhjdv3sSpU6fU6iIRERER0YNr2TLLAihALsfRqDJTLYg6fvw4ADlBRNeuXVG7du0ytVO7dm107dq1SLtERERERFSMrCzg44+tq/PRR4V7M5FVVAuiEgyi3kaNGtnUlmH9BEujaSIiIiKih5U1o1B6HI0qM9WCqIKCAuXY1ox6hvUN2yUiIiIiIhNlGYXS42hUmagWRAUGBirHFy9etKktw/qG7RIRERERkYmyjELpcTSqTFQLovRroIQQ2LRpE7KyssrUTlZWFjZu3FikXSIiIiIiMqHf+8kWH39svCkvlUq1IKpdu3bw8vKCJElIT0/H5MmTy9TO5MmTkZ6eDgDw9PREu3bt1OoiEREREdGD5fBhID7etjbi44EjR9Tpz0NCtSDK0dERgwcPhhACQgh88cUXeOONN5CXl2dR/by8PLzxxhtYvnw5JEmCJEkYMmSIku6ciIiIiIhM7N5tl3Zyc4H33gN69ABCQwEvL8DJCQgIANq3Bz78EPh33ENx4wYwaxbQty9QtSogSYWvGTPMP7ZzZ+Nyxb1CQ83Xz80FPv0U6NhR7puzM1C5MtC2LfDuu/Zb7qVaEAUAM2fOhIeHByRJghACS5YswWOPPYZPPvkEFy5cMFvnwoUL+O9//4t69ephyZIlAOQpge7u7phR3KdNRET0oCrLNxcAuHYNmDABqFsXcHOT6zVtCvzf/5n/FnHzJvD558CzzwINGhR++wgOBp58Evj11+L7aO2ziMh+fHzs0k5mpvyfm61bgatX5fO7d4HkZGD/fvk/Uy1bAikphXUOHwamTwf+/BO4fVudbulJUtFrsbFA48ZAZCSwZ4/ct4ICeWDt4EFg7lzgzh11+6Gn6jBPlSpV8MMPP2DQoEHQ6XQQQuDKlSt4++238fbbb8PDwwNBQUHw8PBAVlYWEhISlLVTQghI/346jo6O+PHHH1GlShU1u0dERFTx6b+5mNJ/c9m/H1i5Uv6G4Ocn39u9G+jTp2hwFR0tv378Edi+XQ6Q9L7/Xv6Z1lRCArBxo/waM6bogvOyPIuI7KduXXXaefTRIpdCQoB27YCaNYFKlYCkJOCXX+SgCgDOnwe++AJ4553COp6e8m8qLVoACxaU/thx44Cnnip6PSvLePSqZ0/j+xkZQK9eciAFAN7ewIABwCOPyMHerVtyUKfRlN6HslB9rly/fv2wevVqvPLKK0hLS1MCIyEEMjMzkZmZWaSOfvqeEAI+Pj5YsWIF+vTpo3bXiIiI7g/WfHPJzJRHk/RBTXAwMHIkkJ8PrFgBpKUBp0/L1zZsKPqsatXkkacaNYBz54DVq+VvIACwfDkwaBDQrZt8buuziEh9ERGIq9YclW+UfU1TXLXmqBwRYXQtIECenmfqzTfl/0TpXblSeNyrl/yfAYd/57pZEkQNHmz++uLFhccODsCkScb3P/mkMICqX1/+fadSpdKfpxa7LDgaNGgQWrdujffffx8//fRTkXVR+oBJTwgBFxcXDBkyBLNmzUL16tXt0S0iIqKKz9pvLhs2GC8qX70a6NJFPm7ZEhgyRD7+6y/g2DH5J2JADtB+/FEOigx/qu3RA3j++cLzjRsLg6iyPouI7EYLDUZkL8MfaA8X5FtdPw/OGJG9DH9Bg5IGbbRaIC5O/v3GUP36hccuLlY/3iydDli4sPC8b1+gTh3jMt98U3j85JPAqFHAoUPy9L1atYChQ4HJkwFXV3X6ZMpuWRuqV6+Ob7/9Fv/973+xadMmHDhwAOfOnUNKSgoyMjLg5eUFPz8/1K1bF+3atUPPnj0REBBgr+4QERHdn0r75nLpkvH1hg0Ljxs1Mr63fn1hYDN0qPnn9e9vfJ5v8KWsrM8iIrs5fBjYcqcFWiIKP+A5NMJJi+ueQEMMxyqcutMQR44ArVoVLbNzZ+FvJaY6dpSDF7WtW2f8n5u33jK+f/OmvDRTzzTD+5kzwNSp8u8+27cD7u7q99Huqe8CAwPx/PPP43nDX7WIiIioZJZ+c/H1Nb538mRhvRMnjO+dPl36c8+dMz5v2bLwWO1nEZHN9En1TqIRWuAwmiAaLRGFajAzov2vG6iGKLRENJqgAM5KO+aCqOIMGybP+LXHSM8nnxQet20rz242ZC5pRatW8nTCffuAbdvkawcPAnPmyDlv1Mb84URERPcT028uvXoBjo6F65iGDpXXJBUUAF9+aVzXMI2WOVlZwPjxheePPipP99NT81lEpArDpHoFcEYUWiEKVkRDZtoxVLu2PNKTlycvy1y3Ts5z8+OPwNGjwKZN8uxgtezfLwc/eua2ns03mbVYpYocBLq4AELIv/3ot71atYpBFBER0cPD0m8uoaHAxx8XrrqOjzef3Q+QU5gXJzFRXnjwzz/yeXCwnKfY8GdmtZ5FRKqxY3I+AED16saBzJw5QJMm8mhQTIycXrykHRGs9d//Fh6HhRWdYQwUHRRv27ZwPZYkyYP1+iDq2jX5dx4nJ/X6CKi8TxQRERGpRP/N5T//kddDnTkj/9wKFH5z0Zs4EdiyRc4B7Ocn793UqBEwfz4QHl5YrmpV88+6cEH+FqL/+bd6dXk6oblvVbY+i4hUFREBNG9uWxvNm8vtWCIoCGjTpvB8507bnm3o4kXg998LzydOLMz0Z6hOnZKTWBjuKeXoKL/UpmqTFy5cwNKlSwEA7u7umDNnjtVtvP/++8j+d6O+CRMmILS47YmJiIgeJvpvLvqffE2/uXTvLr8MxcUZ/4RsurAAkBcQ9Osnj3IB8k/M69cbZwI0VdZnEZHqNBp5O7f27YtOc7OEs7Nc33Q/pa1b5f8cBAYaX09KkrPg6ZnbBLesFiyQM/MBgL8/8OKL5ss5OcmJRP/8Uz4/eFB+7/oBcP06MUAOENXso56qQdSiRYvw2WefQZIkvPrqq2VqIz09HUuWLIEkSdBoNJhnmm6DiIjoQVaWby537hTdICUnB3jllcJvJD4+8k6Uhn7+GXjhBSA3Vz5/8kngf/+Td8ssTlmfRUR206IFEBUFPPecnO/FUg0bymuGDBNt6n32mbzDQY8eQOPGcoa7mzflLesMdzow3Cj34kXg3/GUIrZskbeaA+Q1S6b7Q925Y5y2fPz4krPqvfuunH1Pp5M31u3YUV62uX+/nLFQb8KE4tuwhSQMN2yyUUhICG7fvg1JknD06FE0btzY6jZOnjyp1KtRowauGO7gRTbLyclBZGQkFi5cCDc3t/LuDhERmerf37JvLs8/D3z3nXw8ebI8B+bxx+XNc+Pj5W8Xly8Xll+2DBgzpvD855/lbzH6rwFBQfJaJ9Ofo6tXN/62U5ZnEdE9kZ8PREfLAZW57eb0qlWTA5kmTYpfvti/v/HUOnOaNJGDI/1vPiUlFTU0YgSwcqXxtQ8+AN5/Xz52dZWXggYFldzO4sXAG28U/mfM1OuvA4sWld6fslBtJOrcuXNKAFWjRo0yBVAA0LBhQzzyyCO4fPkyrl+/jtjYWISFhanVTSIiooovP1+eUrd+vfn7TZoY5wAGgNhY+WVKkoDp04sGNadPG3/zSEgApkwpWr9Tp6I/GVv7LCK6J5yd5VTf1qQqL86rrwKVK8sD4LduySNFjo5yzplGjYCnn5ZHvtRI2JCfDyxZUnj+/POlB1CAHCQ1by4vydy7V56V7O0tB4jjx8u5cuxFtSDqpMHYYSsb/+ZatmyJy//+onXq1CkGUURE9PAoyzeXp54Crl+X57AkJMipqKpUkQMg/bcMtdzLZxFRuTG39LE0nTsXPypUEmdn83s/WaJdu/JZgqlaEHXDYMywpo3J4g3rX79+3aa2iIiI7rmrV4EDB+TRmrAwOfOdpf9vLOs3l86draszY4b8slZZnkVE9IBRLYjSZ9QDAA8PD5vacjdYRZapX4FGRERU0WVlyYHJggWAVlt4XaOR1xtNnw7Y+P9IIiIqf6oFUd7e3srxnTt3bGrLsD6THxAR0X1BCHkiv7ldJ7VaeePcixeBtWvtk2+XiIjuGdU22w0ICFCOT1qTW9EMw/qBpileiYiIKqJly8wHUIbWrZPLERHRfU21kagmTZoAAIQQ2L9/PxISEhBkSVoNE/Hx8di3b59yXr9+fbW6aFZWVhZiY2ORmpqKnJwc+Pj4IDAwELVq1YKDuS2S7Uin0+H27du4fv06MjMzkZ+fD1dXV3h5eaFGjRoICgqCxF8viYgqHv00PkvMmCHn9y1pAxQiIqrQVAuiwsPDUbVqVdy+fRsFBQWYOnUqli9fbnU706ZNQ0FBAQB5dEsfnKktPj4e69atw6lTp3D37t0i9318fNChQwf06tULjo6q7klcRE5ODjZv3ox9+/YhPT292HL+/v7o2LEjHn/8cTipkU+SiIjUsWyZnKnOEgkJcvlJk+zbJyIishtVh1peeuklCCEghMCKFSuwcOFCq+rPnz8fX375JSRJgiRJGDFihJrdUxw8eBAffPABoqOjzQZQAJCWlob169dj7ty5SEpKsks/AODixYuYMWMGNm7cWGIABQDJycn49ddfMXv2bNwuax5IIiJSV1YW8PHH1tX56CPAICETERHdX1QNot588034+PhAkiQIIfDmm2/i2WefxZkzZ0qsd+bMGTzzzDN46623lLpeXl5455131OweAHm91cqVK5GXl6dcCwoKQseOHdGzZ080b97caJTn+vXrWLJkCXJyclTvy/Xr17Fo0SKkpqYq1yRJQlhYGB5//HE89dRT6Nq1K2rXrm1ULz4+HvPnz7c5gQcREanAmlEoPf1oFBER3ZckIcqyJVbx/vjjDwwYMEAZkdKv4WnYsCHatGmDqlWrwtPTE5mZmbh58yYOHTqkJJLQd8XBwQFr165F//791ewa0tLSMG3aNOTm5gKQA5aBAwfi8ccfN1r/lJGRgS+++ALnz59XrrVs2RKjRo1SrS9CCMydOxdXrlxRrlWtWhWjRo1CSEhIkfLXrl3DihUrEB8fr1xr0qQJxo0bZ9Vzc3JyEBkZiYULFzLzIRGRrbKygFq1rA+iACAoCLh8mWujiIjuQ6ov9unbty+WLFmCCRMmQPvvHhlCCJw4ccJs1j594KQPthwdHbF48WLVAygA2LBhgxJAAUCfPn3Q3cyGhl5eXpgwYQI++OADZdrc4cOH0aNHD9SoUUOVvly5csUogHJ3d0dkZCR8fHzMlq9RowYmTpyImTNnKqNix48fR0pKCvz8/FTpExERWakso1B6XBtFRHTfskv6ubFjx2L79u2oXbt2kSDJlP66EALh4eHYuXMnxowZo3qf0tPTjbL+BQYGomfPnsWWd3JywpAhQ5RzIQT++usv1fpz9uxZo/MOHToUG0Dp+fn5ISIiwqhPhqNlRER0D+n3frLFxx8bb8pLRET3Bbvl8O7QoQPOnTuHNWvWoH///qhUqZIyxc/w5e/vjwEDBuCXX37B6dOn0a5dO7v0xzSJRIcOHaDRaEqsEx4ejsqVKyvnp06dMlpLZQvDdVAAUKtWLYvqma6PMm2HiIjukcOHAYMp1mUSHw8cOaJOf4iI6J6xa+5uSZLw7LPP4tlnnwUA3Lp1C8nJycjIyIC3tzf8/f1RpUoVe3ZBceLECaPzZs2aWVSvWbNmyghUQUEBzpw5g6ZNm9rcH9OlaM7OzhbVc3FxMTrnvlFEROVk92712mnVSp22iIjonrDvBkgmqlatiqpVq97LRypiY2OVY29vbwQGBlpUz3Tk58KFC6oEUQEBAUbnlmbaM023XpYNjYmISAWlTMG+5+0QEdE9Y7fpfBVJamqqUYry6tWrW1zXtKxa+zPVr1/f6Pzw4cMW1TMs5+Ligrp166rSHyIispJa//199FF12iEionvmoQii4uLijM4rVapkcV1vb284OhYO2MXbOv/9X9WqVUPDhg2V87Nnz2LXrl0l1tm+fTvOnTunnHfr1o1pyomIyktEBNC8uW1tNG8ut1OKu3flopJU+HrxxaLltFpg+XK5ST8/wM0NqFMHeOMNoLjfADMygLlzgXbtgEqVAEdHOet6WBjwwgvAoUPF9ys3F/j0U6BjRyAgAHB2BipXBtq2Bd59l/sJE9GD655O5ysvpskXrEkJLkkSfH19lWl0KSkpqvXr+eefx8cff6y0/eOPP+LChQvo0KEDatSoAVdXV+Tm5uLKlSvYtWsXjh07ptRt2LAhevfurVpfiIjIShoNsGwZRPv2kPLzra4unJ0hLVsmt1OKDz8Ejh4tuUxuLtCvH7Bli/H12Fhg0SLghx+AzZuBFi0K76WlAW3aADExxnVycoCLF+XXqlXAt98Czz1XtN3evQHTJLHx8fLr4EHg1Ve5DRYRPZgeiiDKNKOeaXKG0ri6uirHOp0OBQUFcHJysrlfPj4+eOedd/Djjz8qAVJUVBSioqJK7MsTTzyBnj17Gm0QTERE5aBFC3z3WhSazn8OjVB0L8TinEBDRE9YhRdaNCy97Alg9uzS23z//cIASqMBRo4EqlQBVq4Erl0D7twBnnkGOHUK8PCQy33xhXEA1akT0K0bcOMG8NVX8giYTgdMm2YcRGVkAL16yYEUAHh7AwMGAI88Ite5dUtOXmhBfEhEdF96KIIoww12AVgdABlO5wPkoEyNIAqQpwuOHTsWJ0+exKpVq0oc6QoMDMTQoUOLrKciIqLykZUFvP1DI6TgMJogGi0RhWq4UWz5G6iGKLRENJrA7ztnDJpZ8khNQQEwYoT8Z4sW8pS8mzeLlktJAT77rPD8nXeADz6Qj4cNAx57DBACuHIF+P57YOxY+d7Fi4V1fHyA7dsLA5+8PDkAA4DEROPnffJJYQBVv76cYNCKmfJERPe9hyKIMtwfCigaFJXGtHx+GaZtFCc1NRU//fQTjh49WiTtuanExEQsWrQItWrVwvPPP19umQ6JiEi2bBmQkAAAzohCK0TB8lTlCQly/UmTii/zwQdAdDTg4iJPqXvySfPltmyRp/PpDRxYeFy3LtCgAXDy34GyP/4oDKLq1Sssl5EB/PUX0KMHcP268Voo073pv/mm8PjJJ4FRo+Tyd+4AtWoBQ4cCkycDBhM5iIgeKA9FEGUaBJkGVaUxLW/pnk6luX79OhYuXIjMzEwA8vqrli1bol27dqhevTrc3NyQk5ODa9eu4cCBA4iKioIQApcuXcKHH36I119/HY8yqxMRUbnQaoF582xr4+OP5aQP5qa9RUcD//d/8vGsWcYBjymTrRBhun97rVqFQZRh2VdeAdasAQ4ckKft9e1rXE+jAQYNApYuLbx286Y8PVDP9DM4cwaYOhXYsEEe2eKaKCJ6ED0Ui2pcTX4KKygosKq+aRBl7Zoqc7KysrBkyRIlgNJoNBg/fjxefvllPPbYY/D09IRGo4Gnpyfq1auHl19+GePGjYPm3//T5ufnY9myZUhLS7O5L0REZL3Dh+UECraIjweOHCl6vaBAzr5XUCAnfnjzzZLbSU42Pvf2Nj738io8Ntxu0M0N2LlTHkky57HH5FElw3xM5rL8tWoFTJ8ur6fSO3gQmDOn5H4TEd2vHoogyjToMU00URrDNVUODg6qrIfauHGjUdbAfv36oVGjRiXWady4Mfr166ecZ2Vl4a+//rLoeQUFBcjJyVHeS25uLnJycqwOKImISLZ7t/3amT0bOH5cng63cmXpCRpMZ4OXdC5Jhcfp6XKCiBUr5PNWrYAZM4CXXpJTnZ86BfTvL6+B0jOd0V6livweZsyQpxUaZn1ftarkfhMR3a8eiul8vr6+RufWpCkXQhgFO9akRy+pzYMHDyrnLi4u6NKli0V1u3Tpgg0bNiiB4D///IPBgweXmqlv06ZNWL9+vXI+ZcoUAMBTTz2FPn36WPsWiIgeej4+9mnn+nU5pTkgr4myZE9ff3/j84wMwPB/fenphceGCSBmzQL+/ls+DgsD9u2TgycACA2VR5cAeXreK6/II1wm/0tF27bymi1ADtA6diwcXbt2TR5NUykXExFRhfFQjERVqVLF6DzZdN5DCdLT042m81WuXNnm/iQmJiIjI0M5Dw0NtXidlbOzM0JDQ5Xz7OxsJJqmTTKjZ8+eWLhwIebOnQsAmDt3LhYuXIiepquFiYjIIpYEN5YwXdqanCynCQfkaXyGG+xevVpY7ttvCzfdNZ3IYJh1z/TcsOz27YXHzZoVBlCA8X5SOTmF+0HVqVMYNJljONLl6GjcJhHRg+KhCKJ8fHzg5uamnF+/ft3iutcMV89CnSDKMIAC5P5Zw9tksrt+XVVJnJyc4ObmpqwPc3V1hZubm2qp2omIHjYREcZT18qieXO5HVv16GEc2KxdW3h8+rSc7EHPMHmEVlt4HB1tfG66Vkv/v1EnJ/l5egcPGk/xM5ye2Ly5cVBFRPSgeGh+HwoLC8PJf1MTZWRkIDExEYGBgaXWu2jyc16dOnVs7otp4GJtynTT8mplCyQiIstpNHKK8vbti64TsoSzs1zfdL2Tr69xinJDGzcC2dnycc2a8mhRy5byFL1x44CFC+V78+bJ6cYrVwa+/rqwfo0awPPPF5537FiYte/8efn8iSfkaXjffVdYrlo1OcmE3rvvytn3dDp5Y92OHeW1Vfv3ywk39CZMsOYTISK6fzw0QVTjxo2VIAoAjhw5YtFUtqNHjyrHTk5OqFdSjlkLeRmmSQIQFxdnVf3bJqmRTEemiIjo3mjRAoiKAp57rjAYsUTDhnLShYYNi94LDTUeSTK9p5/S17lz4Wa4gJwO/eRJeYqeVgt88YVxXV9f4OefAU/PwmtTpwKbNhVO99u/X34ZcnGRE08YLr1t21YO2N54Q05aceiQ8b5SAPD66/JGv0RED6KHKohas2aNsr5p79696N69u5Iy3JyYmBjEG+SvbdCggSrpzf38/ODr66skrIiLi8P169dRvXr1UuteuXIFCfLOjgAAf39/q6cDEhGReho1kkdfoqPlgOrGjeLLVqsmjxw1aSKPRKnJzU0OiFaskEeRTp8G8vLkZ/bqBUyZAoSEGNcJDgaOHgWWLAH+/BM4d05OQuHiAlSvDnTpAkRGml//9frr8nS9+fOBvXvltVze3vL7Gz++6J5TREQPkocmiPL29kZERAR27twJQE7usGnTJvTu3dts+YKCAqxZs0Y5lyQJTxa3VTyApKQk/Oc//1HO/f398X/6XRLNaNSoEXYbTBxfvXo1Jk2aVGRjYNM+rV69ukg7RERUvpyd5fTgrVrZ9zlXrpR839ERGDtWflnK2xt47z35Za127eQXEdHDxm6JJS5fvoxZs2ahV69eCA0NhY+PDzQajVWvkgKKsujVq5fRxrt//vkntm7dCp1OZ1QuIyMDixYtMpo216JFC9SoUUO1vjzxxBNG7+/ixYv49NNPi820FxcXh/nz5+OKwf9BnZyc8MQTT6jWJyIiIiIiKp3qI1GZmZmYNGkSvv76a4h/d/cTprv+lRNfX1+MGjUKn332GYQQEEJg7dq12L17N8LDw+Hh4YGEhAScOHHCaBPaKlWqYPjw4ar2JSAgAIMHD8Yqg50Iz58/j2nTpiEsLAzVq1eHm5sbcnJycPXqVVy8eLHI5zh8+HBV9q0iIiIiIiLLqRpEZWdn44knnsDBgwchhIBUAfOaNmzYEC+++CJWrVqlZLlLSEgwWmdkqHr16hg7dqxRinS1dOzYEQDw008/KUGbTqfD+fPncV6/IYcZLi4uGDJkCNq2bat6n4iIiIiIqGSqBlEzZ87EgQMHIEkSJEmCEAKVK1dG+/btERoaCg8PjwoRWLVp0wahoaH49ddfcfLkSWgNN8b4l4+PDyIiIvDkk0+qPq3QUMeOHREeHo5t27bh0KFDyM3NLbasq6sr2rRpg+7duyMgIMBufSIiIiIiouJJQqW5djk5OfD390deXh6EEPD29saSJUswbNgwODhU3D19MzMzcfHiRaSkpCA3Nxfe3t4ICAhAWFjYPe+3TqfDjRs3cPPmTWRlZSEvLw8uLi7w8PBAtWrVEBISYnOfcnJyEBkZiYULF9pldI2IiIiI6EGn2hDLrl27lFEUSZKwbt06dO3aVa3m7cbT0xONGzcu724AABwcHFCjRg1VE1gQEREREZG6VBtquXTpEgA5gIqIiLgvAigiIiIiIiJrqRZEpaenK8etW7dWq1kiIiIiIqIKRbUgKjg4WDnmWhsiIiIiInpQqRZE1a5dWzmOi4tTq1kiIiIiIqIKRbUgKiIiAlWrVgUA7Ny5U61miYiIiIiIKhTVgigHBwdMmDABQgjExsbijz/+UKtpIiIiIiKiCkPVjZDeeustdOrUCUIIjBo1CjExMWo2T0REREREVO5UDaIkScJvv/2G7t27IykpCa1bt8aiRYuQnZ2t5mOIiIiIiIjKjWqb7QLAyJEjAQBBQUFwdnZGRkYGJk6ciHfeeQfNmzdHSEgIPDw8LG5PkiR89dVXanaRiIiIiIjIJpIQQqjVmIODAyRJMrqmb970emmEEJAkCVqtVq3uEYCcnBxERkZi4cKFTEVPRERERFQGqo5E6ekDIKBo8GRJzGZtwEVERERERHSvqBpE1ahRgwEQERERERE90FQNoq5cuaJmc0RERERERBWOqtn5iIiIiIiIHnQMooiIiIiIiKzAIIqIiIiIiMgKDKKIiIiIiIisYJcU56a0Wi2io6MRExODlJQUZGRkwMvLC35+fggPD0eTJk2g0WjuRVeIiIiIiIhsYtcgat++ffj000/x119/IScnp9hybm5u6NOnDyZMmIC2bdvas0tEREREREQ2sct0vvT0dAwbNgwdO3bEL7/8guzsbAghimy0q7+WnZ2Nn376CRERERg+fDjS09Pt0S0iIiIiIiKbqR5EJScno02bNvjf//6nBE2GG/DqAyfDgEp/XwiBNWvWoG3btrhz547aXSMiIiIiIrKZqtP5hBDo27cvYmJiIEkSJEmCEAJhYWF4+umn0axZM1SuXBmenp7IzMxEXFwcjh49it9++w0XLlxQyp89exZ9+/bFnj17jAIwIiIiIiKi8qZqEPXNN9/gwIEDSjBUrVo1LFq0CP379y+2zuDBg/HRRx/hjz/+wOuvv44bN25ACIEDBw5g5cqVeOmll9TsIhERERERkU1Unc730UcfKcd169bFoUOHSgygDPXt2xcHDx7Eo48+qgRhhu0RERERERFVBKoFURcvXsSFCxcAyGucVq1ahSpVqljVRpUqVbBq1Srl/MKFC4iNjVWri0RERERERDZTLYiKiooCIAdQ7dq1Q7NmzcrUTrNmzdC+fXvl/PDhw6r0j4iIiIiISA2qBVEJCQnKcYsWLWxqq2XLlmbbJbonJKn0140bctmdOy0rL0nAypVFn7V6NdC9O+DvDzg7A1WrAs88Axw4YFlf160r+pwrV1T6IIiIiIjIHNWCKMPNdD09PW1qy8PDw2y7RPc1w0yTd+/KwdKwYcC2bcCdO0BBAXD7NrB2LdC+PbB4ccntJScD48bZt89EREREVIRq2fkCAgKU46tXr9rU1rVr15Rjf39/m9oiKjM/P+C998zf8/WV/6xdG5g3z3yZ334D9u2Tj52cgMcfL7y3YIEcLOn17Qs0bw7s3g1s3w4IAURGAi1bAm3amG9//HiAI7VERERE95xqQVSNGjUAyHtFbdq0CXfv3oWjo/XN3717Fxs3blTOa9asqVYXiazj7Q1MnlxymerVzZfJzwfmzy88HzwYqFat8PzrrwuPO3YEfv9dPhYCaNAAOHMG0OmADz8svGdo7Vrgp5/k4/795YCNiIiIiO4J1abzRUREwNXVFZIkISkpCR988EGZ2vnggw+QmJgIAHBxcUGHDh3U6iKRdeLi5CDJyUkelerQAVi6VJ6KV5off5Sn5umZBlqXLhUeN2xYeCxJchClt2WLPM3PUGKiPAoFAM8/D/TrZ9n7ISIiIiJVqBZEubm5oU+fPhBCQAiB2bNnY8GCBVa1MX/+fMyaNQuSJEGSJPTt2xeurq5qdZHIOnl5cgKJu3eB1FRg7145eHn8caC0tXqGo1DduwONGxvf108HBICTJwuPhQBOnSo8z80FLl40rjt+vBxIVa0KfPqpNe+IiIiIiFSg6ma7c+bMgaOjIyRJgk6nw+TJk9G+fXv88ssvyM3NNVsnNzcXa9euRfv27fHWW28pQZijoyPmzJmjZveILNeoETB6NDBrlhy0GAY9u3cD06cXX3fzZuPAyNx0v759jdvr1w+YPVsOuM6cMS6bklJ4/L//Fa6l+vJLeYSMiIiIiO4pSQgh1Gxw6dKlePXVVyFJEoQQkP7NSKbRaPDoo4+icuXK8PDwQFZWFuLi4nD+/HlotVoAMCr/+eefY8yYMWp2jSBnO4yMjMTChQvh5uZW3t2pmGJigPBw42u3bgFNmsgjQAAQGAjExxtn3NPr0QPYulU+btQIOH68aJmEBCAiAvh3g+oSHT4sJ51ISADq1weSkoCXXipcV7VypXyud/kyEBpaertEREREVCaqJZbQGzduHHJzczFlyhTc/XftiBACd+/exZkzZ3D27FmlrGH8pg+eHB0d8dFHHzGAovJjGkAB8tS5gQOBZcvk88REOZgJDDQud+JEYQAFAG++af4ZQUFAVJScOOLXX+W9nfz9gXbt5DVSM2YYPxsApk6Vn1mtmpzdj4iIiIjKharT+fQmTpyIffv2ISIiwmygZO5cCIFOnTrhwIEDiIyMtEe3iGxjOmjrYOZfn08+KTwOCQGGDi2+PR8fYO5c4Nw5ef3VrVvyVD3DtOWhoUCVKvJxfLz8540b8vRC/ea6hqNQAPDII9x0l4iIiMiOVB+J0mvRogV27dqFEydO4Pfff8eBAwdw7tw5pKSkICMjA15eXvDz80PdunXRrl079OvXDw0Ns5QRlYcvv5RHfp580niq3q1bwLp1hedVqgCVKhnXvXULWLOm8HzCBDmznzk6HZCebrzWCgB27gS++KLwfOTIsrwLIiIiIrIjuwVReo0aNUKjRo3s/RgidZw+LSeUqFVLXttUvTpw8yawerVxgodx44quh1q0SN4fCgC8vICSpqRmZwOVK8vPqFcPcHGRpwL+8YccYAFAWJi84a5eu3aAub3Xrl6V103p9eoFuLsDHh5WvXUiIiIisozdgyii+9KlS4Xrn0w9+yzw7rvG1zIzgeXLC89HjZKn65UkLw/480/5ZapOHeCvv+RgTO/tt823Y5pY4vPPmViCiIiIyI4YRBEZevddOQPehg3A2bPyOqTsbCAgAGjRQg5Wnn66aL2vv5b3kgLk0aLS1vW5ugLvvAPs2iVn07tzB/D0lEelBg2SR7GYPZGIiIioQlI9xTlVbExxTkRERERkG45E0YPn6lXgwAEgNlZeV9S2LVCzZnn3ioiIiIgeEBYHUbt37zY679ixY6ll1GDuOURmZWXJ+ystWAD8u4EzAECjASZNAqZPZ7IFIiIiIrKZxUFU586dlX2dJElSNtItrowainsOURFCAM8/L29ca0qrBebNAy5elPdhUvGfUSIiIiJ6+Fi92a4QAqUto9KXKevL0ucQKZYtMx9AGVq3rviMe0REREREFrIqiLIkqFEj8GHwRFbRT+OzxIwZcrY9IiIiIqIysng6344dO1QpQ6S6ZcuAhATLyiYkyOUnTbJvn4iIiIjogcUU5w+ZBy7FeVYWUKuW5UEUAAQFyXszubvbr19ERERE9MCyek0UUYVizSiUnn40ioiIiIioDBhE0f0rKwv4+OOy1f3oI66NIiIiIqIyUXWzXf0+UZIkoUOHDmVuZ9++fdD+u88P94miYpVlFEqPa6OIiIiIqIxUXRPl4OAASZLg6OiIvLy8Mrfj5eWF7Oxs7hNlBw/MmiitFggJAeLjy95GcDBw86a8GS8RERERkYVUn86n1v5O3CeKSnT4sG0BFCDXP3JEnf4QERER0UODa6Lo/vTv1NEK0w4RERERPTQqZBCl0+kAyNMDiczy8alY7RARERHRQ6PCRSm5ubnIyckBAHh4eJRzb6jCqltXnXYefVSddoiIiIjooVHhgqidO3cCkDP8ValSpXw7QxVXRATQvLltbTRvLrdDRERERGQFq1Ocp6enIzU1tdRy169ftzgxhFarRUpKCqKiojBnzhzlepMmTaztHj0sNBo5RXn79kB+vvX1nZ3l+szMR0RERERWsjqIWrBgAWbNmlViGa1Wi9DQ0DJ1yDDwGjBgQJnaoIdEixZAVBTw3HPAyZOW12vYEFi1Sv6TiIiIiMhKZdpst7QRprKmJpckCZIkQQiBiIgIDBo0qEzt0EOkUSM53Xl0tBxQ3biBtDR5+6eUFMDPT95OyscHQLVqQMuWQJMm8kgUEREREVEZlCmIshchBHx8fPDSSy9hzpw5kCSpvLtE9wNnZ6BVK2TVb4UZM4AFX8h78eppNMCkScD0FwHmKiEiIiIiW1kdRPXv39/sVD0hBEaOHAkA0Gg0WLFihcVtOjk5wcvLC6GhoahXrx40XKdCVhICeP554Ndfi97TaoF584CLF4G1awHG5kRERERkC0mUde6dGQ4ODpAkCRqNBvllWexPdpeTk4PIyEgsXLgQbm5u5d0d1SxdCowfX3q5zz8Hxo2zf3+IiIiI6MGleopzFWMyIotkZQEzZlhWdsYMIDvbnr0hIiIiogedqkGUTqeDTqfjKBTdU8uWAQkJlpVNSJDLExERERGVVYXbbJfIGllZwMcfW1fno48sG406dgwYORKoXRtwcwO8vYGwMGDIEGDLFuOyWi2wfLm8d6+fn1y+Th3gjTeA27fVe86MGfKarpJeo0ZZ9XEQERERkZUqVHY+ImtZMwqlpx+NmjSp+DIzZ8ovw9mpublARoacoMLTE+jRo/B6v35FA57YWGDRIuCHH4DNm+VtrWx5DhERERFVDAyi6L5VllEovY8+AsaOBdzdi95butR4jVXbtkC7dkClSsCdO8DZs0BAQOH9998vDKA0GnlUqUoVYOVK4No1uc4zzwCnThmnWLf2OaYGDzYfmDVqZMEHQERERERlZvcgKiUlBYcOHcKNGzeQkpKC3Nxcq5JPTJs2zY69o/tZWUah9IobjUpPB6ZMMX7GmDHFt5OSAnz2WeH5O+8AH3wgHw8bBjz2mDzKdOUK8P33cuBWlueY07Mn8OKL1tUhIiIiItvZLYj6+eefsXDhQhw8eNCmdhhEkTn6vZ9s8fHH8polw23JfvlFDnAAoHp1IDMTaNxYnprn5iaveXr3XaB1a7nMli3y9Du9gQMLj+vWBRo0AE6elM//+KMwiLL2Oea8/z7w2mvA3btA1apAly7Am28C9erZ9rkQERERUclUTyyRmZmJAQMGYMiQITh48KAy6iSEUF6mDO8ZlicqzuHDQHy8bW3ExwNHjhhf27+/8Pj6dWDyZODECTkRRXIy8PvvcoDz009ymRMnjOvXqlX8uWFZa59jzs2b8pTGvDzg8mXg66+BZs2AdetKf+9EREREVHaqBlFCCAwdOhS//fZbkYBIkiTlZS5o0t/Tlycqye7d9mnHNJOei4s82vPWW4CPj3zt7l05A15ysvwy5O1tfO7lVXiclFT25xjy9ASefhp4+21g6lSgc+fCe3l5wIgRZZ/mSERERESlUzWI+v7777FhwwYlIHrqqacQFRWFnJwcODg4KMGRTqdDRkYGYmNjsWbNGgwYMEC57+npia+++go6nQ5arVbN7tEDRB9oqN2O6RZn8+YBixfLU/++/77wekaGPD3PNN4v6fzf3wjK9By9l16SA6R16+TkGLNmATt2AO+9V1gmMxP4+Wfz75eIiIiIbKdqEPXf//5XOe7Tpw9+//13NG/eHC4uLkXKenh4oFatWnj22Wexdu1aHD16FA0aNEBmZiZGjRqFj8uado0eCnXrqtPOo48an/v6Gp8bjvJ06mR87+JFwN/f+FpGhvG5ft0TIGfdK+tz9GrWlNdMmZowwfj8zJmiZYiIiIhIHaoFUTdv3sSpU6cAyFPzPv30U2V6niUaNmyI3bt3o379+hBC4L333sNuteZs0QMnIgJo3ty2Npo3l9sx1KBB8eVN/3F2dS2aTtww4DE9Nyxr7XOs5cBttImIiIjsRrWvWocOHQIgB1CtWrVCzZo1rW7D19cX33zzDQB5XdTs2bPV6h49YDQaOS24s3PZ6js7y/UNM/MBwFNPGZ/v2mX+GJD3aOrRQ17PpLd2beHx6dPGI0J9+5b9OYA8yvXmm+YTaixaZHxeUpBGRERERLZRLcV5vME3uyZNmhS5bzgqlZeXZ3aKHwC0aNECLVu2RFRUFHbu3Im4uDhUrlxZrW7SA6RFCyAqCnjuucI04pZo2BBYtUr+01SzZkCvXsDGjfL5W28BFy7Io0FffllYLjwc6N5dDsLGjQMWLpSvz5snb5RbubKcLU+vRg3g+efL/hxATus+fz6wZAnw+OOFwdWePcDOnYV1/P2BZ5+1/PMgIiIiIuuoFkSlpqYqx4GBgUXuu7q6IjMzEwCQlZVVbBAFAG3atEFUVBR0Oh0OHTqEfv36qdVNesA0aiSnO4+OlgOqGzeKL1utGtCyJdCkSckjWN98Iwcpp0/Le0CZjvJUriyPOOlHsf7v/+Qgbvt2OdD54gvj8r6+cqIHT0/bnqOXny8HX/oAzPRZ69YBfn7Fvz8iIiIiso1qQZSzwbdSc2uhvL29lSDq5s2bqGS4yt6En8E3wNumuaCJTDg7A61ayS81BAcDhw7Jo0s//yxvgKvVAqGhQJ8+8p5OQUGF5d3cgE2bgBUrgO++k4OivDw5aOvVC5gyBQgJsf05Pj7yiNOff8qp2W/dAhITAScnICwM6NlTTjBRtao6nwMRERERmadaEGUYFGWYpigDUKVKFdy6dQsAcPr0aTQ0N5fqX0kGG+oYjnAR3SseHsB//iO/LOHoCIwdK7/s9RxJkjP3mWbvIyIiIqJ7S7XEEo8a5Iq+cuVKkfuNDFKTbd68ucS2tm3bphz7muaCJiIiIiIiKkeqBVENGzaEw795lc+Y2aSmS5cuAOSse//73/9w7tw5s+0sXboU58+fV84bmeaQJiIiIiIiKkeqBVHe3t5o2rQphBA4f/68UbY+AHj66afh5eUFSZKQm5uLrl274ocffkBSUhK0Wi0uXryIKVOmYMKECcqaqsqVK6N169ZqdZGIiIiIiMhmqm7J2bt3b+X4r7/+Mrrn4eGBqVOnQggBSZJw+/ZtjBgxAsHBwXB2dsajjz6KefPmQavVKmWmTp0KjWlqMiIiIiIionKkahA1ZMgQeHt7w9vbGz///HOR+xMnTsSgQYOUIEkIYfQCCjP7vfDCCxhr7Sp9IiIiIiIiO1MtOx8AhIeHl5hNT6PRYPXq1WjRogU+/PBDpKWlGd0XQiAgIADvv/8+JkyYoGbXiIiIiIiIVKFqEGUJjUaDt99+GxMnTsSePXtw7tw5pKamwsvLC/Xr10f79u2N9pwiIiIiIiKqSO55EKXn5OSErl27omvXruXVBSIiIiIiIqupuiaKiIiIiIjoQccgioiIiIiIyAoMooiIiIiIiKzAIIqIiIiIiMgKFieWmDVrlj37Uaxp06aVy3OJiIiIiIjMkYR+l9tSODg4KBvh3ktarfaeP/NBlpOTg8jISCxcuBBubm7l3R0iIiIiovtOhZ3OZ2FsR0REREREdE9ZtU8UAxsiIiIiInrYWRxE6XQ6e/aDiIiIiIjovlBhp/MRERERERFVRAyiiIiIiIiIrMAgioiIiIiIyApWJZZ4EGVlZSE2NhapqanIycmBj48PAgMDUatWLTg4lF+MmZGRgUuXLiEpKQm5ublwdHSEl5cXgoKCUK1aNbi6upZb34iIiIiIHmb3LIi6desWkpOTkZaWBp1Oh44dO96rR5sVHx+PdevW4dSpU7h7926R+z4+PujQoQN69eoFR8d7F2seP34cW7duRWxsbLHZECVJQs2aNdGlSxe0adPmnvWNiIiIiIjsHETt3LkTS5cuxc6dO5GUlKRclyTJbOBy+vRpbN++HQDg4uKCMWPG2KVfBw8exI8//oi8vLxiy6SlpWH9+vU4fvw4xo4di4CAALv0RS8zMxMrV67EyZMnSy0rhMCVK1dw8uRJBlFERERERPeYXYKo+Ph4DB8+HDt27ABg+f5SgYGBmDJlihLcNGvWDC1btlS1bydPnsTKlSuN+hQUFITw8HC4u7sjMTERJ06cQEFBAQDg+vXrWLJkCd555x24ubmp2he9lJQULFiwAPHx8UbXq1atikceeQTe3t7Q6XRIS0vD1atXcfv2bbv0g4iIiIiISqd6EHXp0iV06NABcXFxRYInSZJKDKiCgoIwfPhwfPXVV5AkCatWrVI1iEpLS8OKFSuUPkiShIEDB+Lxxx83Wv+UkZGBL774AufPnwcA3L59G6tWrcKoUaNU64teQUEBlixZYhRAhYWFYdiwYQgJCTFbJykpCQcOHEB2drbq/SEiIiIiopKpmjkhJycHvXv3NhopadOmDebPn48//vgDkiSV2sawYcOU402bNqnZPWzYsAG5ubnKeZ8+fdC9e/ciCSS8vLwwYcIEVKlSRbl2+PBhXLt2TdX+6Pt048YN5bx169aYPHlysQEUAAQEBKBPnz4YPHiw6v0hIiIiIqKSqRpEffrppzh37hwkSYJGo8Hy5cuxf/9+REZG4qmnnrIoiOrUqRO8vb0hhMCFCxeQkJCgSt/S09Oxb98+5TwwMBA9e/YstryTkxOGDBminAsh8Ndff6nSF71bt25hy5YtynmNGjUwYsQIiz4nIiIiIiIqH6oGUQsXLlSOZ82ahVdeecXqNhwcHNC0aVPl/PTp02p0DdHR0UbJLDp06ACNRlNinfDwcFSuXFk5P3XqVInJKKy1bds2aLVa5Xzw4MGl9omIiIiIiMqXakHUsWPHkJCQAEmSEBQUhDfffLPMbYWHhyvHly5dUqN7OHHihNF5s2bNLKpnWK6goABnzpxRpT+5ubk4fPiwcl69enWEhYWp0jYREREREdmPakGUYWrubt26wcnJqcxt+fn5KcdpaWk29UsvNjZWOfb29kZgYKBF9WrXrm10fuHCBVX6c+LECaNRLbWzEBIRERERkX2oFkQlJiYqx6GhoTa15ezsrByrMX0uNTUVOTk5ynn16tUtrmtaVq304pcvXzY65ygUEREREdH9QbUU55buBWWJ5ORk5djX19fm9uLi4ozOK1WqZHFdb29vODo6KuupTPdyKqurV68qxw4ODkqwlpqaioMHD+L48eNITk5GTk4OPD09ERAQgPDwcLRu3druG/8SEREREVHxVAuiDKfH2TpaEx0drRyrETCkpqYanRtOFyyNJEnw9fVFUlISAHljXDUYfkY+Pj5wdnbGrl278MsvvxQZfbtz5w7u3LmD8+fPY8OGDejYsSMGDhxo05RJIiIiIiIqG9Wm8xmuHTpw4ECZ20lISMA///yjnDdp0sSWbgEoOiXQxcXFqvqurq7KsU6nQ0FBgU390el0RtMLfXx88Pvvv+PHH38sdfqiVqvFjh07sGDBAqM9r4iIiIiI6N5QbSSqTZs28PLyQkZGBmJiYnDw4EG0adPG6nY+/PBDJUgJCQlBnTp1bO6babBh7QiOo6Pxx5SXl2fTKFBOTo7R9Mf4+HhcuXIFgDy1r2PHjmjbti0qV64MSZIQFxeHAwcOYNeuXdDpdACAixcv4rvvvsPo0aPL3A8iIiIiIrKeaiNRjo6O6N+/v3L+6quvWj1S8tNPP2HRokWQJAmSJGHEiBGq9M1wfyigaFBUGtPy+fn5NvXHdLRJPyrl5OSE119/HUOHDkVoaChcXV3h4uKCmjVrYsiQIXjjjTeMgrcjR47g+PHjNvWFiIiIiIiso+pmuzNnzlQy60VHR6Nnz564fv16qfUyMjLw3nvvYdiwYQDkJBUeHh6YOHGiKv0yDYJMg6rSmJY3zB5YFsWNYvXr1w/16tUrtl54eDiefvppo2tbtmyxqS9ERERERGQd1abzAXJq87lz52LSpEmQJAl79uxBeHg4+vfvj4iICKMpbDt27EBsbCx27dqFv/76C2lpacp9SZKwbNkyq7LolcRwTRMAq9c0mQZR1q6pMmWuvru7Ozp37lxq3U6dOmHjxo3IyMgAIE/ry8rKgoeHh019IiIiIiIiy6gaRAFAZGQkbt68iU8++QSSJCEnJwdr1qzBmjVrlDJCCHTr1s3oXD+FTwiBqVOnKqNSajANWqzde8pwWqKDg4PNWfGcnZ3h4OCgrG8CgMcee8yidh0dHVGvXj0cOnQIgPzZXbx4EY0aNSqxXkFBAe7evau8F/2fjo6OzPJHRERERGQF1YMoAJg3bx6aNm2K8ePHIz09XQmSACh/Go466YMnLy8vfPbZZ3juuedU7Y/pXlPWpCkXQhilSLcmPXpJ/Pz8jPbDCgkJsbiuaVnTFO7mbNq0CevXr1fOp0yZAgB46qmn0KdPH4ufTURERET0sLNLEAUAw4YNw1NPPYXPP/8c33//Pc6ePWu2nBACjzzyCIYOHYpJkyapNoXPUJUqVYzODYOX0qSnpxtN56tcubJqfTLsh7u7u8V1TctmZWWVWqdnz57o1q0bcnNzMWXKFMydOxeurq5WJ9kgIiIiInrY2fUbtLe3N6ZMmYIpU6YgKSkJx44dQ1JSElJTU+Hu7o6AgADUq1cPjzzyiD27AR8fH7i5uSlZ8CxJdqF37do1o3M1g6hTp04p59YkuzAta8l0PCcnJ6Nyrq6ucHNzs/iZREREREQkUy2IysjIwOXLl5Xz+vXrQ6PRKOcBAQHo3r27Wo+zWlhYGE6ePAlA7mtiYiICAwNLrXfx4kWjczX2rQKARx99FFu3blXOrZliaFrW09NTlT4REREREVHpVEtx/uOPP6Jp06Zo2rQp+vfvbxRAVQSNGzc2Oj9y5IhF9Y4ePaocOzk5lZiC3BqPPfaYUcIL02CtJKZla9SooUqfiIiIiIiodKoFUcnJyUqyCMPMexVF48aNjdb/7N27F1qttsQ6MTExiI+PV84bNGhgc3pzPScnJzRp0kQ5v3LlCm7evFlqvbi4OKMgytfXF1WrVlWlT0REREREVDrVgih/f3/l2DSRQ0Xg7e2NiIgI5TwxMRGbNm0qtnxBQYFRWnZJkvDkk08WWz4pKQljxoxRXu+9916pferduzccHAr/Cn788ccSAzutVotVq1YZ7bfVpUuXUp9DRERERETqUS2IMgyc9BvBVjS9evUy2nj3zz//xNatW432awLk/i9atAi3b99WrrVo0UL1aXPBwcHo2LGjch4bG4vPPvsMaWlpRcqmpaVh6dKlOH/+vHLN39/fog16iYiIiIhIPaollmjfvj0cHR2h1Wpx/PhxtZpVla+vL0aNGoXPPvsMQggIIbB27Vrs3r0b4eHh8PDwQEJCAk6cOIGCggKlXpUqVTB8+HC79GnQoEG4ceMGYmNjAQCnT5/Gf/7zH9SrV08JTOPi4nDmzBnk5+cr9ZydnTF27FijoJCIiIiIiOxPtSDK398f3bt3x8aNG7F3715cvnzZ7qnLy6Jhw4Z48cUXsWrVKiUoSUhIQEJCgtny1atXx9ixY+2WDtzJyQnjx4/Hl19+qeylVVBQgOPHjxcbjPr4+GD8+PFMKEFEREREVA5U3Sdq3rx52LlzJ3Jzc/HKK69g48aNFu1hdK+1adMGoaGh+PXXX3Hy5Emz65B8fHwQERGBJ5980u4b0np4eOCNN97Anj178PfffxtNIzQt16FDB/To0QMeHh527RMREREREZknCcMsBSr44Ycf8PLLL+Pu3buIiIjAihUrVNtbyR4yMzNx8eJFpKSkIDc3F97e3ggICEBYWJhR0od76caNG7h9+zZSU1Oh0+ng6emJqlWrombNmjb3KScnB5GRkVi4cCE32yUiIiIiKgNVg6jdu3cDkNOHT58+HTqdDpIkoUuXLujcuTPCw8Ph6+tr1eiUYeIFsh2DKCIiIiIi26g6T61z586QJEk51ydv+Pvvv/H3339b3Z4kSbh7966aXSQiIiIiIrKJXRb7CCEgSZJRQKW/bglJkiwuS0REREREdC+pHkTpgx9bgiAGUEREREREVFGpGkTt2LFDzeaIiIiIiIgqHFWDqE6dOqnZHBERERERUYVTPjm8iYiIiIiI7lOqBVHr169Hs2bN0KxZM0RERDCrHhERERERPZBUC6JOnz6N6OhoHD9+HJUrV4ajo10S/xEREREREZUr1YIoFxcX5TgsLEytZomIiIiIiCoU1YKoKlWqFDbqwKVWRERERET0YFIt2nnssceU46tXr6rVLBERERERUYWiWhDVqFEjhIeHQwiBrVu3oqCgQK2miYiIiIiIKgxV5929+eabAIDk5GR8/PHHajZNRERERERUIagaRL388st4+umnIYTA9OnTsXTpUjWbJyIiIiIiKneqZ4BYs2YNxo0bB51Oh9deew0dOnTAqlWrcPPmTbUfRUREREREdM+puplTrVq1lGONRgOtVov9+/dj//79AAB3d3f4+vrCycnJovYkScLFixfV7CIREREREZFNVA2irly5AkmSlHP9sRACAJCVlYWsrKxS25EkCUIIo7aIiIiIiIgqAlWDKKAwYCrvNoiIiIiIiOxB1SBqxIgRajZHRERERERU4agaRH3zzTdqNkdERERERFThqJ6dj4iIiIiI6EHGIIqIiIiIiMgKDKKIiIiIiIiswCCKiIiIiIjICqqnODel1Wrxzz//YP/+/YiJiUFKSgoyMjLg5eUFPz8/hIeHo127dmjVqhU0Go29u0NERERERGQTuwVR2dnZWLBgAZYuXYrbt2+XWr5q1ap49dVXMWHCBLi7u9urW0RERERERDaxy3S+I0eOoGnTppg2bRpu3bpV6ua5QgjcvHkT//nPf9CsWTMcPXrUHt0iIiIiIiKymepB1NGjR9G1a1fExsZCCAFJkgDIgZL+3N3dHZIkKdcAKOfnz59Hly5dcOzYMbW7RkREREREZDNVg6iMjAw89dRTyMjIUK65u7vj5ZdfxoYNG3D79m3cvXsXGRkZuHv3Lm7fvo0NGzZg1KhR8PDwACAHU/p2MjMz1eweERERERGRzVQNoubOnYu4uDhlVKlHjx6IiYnBl19+iV69eiE4ONiofHBwMHr16oUvvvgCMTEx6NmzpzIyFRcXh7lz56rZPSIiIiIiIpupFkQJIfDll18q0/d69+6N9evXIyQkxKL6VatWxZ9//onevXsbtUdERERERFSRqBZEHT58GElJSRBCwMXFBV999RUcHa1L/qfRaLBixQq4uroCAJKSkhAVFaVWF4mIiIiIiGymWhB19uxZAPKapieeeAJBQUFlaic4OBhPPPFEkXaJiIiIiIgqAtWCqISEBOU4PDzcprYM6ycmJtrUFhEREZE9rFy5EpIkITQ01Kp7VLoXX3wRkiThxRdfvOfP5t8rWUK1zXb1a6EAlLovlDXUbIuIiIiIbPPbb78hOjoaTZo0Qf/+/cu7OxXGzp07sXPnToSGhpZL8Ef3lmojUYGBgcrxuXPnbGorJiZGOS7rtEAiIiIiUt9vv/2GmTNn4rfffivvrtxzPj4+qFu3LmrXrl3k3s6dOzFz5kysXLny3neM7jnVgij9FDwhBLZs2YKkpKQytZOYmIgtW7YUaZeIiIgefPn5wMWL8p9EFc3TTz+NmJgYbN++vby7QuVMtSCqZcuW8Pf3hyRJyM3NxZgxY6yeiqfT6TBmzBjk5OQAACpVqoRWrVqp1UUiIiKqoA4fBjp1Ajw9gbAw+c9OneTrREQVjWpBlCRJGDlypBI4/fbbbxg4cKDFiSESExMxaNAg/P7770p7L7/8slrdIyIiogrqiy+AVq2A3buBggL5WkGBfN6qlXzfXtLS0rBmzRoMHz4cDRs2RKVKleDq6oqaNWti2LBhOHjwoP0eXowZM2ZAkiR07twZAPDHH3/g8ccfh7+/P7y9vdGuXbsiU+m+//57tG/fHn5+fvD09ETHjh0tGi3ZuXMnhg4diho1asDV1RU+Pj5o1aoVPv74Y2RlZRUpK0kSvv32WwDAt99+C0mSjF47d+5UyickJODrr7/GgAED8Nhjj8HHxwdubm4ICwvDqFGjcPr0aYs+DyEEli1bhlatWsHHxwfe3t6IiIjAqlWrLHp/zzzzDEJCQuDi4oKAgAA8/vjj+Oabb6DVai16viFziSWuXLkCSZIwc+ZMAMCuXbuKfC7mpvjFxcVhypQpaNy4MXx8fODq6opatWph1KhROHPmTLF9uHHjBiZOnIj69evDw8MDLi4uqFq1Kpo3b46JEydye6B7RagoNTVVBAQECAcHByFJknBwcBC+vr7itddeE1u2bBGJiYlG5RMTE8WWLVvEq6++Knx9fYWDg4NSNygoSKSmpqrZPRJCZGdni9GjR4vs7Ozy7goREZGIihLCyUkIoPiXs7Nczh6mT58uACgvT09P4eLiopxLkiQ+/fRTs3W/+eYbAUDUrFnTqnuW9qlTp05i2rRpAoBwcHAQPj4+Rn1dunSp0Ol0YsSIEQKAcHR0FF5eXsp9jUYj1q9fb/YZBQUFYtSoUUXeu0ajUc7r1q0rrly5otTZt2+fCA4OFq6urgKAcHV1FcHBwUavffv2KeX1/dK/vL29haOjo3Lu4uIi1q5da7Z/+rojRowQgwcPVj4DPz8/IUmS0sZLL70kdDqd2TYmTpxo9Pfo6+tr9P66du0q0tPTi9Sz9u/12rVrIjg4WHh4eAgAwsnJqcjnsmbNGqN2/vzzT+Hp6an0xcnJSakPQDg7O4tvv/22yPOjo6OFn5+f0d+x6WcyYsQIs58HqUvVIEoIIfbv3y88PDyMAiL9sYODg3B0dFT+JTK8ri8nSZLw8PAQBw8eVLtrJBhEERFRxaHTCdG2bckBlP7Vtq1cXm1Lly4VEydOFAcPHhQpKSn/9ksnLl26JN544w0hSZLQaDTi6NGjReraO4jy8fERGo1GzJkzR/lh+caNG+KJJ54QAISXl5eYNm2acHV1FcuWLRNZWVlCCCHOnz8vWrRoIQCIGjVqCK1WW+QZb7zxhgAggoODxeeffy6Sk5OFEELk5+eLHTt2iKZNmwoAolmzZkXqGwY4JZkxY4Z4//33xbFjx0RmZqYQQgitVitOnTolhg8fLgAIDw8PcfPmzSJ19c/w8fERkiSJ2bNni7S0NCGEEAkJCeK1115TggZzQe7ixYuV+6NHjxa3b98WQgiRmZkpFixYoARzgwcPLlK3rH+vhsFvSQ4dOiScnZ0FADFmzBhx9uxZcffuXSGEEFevXhXjx49XguIok18PHn/8ceXv5cCBA0oAmZeXJ86fPy/++9//io8//rjE55M6VA+ihBBi7969ombNmkYBlCRJxb4My4SGhhr9ikHqYhBFREQVxebNlgVQ+tfmzfe+j6+++qoAIF5++eUi9+wdRAEQc+bMKXI/LS3NaNTihx9+KFImNjZWub9nzx6jeydPnhSSJAl3d3dx4sQJs31IT08X1apVEwDEr7/+anTP0iCqNL179xYAxOzZs4vcMxzFmjp1qtn6zz33nAAgKlWqJHJycpTr2dnZolKlSgKAGDp0qNm6ixYtUto3DVTsHUS1bNmyxPclhBATJkwQAES/fv2Mrru5uQkAYv/+/SU+g+xPtTVRhtq3b4+TJ09i6tSpCAoKKjXBhBACQUFBmD59Ok6cOIF27drZo1tERERUQQgBzJhhXZ0ZM+R691Lv3r0BAHv37r23Dwbg6uqKyMjIIte9vb3Rtm1bAECNGjUwbNiwImVq166NsLAwAMCJEyeM7n311VcQQqB3795o2LCh2Wd7eXkpe0Bt3rzZhndRPEs+Wzc3N0yePNnsvWnTpgEA7ty5g61btyrXt27dijt37gCQ15eZM378eFSpUgUAsHr1aqv7XlbHjx9HVFQUnJyc8OabbxZb7oUXXgAAbNu2zWjtlq+vLwDg9u3bdu0nlU61zXZNeXl5YebMmXj//fdx8OBBHDhwAOfOnUNKSgoyMjLg5eUFPz8/1K1bF+3atUPr1q3h5ORkr+4QERFRBbJ1K3DggHV1DhyQ6/XooW5fLl26hM8//xw7duzAxYsXkZGRAZ1OZ1Tmxo0b6j7UAvXq1YOHh4fZe8HBwQCAFi1aQJKkYsvExsYiJSXF6Lo+aNm4cSMqV65c7PMzMzMBAFevXrW673rHjx/H8uXLsXfvXly5cgWZmZlFflwv6bNt0aIFvL29zd6rU6cOqlWrhhs3buDw4cPo06cPAODwvykdq1evjkcffdRsXY1Gg65du2LVqlVK+XtB/9nrdDrUrVu32HL6wCkrKwvJycnKvqlPPfUUvvzyS4wYMQL79u1D37590bJlS7i7u9u/82TEbkGUnpOTEzp06IAOHTrY+1FERER0HyjLKJTejBlA9+5AMXGD1X799VcMHToUeXl5yjVvb2+4urpCkiTk5+cjJSWlSKa6e8HLy6vYe46OjhaXKdCnPPzXrVu3AMhBkj5QKkl2dnapZcxZsmQJ3njjDSUglSQJPj4+cHFxAQDk5OQgPT29xM82JCSkxGeEhITgxo0bSEhIUK7pj0urW61aNaPy94L+s9dqtYiPj7eojuHn//HHHyM2NhY7duzA/PnzMX/+fGg0GjRp0gS9e/fG6NGjS33fpA67TOcjIiIiKk5ZRqH09KNRakhOTsaLL76IvLw8dO3aFTt37kR2djbS0tIQHx+PuLg4/Pzzz+o8rALRj3LMnTsXQl4fX+LLMG25pc6ePYvIyEjodDo888wz+Oeff5Cbm4uUlBTExcUhLi4O8+fPB4ASl30UN8pmCUvr2vIMa+k/+/DwcIs+eyGEUTp1X19f/P3339izZw/efvtttG/fHo6Ojjhy5AhmzZqFOnXq3NPpiQ8zBlFERER0T332mW31P/9cnX789ddfSE9Ph5+fH/7880906tQJbm5uRmXi4uLUeVgFop/Cd/LkSbs9Y+3atdBqtXjsscewZs0atGzZEs7OzkZlLPlsS5tGefPmTQBQprsZHl+/ft2itgMDA0vth1r0n/2lS5dsGt2MiIjARx99hL179yI1NRW///47GjZsiJycHIwcOdLiUS4qOwZRREREdM/cvQvs2GFbGzt2yO3YSv8lu27dusWuKdm2bZvtD6pg2rdvDwDYsGGDRdP5TDk4yF8fSxpB0n+2jRs3VsqbsuSzPXz4MDIyMszei42NVQKhFi1aKNf1xzdu3MD58+fN1tVqtdjx7z+ILVu2LLUflrDkc9F/9vn5+fj1119Vea6rqyv69u2LdevWAQByc3PLJRHKw0bVIOro0aMYMGAABgwYgJdffrlMbYwcOVJp49SpU2p2j4iIiMrZjRtAMd+JLZaeLrdjKx8fHwDA+fPnkZubW+R+dHQ0fvzxR9sfVMG88sorkCQJqampeOutt0osW1BQUCTQ0id6SE1NLbae/rM9efKk2aBi48aNFk0TzMnJwSeffGL23pw5cwAAlSpVQvfu3ZXr3bt3h7+/P4Dis/MtX75cWZ80dOjQUvthCUs+lxYtWqBp06YAgP/85z9ITEwssU19lkEAuHv3bpGEJ4YMR1E1Go0lXSYbqBpELV68GL/99ht+//13JW2ktapWraq08Zmt4/1ERERUoYSEALZ+v3N0BP7NCWCTHj16wMHBAXfu3MHw4cOVqWH5+fn46aef0KNHjxITN9yvmjRpoqROX7ZsGZ555hlER0crwY5Wq8Xx48cxe/Zs1K5dG9HR0Ub1GzRoAADYs2cPYmJizD6jZ8+eAIDTp0/j1VdfVYKBrKwsLF++HIMGDVICnZL4+Phg9uzZ+PDDD5URqaSkJLzxxhv49ttvAQBTp06Fq6urUsfNzU0JnlavXo2xY8cq09uys7OxePFi5f0PHjwYzZs3L7UfltB/LqdPn8b+/fvNlpEkCcuWLYOLiwuuXbuG1q1bY+3atUbJI27evIkffvgB3bt3xzvvvKNcv3HjBurUqYM5c+bg2LFjuGswHHvixAk899xzAAAPDw907NhRlfdEJVBrwymdTif8/f2VzXMvXLhQpnZiY2OVTXiDg4PV6h79i5vtEhFReWvWzLpNdk1fzZur15d33nlH2XQVgPDx8RFOTk4CgHjkkUfEqlWrlHum7L3Zbkmbtlqy4W2nTp0EADF9+vQi9+7evSsiIyON3rurq6vw9/cXjo6ORtf37t1rVPfOnTsiMDBQuR8QECBq1qwpatasKQ4cOKCUGzJkiFE7vr6+QqPRCACiefPmYvHixcV+Robvb/DgwQKA0Gg0ws/PT0iSpLT5wgsvCK1Wa/b9T5w4USknSZLw8/Mzem9dunQR6enpReqV9e+1oKBA1K1bV2nfz89P+Vx+/vlno7JbtmwR/v7+SlmNRiP8/f2Fu7u70Wc2atQopc7ly5eN7mk0GlGpUiXh7OysXHN2di7yLLIP1UaiTp48iTt37kCSJNSpU0fZ4M1atWvXxmOPPQYASExMxJkzZ9TqIhEREVUAxczOumf1Dc2dOxffffcdWrVqBTc3NxQUFCAsLAzvvfcejh07hqpVq6r3sApEo9FgwYIFOHr0KEaPHo26detCo9EgLS0Nfn5+aN++PWbMmIHo6GhlHY+en58fdu/ejSFDhiAkJARpaWm4evUqrl69ajQtctWqVVi4cCEaNWoEFxcXaLVaNGzYEB9++CH27dsHT09Pi/q6evVqLF26FE2bNsXdu3fh4eGBtm3b4rvvvsO3335b7Jqr+fPn4++//8bAgQMRHByMzMxMeHl5oUuXLvj666+xdetWVUcaHR0dsX37dowaNQqhoaHIyspSPhfTKZHdu3dHbGwsPvzwQ0RERMDHxwepqalwcHBAvXr18PLLL+OPP/7A4sWLlTohISH4448/MHHiRLRp0wZVqlRBZmYmHB0dUa9ePbz66qs4deoUBg0apNp7ouJJQqiz9/fq1asxfPhwSJKEYcOG4fvvvy9zWy+88AJ++OEHSJKENWvW4JlnnlGjiwR5bnFkZCQWLlxYJAMRERHRvTJtGjB7tvX1pk4FZs1Svz9ERNZQbSTKME2lrZt8GdbXL/ojIiKiB8esWcC2bUCrVoCTU8llnZzkctu2MYAioorBUa2GcnJylGPDxX1lod/JGkC57BBORERE9vf448ChQ0BeHhAbC/y7D6kRjQYICwMMvhoQEZU71YIoPz8/5TgpKcmmtpKTk5XjBzErDhERERVycQHq1y/vXhARWU616XyGuz0fPnzYprYM69/LXaSJiIiIiIhKo1oQpd8dWgiBI0eO4PLly2Vq59KlS4iKilLOmzRpokb3iIiIiIiIVKFaEBUaGoratWtDkiTodDpMnjy5TO1MnjxZ2Y25WrVqCA8PV6uLRERERERENlMtiAKAsWPHKrtd//bbb5g4caISEJVGp9Nh4sSJ+O233wDIOzqPHj1aze4RERERERHZTNUgavz48ahSpQoAeVrfokWLEBERgU2bNpVY76+//kL79u2xaNEiSJIEAAgODkZkZKSa3SMiIiIiIrKZatn5AMDNzQ1r165F165dkZ+fDyEEDh48iN69e8PHxwetWrVC1apV4enpiczMTNy8eRNRUVFIS0sDAGUUy8XFBWvXroWHh4ea3SMiIiIiIrKZqkEUALRt2xY//fQTnn/+eaSnp0OSJAghkJqaiq1btxYprw+c9CNQ3t7e+OGHH9CuXTu1u0ZERERERGQzVafz6fXp0weHDh1Cly5digRJpvTXhRDo3r07jhw5gqeeesoe3SIiIiIiIrKZXYIoAKhbty62b9+OQ4cOITIyEo0bN4ajoyOEEMrLyckJTZs2xaRJk3D48GFs3rwZtWvXtleXiIiIiMhEaGgoJEnCypUr7/mzX3zxRUiShBdffNGqe/ernTt3QpKkYgcX6P6h+nQ+Uy1btkTLli2V84yMDGRkZMDb2xuenp72fjwRERHRfWvhwoVITU1F//79uXemgZUrV+LKlSvo3LkzOnfuXN7doYeQ3YMoU15eXvDy8rrXjyUiIiK67yxcuBBXr15FaGjoQxdEValSBXXr1lUyPxtauXIldu3aBQD3VRDl7u6OunXrlnc3SAX3PIgiIiIiIirNhx9+iA8//LC8u6GqVq1aISYmpry7QSqw25ooIiIiIiKiBxGDKCIiInpode7cGZIkYcaMGdBqtViwYAGaNm0KT09PBAUFoX///jh+/LhSPjs7G3PmzEGDBg3g4eEBf39/DB48GBcvXizxOVqtFitXrsQTTzyB4OBgODs7IzAwEE888QTWrFmjZDPWmzFjBiRJwtWrVwEAL730kpKQwFxignPnzmHevHno1q0bateuDTc3N3h7e6Np06Z4//33kZSUZNHnkZGRgXfffRd169aFm5sbAgIC0L9/fxw6dKjU9/f111+ja9euCAgIgIuLC0JCQvDMM89g586dFj3blLnEEitXroQkScpUvpkzZxb5XK5cuVKkrWPHjmHkyJGoXbs23N3d4enpicaNG5f62Rw6dAjDhw/HI488AldXV3h4eKBmzZro1KkTZs+ejRs3blj1nkpKLKF/b6GhoQCAI0eO4Nlnn0WVKlXg4uKCWrVqYdKkSUhJSbHqmWQngh4q2dnZYvTo0SI7O7u8u0JERFTuOnXqJACI9957T3Tr1k0AEM7OzsLDw0MAEACEp6eniIqKEklJSaJp06YCgHB1dRVubm5KmaCgIHH16lWzz4iLixOtW7dWygIQPj4+Rud9+/YVeXl5Sp158+aJ4OBg4eDgIAAIb29vERwcbPQyVLNmTaUtSZKEr6+vkCRJuRYSEiJiYmLM9k9fd/78+aJu3brKZ+Dt7a3Ud3BwEF999ZXZ+qmpqaJz585KWY1GU+T5kydPNlt3xIgRAoAYMWKERffWrFkjgoODhZOTkwAgPDw8inwu165dM2pn2rRpRn1xd3cXzs7OynmVKlXE0aNHizx/5cqVRvVcXFyMPhMA4ptvvjH7voqzY8cOpa6pb775RgAQNWvWFKtWrVLeo4+Pj/LPAQBRv359kZGRYdVzSX0Moh4yDKKIiIgK6YMoX19f4e/vL37++WeRn58vdDqd+Oeff0StWrUEANGuXTvx9NNPi9DQULF582ah1WqFVqsV27ZtE4GBgQKAGD58eJH28/LyRMuWLQUA0axZM7FhwwaRlZUlhBAiMzNTfPvttyIoKEgAEJGRkUXq6wOc0r6sDx48WCxevFjExsYqwVheXp7Ytm2baNWqlfJ8c/TP8PHxEX5+fuKnn34SBQUFQgghzpw5o3xGjo6O4siRI0XqDxw4UAm8Fi1apLy/27dvi5EjRypf/pcuXVqkrrVBlJ6+T9OnTy/xc1mwYIEAILy8vMSHH34obt++LYQQ4u7du+Lw4cOia9euAoCoVq2aUWCSlZUlvLy8BADx3HPPidjYWOVeZmamOHz4sHjrrbfEhg0bSny+KUuCKHd3d+Hi4iJGjRqlBIRZWVliyZIlSmA1depUq55L6mMQ9ZBhEEVERFRI/2UcgNizZ0+R+9u3b1fuu7m5iQsXLhQp89VXXyn38/Pzje4tWbJEGT1IT08324fDhw8LSZKEs7OziI+PN7pnaRBVkoyMDBEcHFzsezQcxdq2bVuR+9nZ2aJOnToCgHjyySeN7h06dEipu3z5crPP1wdZAQEBIicnx+iePYOoxMRE4e7uLiRJMvu+hBCioKBANG/eXAAQCxYsKPK+PDw8lIBSDZYEUcW9ZyGEmDRpkgAgwsLCVOsTlQ3XRBEREdFDLyIiAhEREUWud+rUCS4uLgCAQYMGISwsrEiZJ554AgCQk5ODCxcuGN1bsWIFAGD8+PHFbvHSvHlz1K9fH/n5+dixY4dN78McT09PdOrUCQCwd+/eYsu1b98ejz/+eJHrbm5ueOuttwAAmzZtQlpamnJvzZo1AIBq1aph1KhRZtudPXs2ACApKQlbt24t25sog1WrViE7OxstWrQw+74AwNHREUOHDgUAbN68Wbnu6+sLAMjPz0dycrLd+2rq/fffN3u9X79+AIDY2FhkZ2ffyy6RCaY4JyIioodeq1atzF7XaDQICAjAzZs30bJlS7NlgoODlWPDRf8ZGRk4ceIEAGDq1KmYNWtWsc+/c+cOACiJJMpi/fr1+P777xEVFYX4+HizX7JLSoTQtWvXUu/pdDocPXoUXbp0AQAcPnwYANClSxc4OJj/bf6xxx5DSEgIbt68icOHD6NPnz4Wvydb6APGU6dOoXLlysWWy8nJAWD82deuXRvh4eGIiYlB69atMW7cODzxxBNo2LAhNBqNXftdqVIls8E6AFStWlU5TklJgbu7u137QsVjEEVEREQPveJGiQB5tKKkMvr7AFBQUKAcx8XFQafTASgMkkpTltEFnU6H5557DqtXrzbqk5+fH5ydnQEAaWlpyM3NRVZWVrHthISEWHQvISGhyHFJdQF5pOrmzZtGde3t1q1bAOQgSR8olcTws9doNFizZg2efvppXL58GVOmTMGUKVPg7u6Odu3aYcCAARgxYoRdghhL/lkEjP9Zo3uP0/mIiIiI7ECr1SrHBw8ehJDXopf4mjFjhtXP+eqrr7B69WpoNBpMmzYNFy5cQF5eHu7cuYO4uDjExcVh0KBBAFAklbohc2m3LblnyX1ry6lB//mPHTvWos/eNDV648aNERMTg19++QWjR49GgwYNkJOTg23btmH8+PEIDw/HyZMn79n7oYqFQRQRERGRHRhO87Pnl239uqRRo0Zh5syZCAsLKzK1Li4urtR2SprqZ3gvKCioyPH169ctajswMLDUfqhFP4XPls/e2dkZAwYMwPLly3Hy5EkkJiZi2bJlqFSpEq5fv44RI0ao1V26zzCIIiIiIrIDPz8/1KtXD0BhoGMtfTBU0giSPoBp2rSp2fuZmZmlbpYLoMSkFvp7Dg4ORs9p0aKFcl8/ddFUTEwMbt68CQDFriuzliWfS/v27QHIo4C2rDUz5O/vjzFjxuCjjz4CIG/iWx6JJ6j8MYgiIiIispPRo0cDALZv315qIGVu3ZS3tzcAIDU1tdh6Pj4+AIDjx4+bvT979mxkZGSU2te9e/di586dRa7n5ubik08+ASBnItRnrgOAIUOGAABu3rypZCI0NW3aNABAQEAAunXrVmo/LGHJ5/L888/Dzc0NWq0Wr776qtH0SlM6nc6orby8vBKf7+bmphzbO9EEVUwMooiIiIjsZOzYsWjdujUA+Uv9+++/bzT1LTs7Gzt37sRrr72G2rVrF6nfoEEDAMDatWuNMv8Z6tmzJwDgyy+/xBdffIH8/HwA8hS+iRMn4uOPP4a/v3+pffXx8cHAgQOxdu1a3L17F4A8itS7d2/ExMRAo9EUyTDYqlUrDBw4EADw+uuvY8mSJUqChri4OLzyyiv4+eefAcjBnKura6n9sIT+c/nrr7+UUS5TlStXxty5cwEAGzZsQPfu3bFv3z4lmBJCICYmBvPnz0eDBg2wfv16pe6aNWvQvn17LF++HJcuXVKua7VabN68GVOmTAEAtG3b1iiopIdHuWbnE0Jg9+7dOH78OPLz8xEaGoqePXvC09OzPLtFREREpAoXFxesX78egwcPxt9//40PPvgAH3zwAby9veHg4IC0tDRlSpph5jW90aNH48cff8T+/fsRGBiIoKAgJeOePhHCm2++ibVr1yImJgZjxozBuHHj4O3trbQ9ZswY5Obm4ttvvy2xr9OnT8fy5cvxzDPPwMXFBa6ursqeUJIkYenSpcr0PUNfffUVkpKSsGvXLrz++uuYOHEivLy8kJqaqry3yZMnY+zYsWX+HE2NGDECn3zyCWJjY1GjRg0EBgYqAdrevXtRrVo1AMCECROQl5eHd999Fzt27EBERAScnZ3h5eWF9PR0owx3hkkvhBDYv38/9u/fD0D+e/T09ERKSooybbFq1ar4+uuvVXtPdH9RdSTq8uXL+Omnn/DTTz8ZRfPmnDhxAvXr10fXrl0xceJEvPPOOxg8eDAqV66MxYsXq9ktIiIionITEBCAbdu24ffff8egQYNQvXp15OXlIScnByEhIejVqxeWLFlSJDscAHTs2BEbNmxAt27d4OPjg/j4eFy9etVojY+vry/279+PyMhIhIaGQqPRwNHREZ07d8bq1auxbNkyi/rp5+eHf/75B1OmTEGNGjWQl5eHSpUqoU+fPti3bx9eeeUVs/V8fHywfft2fPXVV+jcuTO8vLyQmZmJypUrY+DAgdixYwfmzZtXps+uOHXq1MGOHTvQt29fBAYGIjk5Wflc9KNoem+99RZiYmIwceJENGrUCK6urkhNTYWnpydatmyJt99+G/v378ewYcOUOn379sV3332Hl156CY0bN4aPjw/S0tLg5eWFVq1aYfbs2Th9+jTCw8NVfV90/5BESSvyrDRy5EjlV45XXnml2H9pr127hiZNmii/kOgjf31XJEnCzJkzi92tmcouJycHkZGRWLhwodF8XiIiIiIisoyqI1EbNmxQAqGRI0cWW27SpEnK4j1JkpT8/IbnM2fORHR0tJrdIyIiooouPx+4eFH+k4ioglItiLp8+TISExMhSRL8/f3RqlWrYsv9+uuvyuhTgwYN8Oeff+LMmTNYunQpPD09IUkSdDodPvjgA7W6R0RERBXZ4cNAp06ApycQFib/2amTfJ2IqIJRLbFETEyMctysWbNiy61Zs0YZdQoMDMSePXuUNJXh4eEICgpSsrxs2LABWVlZ8PDwUKubREREVNF88QUwdixguMKgoADYvRto1QpYtgz4N1U4EVFFoNpI1LVr15TjunXrFltu69atAORpeyNHjlQCKL2nn34aYWFhAOQc/ZzSR0RE9AA7fBh47TXjAMqQEMDrr3NEiogqFNWCqPT0dOVYv+mbqYKCAhw8eFA5HzBggNlyERERyrHhCBcRERE9QIQAJkyQR51Kkp8vl1MvFxYRkU1UC6LyDRaAFrdz89GjR5GbmwtA3mna3F4DABASEqIcF7exHBEREd3ntm4FDhywrOyBA3J5IqIKQLUgynDdUnGBz969ewHIU/natm1rtKmZIcPN5vKZnYeIiOjBIwQwY4Z1dWbM4GgUEVUIqgVRwcHByvGZM2fMltm8ebNy3LZt22Lb0qc/BwB3d3fbO0dEREQVizWjUHocjSKiCkK1IKpRo0YA5A1z9+7di6SkJKP7t27dwo4dO5TzTp06FduW4Y7dhsEZERERPQDKMgql9xCPRu3cuROSJBU7k8cWK1euhCRJCA0NVb1togeRakFU/fr1UaNGDUiShLy8PIwcORLZ2dkAgNzcXIwePRparRYAEBAQgPbt2xfb1tGjR5Xj2rVrq9VFIiIiqgjKMgqld49Ho1auXIkZM2Zg586d9+yZVLGlpqZixowZmDFjhtHsKXq4qLZPFACMGTMG//nPfyBJEjZs2IBq1aohPDwc586dU/4h06c2Ly75xIULF3D9+nW5c46OaNiwoZpdJCIiovL22We21f/8c6BHD3X6UoqVK1di165dAIDOnTvfk2cWx93dvcRtZGzh4+ODunXrGiX3IvNSU1Mxc+ZMAMCLL74IX1/f8u0QlQvVRqIA4M0330SjRo2UzXRTU1Nx6NAho0QTVatWxTvvvFNsG2vXrgUgB1uNGzeGm5ubml0kIiKi8nT3LmAwvb9MduyQ23nItGrVCjExMXbZ/uXpp59GTEwMtm/frnrbRA8iVYMoZ2dnbNmyBZ06dVICKcM/Q0NDsWHDhmIjdp1OhxUrVijnTzzxhJrdIyIiovJ24waQkWFbG+npcjtEROVE1SAKAIKCgrBjxw7s2rULU6dOxejRo/+/vfsOi+Jq/wb+3QWkN2kiFuxd7F1BxR57N3aNJe0xphkTo8YnyZOYRJOYGE3RWLFEY2IvwRZ7LwSVKCgWBKV3lvP+wbvz29nGDiyC+v1cl5fM7syZM8PuMPecc+6DmTNnIiwsDJGRkVICCmMuXLiAKlWqIDg4GJ06dcLQoUOtXT0iIiIqTQEBgIku/RaztQUqVbJOfUzQJlrQduWbP3++lNRB+083EZb2tYMHD+Lhw4eYOXMmateuDScnJ1kiiMzMTPzxxx946aWX0KRJE/j4+MDe3h4VK1bEgAEDsGvXLpN1MpdYQj8xxNmzZzFs2DD4+/vD3t4e1atXx8yZM01OQ2MuscS8efOgUqmk7owHDhxAnz594OPjAwcHB9SrVw/z58+X5gI1Zdu2bejatSs8PDzg4uKCoKAgfP7558jNzTXYh1IbN25Er1694OfnBzs7O3h4eKBWrVro168fvvvuO5N1S05Oxscff4zWrVvD09MT9vb2qFy5MkaOHIkTJ04YrB8SEoJq1apJy9WqVZN9Jkq7yyc9QYKeKxkZGWLKlCkiIyOjtKtCRETPq2bNhCjIsVe0f82bl3gVw8LChJ+fn7CzsxMAhLOzs/Dz85P9u337trQ+AAFA/Pjjj8LPz08AEA4ODsLV1VXo3m6tWLFCWheAcHR0FE5OTrLX3nzzTaN1Cg8Pl9bRpy23atWqYu3atVK93d3dhVqtlrZr0KCBSE1NNbu9vrlz5woAIjg4WHz++edCpVIJlUolPDw8hEqlksru3LmzyMvLM1r3N998U3aMHh4ewtbWVgAQnTp1ErNnz5b2odTEiRNlZbu4uBic01u3bhlsd+LECel3BUDY2NhIvy8AQqVSiU8++US2zcCBA4W3t7e0jre3t+wzMXDgQMX1p6cTg6jnDIMoIiIqdeHhxQuiDh58YlUNDg4WAMTcuXPNrqd7A1+nTh1x4MABodFohBBCXLt2TVpv69atYsqUKSI8PFwkJCRIr9+7d0/Mnz9fCn62bdtmsA9LgignJydhb28vJk+eLAV56enpYsmSJVLZc+bMMbm9uSDKw8NDqNVq8d5774n4+HghhBDJycniww8/lOr1888/G2y/fv166f1Ro0aJ2NhYIYQQmZmZYvny5cLBwUF4enoWKYg6cuSIACDUarX47LPPxKNHj6T3EhISxJ49e8S4cePE3bt3ZdvdunVLeHh4CABiyJAh4uzZsyI3N1cIIURcXJyYM2eOFORt3brVYFtzwRk9H577ICotLU1cuHBBHDx4UOzatUscO3ZM3LhxQ7rwPWsYRBERUZkwZ07RAigjAUBJUhpEubm5iTt37hR5fwsXLhQARNeuXQ3esySIAiDGjRtntOyZM2cKAKJmzZomtzcXRJk7D4MGDRIARGhoqOz1/Px8UatWLQFAdOvWTeTn55utu9Ig6rPPPhMARPfu3RVtN2TIEAFAjBkzxuQ6X331lQAggoKCZK8ziCIhhLBqivOnSVxcHLZs2YIrV64gz0iGH3d3d3Ts2BG9evWCrW3pnaZ9+/ZJGQu1ateujTfffLOUakRERGQFH30EBAcDs2cD588Dubmm17WzA5o2BT75BOja9cnVsQjGjBmDSsUYr9WnTx+8/fbbOH78ODQajckpYcz54IMPjL7ev39/fPXVV4iKikJGRgacnJwUlWtvb4+33nrLZNlbtmzBpUuXZK9fuHABN27cAADMnj3b6HiucePGYe7cubh9+7ai+gCQkpXFx8dbfL4eP36MLVu2AABmzZplcr2xY8di5syZuHjxIuLi4uDn56e4fvTsKtHoICUlBWvXrkV4eDguXLiAR48eITk5GQCMBi6PHj3C3bt3AQB2dnaoV69eidTrxIkTWLduHbKzs02uk5ycjO3bt+PixYuYNm0avL29S6Qu5iQkJODPP/984vslIiJ6Irp2BU6eBLKzgagoQKMxXMfGBqhZE7C3f/L1K4L27dsXuk5cXBy+//577N27F9evX0dycjI0eseekZGBxMRExfcf5cuXR82aNY2+V7FiRennxMRExUFUgwYN4OLiYrbsx48fy14/d+4cgIL7unbt2hndVqVSITg4GKtXr1ZUHwAIDQ2Fg4MDzp8/j44dO2LSpEno0qWLLPmDvuPHjyM/Px8A0KVLF4v2ExMTwyCKZEokiBJC4OOPP8YXX3yB1P+fxlT8/1TnAIw+hQCA+/fvo0mTJlCpVFCr1fj3339RpUoVq9bt8uXLWLlypaw+vr6+qFu3LpycnBAfH49Lly4h9/8/Ebtz5w6WLFmCd99994nPWbV27VqzgR4REdEzwd4eaNCgtGthFb6+vmbfP378OHr37o2kpCTpNRcXFymLn0ajQUJCAgAgPT1dcRDl6upq8j3dnjW55lr+ilG2/kPy+Ph4AICXlxfKlStncvuiTvJbvXp1/PTTT5g2bRqOHz+O48ePAwB8fHzQuXNnjBo1Cv369ZPde967d0/6OS4uzqL9ZGRkFKl+9OyyeorzzMxMdOvWDXPnzkVKSgpEwbgrAKaDJ62GDRuiZ8+eEEIgPz+/SE8kzElOTsZPP/0kq8+QIUMwf/58vPjiixg4cCCmTJmCTz/9FLVr15a2u3//PtauXWvVuhTmxIkTiIiIAFDQtZCIiIjKPnPdyfLy8jBy5EgkJSWhSZMm2LlzJ1JSUpCamoq4uDg8ePBAllZb94Hv08rSe8DiHOuLL76ImJgY/PDDDxg+fDgqV66M+Ph4bNy4EQMGDEBwcDBSUlKk9bWtfo6OjtJ9amH/mLqc9Fk9iBo1ahT++usvadnZ2Rn9+vWzeAzP+PHjpZ+3b99u1brt2LFDNk9A37590a1bN6jV8tPg6uqK119/Hf7+/tJrZ86cKVJf3aJIS0vDpk2bABRcdDhfFhER0dPv+PHjiImJgY2NDbZv345evXoZtO48ePCglGpXMrQtcwkJCcjJyTG5nm7rUFGUL18eU6dORVhYGG7fvo2oqCjMmjULKpUKR44cwbx586R1K1SoAKDgwX9UVFSx9kvPL6sGURs3bsS2bdukpw2jRo1CdHQ0fv/9dyxcuNAgWDHmhRdeQLly5SCEwNmzZ5GWlmaVuqWkpODvv/+Wln18fNCzZ0+T69vZ2WHEiBHSshACO3futEpdCrNhwwbpuDt27Gi2Xy8RERGVHO29izVahe7cuQOg4B7EVPe1/fv3F3s/ZUmzZs0AFHQfPHbsmNF1hBA4fPiwVfdbo0YNfPrppxg1ahSAgkRdWu3atZPuVcPCwhSXrXs/+yy0FlLRWDWIWrBggfTz8OHDsWbNGnh5eSkqw8nJCY0aNQJQ0Nx69epVq9TtwoULsn66HTt2LDSDS926daWnFQBw5cqVEh+jdPXqVZw6dQoA4ObmhoEDB5bo/oiIiMg0Nzc3AJCNYSoqbff8uLg4o2NxYmNj8c033xR7P2VJkyZNpEQX//vf/4wGHWvWrEFMTEyRyi/svkw7nl33ns/X1xf9+/cHACxcuBDXr183W4Z+sgztZwKwzueCnk5WC6JiYmKkgMfR0bFYFwFtEAVASotZXPopN7VPRgqju15ubq40TqkkZGdny8ZeDRs2THHmHCIiIrKehg0bAgB27twpZRAuqg4dOsDZ2RlCCAwbNky6eddoNNizZw9CQkIKHTv0tFGpVJg/fz4AYM+ePRg3bpzUdS8rKws///wzpk6dCk9PzyKV/+qrr2LYsGH47bff8PDhQ+n1tLQ0/PDDD1i1ahUAoHfv3rLtvvzyS3h5eSElJQUdOnTAL7/8ImWQBgq6H27ZsgWDBg3CyJEjZdt6eHhILYkrVqwwmnGann1WC6K0AyFVKhW6dOlSrJTguts+evSo2HUDIOvz6ubmBh8fH4u2q1GjhmzZWkGdMdu2bZOOt379+mjZsmWJ7YuIiIgKN27cODg4OCAqKgpVqlRBhQoVEBgYiMDAQMTGxioqy93dHV988QUA4PDhw6hTpw5cXV3h4uKCnj17Ijk5GStWrCiJwyhVo0aNwowZMwAAq1evRqVKlVC+fHm4ublh8uTJaNu2LaZNmwYAcHBwUFR2bm4uNm3ahCFDhsDPzw+urq7w9PSEq6srpk+fjpycHHTo0AHvv/++bLvq1atj3759CAwMRHx8PCZNmgRPT0+UL18erq6u8PHxweDBg7F161YpHboubX2//fZbuLi4oEqVKggMDJQNBaFnm9WCKN1m6fr16xerLGdnZ+nn9PT0YpUFFDS1ZmZmSsuVK1e2eFv9de/fv1/s+hgTHR2N8PBwAAXjsbR9eImIiKj01KpVC+Hh4ejXrx98fHzw6NEjxMTEICYmpkgtENOmTcOOHTsQEhICFxcX5OXlISAgAK+99houXrwo643zLFm0aBG2bNmCkJAQuLq6Ijs7G/Xq1cPChQuxZ88e6X5PO3mupebMmYNvvvkGAwcORN26dWFra4u0tDT4+vqiW7du+OWXX3Dw4EHZvaVW06ZNERERgSVLliA0NBTe3t5ITU1Ffn4+atWqhVGjRiEsLEyamFfX7Nmz8fXXX6NFixaws7NDbGwsYmJinrnEIGSa1eaJ0s2fr/Qpgj7dNJSmJnVTQv8DXb58eYu3dXNzg62trXShtHQ+ASU0Gg1Wr14tPeno3bu3xS1lREREVLLatGmDbdu2mV1HSYKB3r17G3Qvs6SskJAQk++NHz9eluHYmMDAwCJtP2/ePFl2O6V10xo4cKDJsd5nzpwBUDChrxI1atTAa6+9htdee03RdlqOjo545ZVX8MorryjaTq1W4/XXX8frr79epP3S089qLVHW7IKnO8BPaWIKY/QH/Snpd6tSqWRPRRITE4tdH3179uyRugT4+/uje/fuVt8HERERUVl06NAhKXOfuczJRGWJ1YIo3VSd58+fL3I52dnZOHLkiLRcp06dYtVLW6Yue3t7Rdvrtqzl5+cXaZZvU+Li4qTU6SqVCqNGjZLNKE5ERET0tHvllVewcuVKPHjwQGqxSkpKwrJly6RMeV26dOF4cHpqWO1uvUOHDlK3t5MnT+L27duoUqWK4nJ++uknqTufm5sbmjdvXuy66U6wCxSMOVJCP6jJzs5WXIYxQgisWbNGCsratWuH2rVrF7tcIiIiorLk77//xvfffw+g4GG2k5MTkpKSpICqfv36UiY9oqeB1VqiXF1d0alTJwAFrTXvvPOO4jL++ecfzJ49GyqVCiqVCv3797dKqk/9gZ9KW3r01zc347YSf//9t9R10cXFBYMGDbJKuURERERlyUcffYTx48ejfv36cHFxQWpqKjw9PdGxY0csWrQIp0+fNjkBMVFZZNV+Y3PnzsVff/0FANi0aROqVKmC//3vf7KZnU3Zu3cvxowZg7S0NAghoFarMWvWLKvUSz8IUppNR3/9cuXKFbtOycnJ+O2336TlIUOGWCWJBhEREVFZ069fP/Tr16+0q0FkNVZriQKAjh07YvTo0VLT7JdffolmzZrhxx9/xD///CNbV6PR4MaNG/jpp5/QpUsX9OrVC/Hx8RBCQKVSYcaMGahbt65V6qWfLVDpmCb9IErpmCpjwsLCpIyGtWvXRtu2bYtdJhERERERlTyrZzD46aefEB0djaNHj0KlUuHy5cvShGS6HBwcZJOXaYMnIQRCQ0Px+eefW61O+kGPfqKJwuiOqVKr1cUeD3Xx4kWcO3cOQEEr2Ysvvlis8iyRm5uLvLw86Vi0/9va2lplfBcRERER0fPC6kFUuXLlsG/fPrz66qv4+eefAUAKjrRjnYCCligt7WtCCEycOBFLly61qAugpfQnblOSplwIIUuRriQ9uimbNm2Sfu7RowcqVKhQ7DILs3v3bmzfvl1a1naVfOGFF9C3b98S3z8RERER0bOiRHJp29vb48cff8SLL76I//3vf9i7dy8A05PHCSHQrl07zJ07F926dbN6ffz9/WXLSuaxSklJkXXns0bAk5aWJv28a9cu7Nq1S9H2169fx/Tp06XlWrVqYebMmWa36dmzJ0JDQ5GVlYVZs2bhf//7HxwcHJhOnYiIiIhIoRK9gw4JCUFISAgePnyIw4cP4/z580hISEBSUhKcnJzg7e2N+vXrIzQ0FJUrVy6xeri7u8PR0RGZmZkAgDt37li87e3bt2XL1m410u3SWNTtLJkl3c7OTtZtz8HBAY6OjkXaNxERERHR8+yJNEP4+vpiyJAhGDJkyJPYnVE1a9bE5cuXAQCpqamIj4+Hj49Podv9+++/suVatWqVSP2IiIiIiOjp8Nz05QoKCpKCKAA4e/YsevbsWeh22gQQQEFrTv369Ytdl8WLFytaPyEhAe+//760XLt2bbz55pvFrgcRERERESln1RTnZVlQUJBs/M/Ro0dlyS2MiYyMRFxcnLTcsGFDq6Q3JyIiIiorAgMDoVKpsHLlyie+7/Hjx0OlUmH8+PGK3qPSs3LlSqhUKgQGBpZ2VUqVVVuili1bhq5du6JmzZrWLNYq3Nzc0KFDBxw8eBAAEB8fj927d6NPnz5G18/NzUVYWJi0rFKp0Lt3b5Pl67cWeXl54ZNPPrFO5YmIiOi5tHjxYiQlJWHAgAFo0qRJaVenzFi5ciWio6Ol8fdUuOjoaClQnjdvXqnW5Vlg1SBq+vTpUKlUqFy5MkJDQxEaGoquXbtaNPboSejVqxdOnDghzZH0559/oly5cujatasspXpqaiqWL1+O+/fvS6+1aNECVapUeeJ1JiIioufX4sWLERMTg8DAwOcuiPL390edOnUMsiwDBUHUoUOHAIBBlIWio6Mxf/58AMULotzd3VGnTh0EBARYqWZPpxIZE3X79m2sWLECK1asAFDQDU4bVAUHB8PJyakkdlsoDw8PTJ48Gd999x2EEBBCYPPmzTh8+DDq1q0LZ2dnPHz4EJcuXUJubq60nb+//xOZEJeIiIiICnz66af49NNPS7sapGfgwIEYOHBgaVej1Fk9iNKm29ZOriuEwJUrV3DlyhUsXrwYdnZ2aN26Nbp27YrQ0FC0bt0aNjY21q6GSY0aNcL48eOxdu1a5OTkAAAePnyIhw8fGl2/cuXKmDZtGtOBExERERERACsnloiPj0dYWBgmT56MatWqyQIq7f85OTk4evQo5s+fj44dO6J8+fLo168fvvnmG1y9etWa1TGpTZs2eP/999GkSROTAZy7uzv69OmDWbNmwdvb+4nUi4iIiJ6skJAQqFQqzJs3DxqNBosWLULTpk3h4uICX19fDBgwABcvXpTWz8jIwH//+180bNgQzs7O8PLywvDhww2mRNGn0WiwcuVK9OjRA35+fihXrhx8fHzQo0cPhIWFGcz5OG/ePKhUKsTExAAAJkyYAJVKJfun69q1a1i4cCFCQ0NRo0YNODo6ws3NDU2bNsUHH3yAhIQEi85Hamoq3nvvPdSpUweOjo7w9vbGgAEDcPLkyUKP75dffkGXLl3g7e0Ne3t7BAQEYOjQodJ4dKWMJZbQJjXQduWbP3++wXmJjo42KOv8+fOYOHEiatSoAScnJ7i4uCAoKKjQc3Py5Em8+OKLqFatGhwcHODs7IyqVasiODgYCxYsQGxsrKJj0k/KcPbsWQwbNgz+/v6wt7dH9erVMXPmTCQmJpot599//8X06dNRq1Yt6XfdrFkzfPTRR0hJSTFYPzAwEJ07d5aW9c+ZkuQd5hJLaD+32i6WBw4cQJ8+feDj4wMHBwfUq1cP8+fPl4bWPNVECfr333/FsmXLxNChQ4W3t7dQqVTSP7VaLftZ+8/f31+MHj1arFy5UsTGxpZk9YQQQqSmpooLFy6I8PBwsWvXLvH333+La9euCY1GU+L7Lg0ZGRliypQpIiMjo7SrQkREVOqCg4MFADF79mwRGhoqAIhy5coJZ2dnAUAAEC4uLuL06dMiISFBNG3aVAAQDg4OwtHRUVrH19dXxMTEGN3HgwcPROvWraV1AQh3d3fZcr9+/UR2dra0zcKFC4Wfn59Qq9UCgHBzcxN+fn6yf7qqVq0qlaVSqYSHh4dQqVTSawEBASIyMtJo/bTbfvXVV6JOnTrSOXBzc5O2V6vV4ueffza6fVJSkggJCZHWtbGxMdj/W2+9ZXTbcePGCQBi3LhxFr0XFhYm/Pz8hJ2dnQAgnJ2dDc7L7du3ZeV8+OGHsro4OTmJcuXKScv+/v7i3LlzBvtfuXKlbDt7e3vZOQEgVqxYYfS4TFmxYoUAIKpWrSrWrl0rHYe7u7v0uwYgGjRoIFJTU42WsWHDBmFvby+t6+rqKluuXLmyiIiIkG3TokUL4enpKa2jf85ef/31Ih2Dvrlz5woAIjg4WHz++efSvb7+56Fz584iLy9P0bkra0o0iNKVn58vzp49Kz777DPRrVs34eTkJAuq9AMstVotbGxsnlT1nhsMooiIiP6PNojy8PAQXl5eYtOmTSInJ0fk5+eLU6dOierVqwsAol27dmLgwIEiMDBQ7NmzR2g0GqHRaMT+/fuFj4+PACBefPFFg/Kzs7NFy5YtBQDRrFkzsWPHDpGeni6EECItLU38+uuvwtfXVwAQM2bMMNheG+AUdrM+fPhw8e2334qoqCgpGMvOzhb79+8XrVq1kvZvjHYf7u7uwtPTU2zcuFHk5uYKIYSIiIiQzpGtra04e/aswfaDBw+WAq9vvvlGOr779++LiRMnSjfOS5cuNdhWaRClpa3T3LlzzZ6XRYsWSYHGp59+Ku7fvy+EECIvL0+cOXNGdOnSRQAQlSpVkgUt6enpwtXVVQAQo0ePFlFRUdJ7aWlp4syZM+Ltt98WO3bsMLt/fdoAxMnJSdjb24vJkydLQV96erpYsmSJFFjNmTPHYPuzZ89K77dv315cvHhRCCGERqMRf/zxh/D39xcARI0aNQyCsPDwcOl3URyWBFEeHh5CrVaL9957T8THxwshhEhOThYffvihVAdTQfnT4okFUfqysrLE/v37xaxZs0SLFi2EjY2NFDzpBlNkXQyiiIiI/o/2ZhyAOHLkiMH7Bw4ckN53dHQUN27cMFjn559/lt7PycmRvbdkyRKpZSElJcVoHc6cOSNUKpUoV66ciIuLk71naRBlTmpqqvDz8zN5jLqtWPv37zd4PyMjQ9SqVUsAEL1795a9d/LkSWnbZcuWGd2/Nsjy9vYWmZmZsvdKMoiKj4+XHtobOy4hhMjNzRXNmzcXAMSiRYsMjsvZ2VkKKK1BG4CYOi4hhJg5c6YAIGrWrGnwXs+ePaX3tMGqrnPnzglbW1sBQCxcuFD23pMMosz9bgYNGiQAiNDQ0GLVo7SV2mS79vb26Nq1Kz799FMsW7YMb731lpS8Qb+fLxEREVFJ6tChAzp06GDwenBwMOzt7QEAQ4YMMToXZo8ePQAAmZmZuHHjhuy9n376CQDw8ssvw9XV1ei+mzdvjgYNGiAnJwfh4eHFOg5jXFxcEBwcDAA4evSoyfXat2+Prl27Grzu6OiIt99+GwCwe/duJCcnS+9p59SsVKkSJk+ebLTcBQsWACiYU3Pfvn1FO4giWLt2LTIyMtCiRQujxwUAtra2GDlyJABgz5490useHh4AgJycHDx69KhE6vfBBx8Yfb1///4AgKioKGRkZEivJyUlSXV8++23jWa7btq0KQYNGgQAWL9+vbWrbDF7e3u89dZbRt/THt+lS5eeZJWsrkRSnBcmOjoa+/fvx759+xAeHl5iH04iIiIiS7Rq1cro6zY2NvD29sbdu3fRsmVLo+v4+flJP+smBEhNTZVuFOfMmYOPPvrI5P4fP34MAFIiiaLYvn07Vq9ejdOnTyMuLk52A65lLhFCly5dCn0vPz8f586dk5IUnDlzBgDQuXNn2ZybuurVq4eAgADcvXsXZ86cQd++fS0+puLQBoxXrlxBhQoVTK6XmZkJQH7ua9Sogbp16yIyMhKtW7fG9OnT0aNHDzRq1MgqWaXLly9vNCAHgIoVK0o/JyYmSsHSuXPnpAQkoaGhJsvu1q0bNm7cKE3ZY2dnV+z6KtWgQQO4uLgYfU97fNrP/NPqiQRRjx8/xl9//YX9+/dj//79uHXrlvSe9sOgpVKpEBQUhNDQUHTr1u1JVI+IiIiec6ZaiYCC1gpz62jfByCbZ/LBgwfIz88HYPkNo7HApzD5+fkYPXq0rOXB1tYWnp6eKFeuHAAgOTkZWVlZSE9PN1mOuclTdd/TnRZG+3NhE69WqlQJd+/eNTmlTEm4d+8egIIgSRsomaN77m1sbBAWFoaBAwfi1q1bmDVrFmbNmgUnJye0a9cOgwYNwrhx44o896klnzdA/nnSPXfmznelSpUAAHl5eXj8+LEsyH9SLDm+vLy8J1WdElEiQVR2djaOHDkiBU0XLlyQBUv6gVO1atWkeaO6du0KLy+vkqgWERER0ROj0Wikn0+cOIHWrVuXyH5+/vlnrF+/HjY2Nnj//fcxZswYVK9eXdYyNGbMGKxZs8bgHkyXueEUhQ21sHQoxpMcsqE9/9OmTcPSpUsVbx8UFITIyEhs374de/bswbFjx3D16lXp/vbTTz/Fjh070KhRI2tX3Wo4RKbkWDWI+uyzz7Bv3z4cO3YM2dnZ0uv6X1gvLy907twZoaGhCA0NRfXq1a1ZDSIiIqJSp9sCcPny5RILorTjkiZPnoz58+cbXefBgweFlmOuq5/ue76+vrKfr127hjt37lhUto+PT6H1sBZtF77Lly8XuYxy5cph0KBB0jijR48eYfPmzZg9ezbu3LmDcePG4dy5c1apb2F0z3tsbCxq1KhhdD3tuda2RlLJsGpiiffeew/h4eHIysqCKMj8ByEEHBwcEBoaiv/97384c+YMHj58iI0bN2LKlCkMoIiIiOiZ5Onpifr16wP4v0BHKW1rkrkWJG0A07RpU6Pvp6WlFTpZLgCzSS2076nVatl+WrRoIb2v7bqoLzIyEnfv3gUAk+PKlLLkvLRv3x5AQStgccaa6fLy8sLUqVPx2WefASiYxPdJje1v1qyZdNwHDhwwud7+/fsBFLSk6Y6H0m2ZNHfeyDIllp3P1tYWEyZMwIEDB5CYmIi9e/finXfeQbNmzdi0SERERM+FKVOmACi46S0skDI2bsrNzQ1AQWY2U9zd3QEAFy9eNPr+ggULkJqaWmhdjx49ioMHDxq8npWVhS+//BJAQSZCbeY6ABgxYgQA4O7du1ImQn0ffvghAMDb29tsQgQlLDkvY8aMgaOjIzQaDV555RVZ90p9+fn5srJ0e1QZo80oDcAqiSYs4eHhIWWCXLhwodHxcxcvXsRvv/0GAFLWQS3tOQPMnzeyTIkEUSqVChqNBitXrsQbb7yBDz74AHv37rVoUB8RERHRs2LatGlSN74xY8bggw8+kHV9y8jIwMGDB/Hqq68a7Z7VsGFDAMDmzZtlmf909ezZEwDw448/Yvny5cjJyQFQ0IXvjTfewOeff27ReHN3d3cMHjwYmzdvlgb9R0ZGok+fPoiMjISNjY1BhsFWrVph8ODBAIDXXnsNS5YskW7uHzx4gJdeegmbNm0CUBDMOTg4FFoPS2jPy86dO6VWLn0VKlTA//73PwDAjh070K1bN/z9999SMCWEQGRkJL766is0bNgQ27dvl7YNCwtD+/btsWzZMty8eVN6XaPRYM+ePZg1axYAoG3btrKgsqR9/PHHsLOzQ1RUFHr06CF1VczPz8fOnTvRu3dv5OXloUaNGpg6daps29q1a0uJRn766Se2RhWXNSed8vX1lSbK1U6Wq/vPwcFBhISEiI8//licPHlS5OfnW3P3ZAFOtktERPR/LJm01ZIJb/H/JxgNDw83eC8+Pl506dJFWgeAcHNzEx4eHkKlUkmv2draGmx76NAhaR0bGxvh7+8vqlatKpvoNDExUdStW1cqR61Wy8qeOnWq2Ylrtcf31VdfiTp16ggAwt7eXri7u0tlqlQqsXz5cqPHnpSUJJu02NbWVnh6esqO7a233jK6bVEn271+/bpwcHCQjtfPz086L3fu3JGt+/nnnwsbGxupLuXKlRNeXl7Czs5O9jtZs2aNtI3upLja8+Hl5SXUarX0WsWKFcU///xj9LhMMTdRrdatW7ekfdy6dcvg/bCwMFGuXDnZZ0l7LgCIypUri4iICKNlT5o0SVrPyclJVKlSRVStWlW8+eabVjkG7WS7wcHBJre31qS/pc2qLVFxcXG4ePEivvzyS/Tq1QvOzs6ysVHZ2dk4fPgw5syZg7Zt28LLywuDBg3C0qVLDSanIyIiInoWeHt7Y//+/di2bRuGDBmCypUrIzs7G5mZmQgICECvXr2wZMkSREdHG2zbqVMn7NixA6GhoXB3d0dcXBxiYmJkY3w8PDxw7NgxzJgxA4GBgbCxsYGtrS1CQkKwfv16/PDDDxbV09PTE6dOncKsWbNQpUoVZGdno3z58ujbty/+/vtvvPTSS0a3c3d3x4EDB/Dzzz8jJCQErq6uSEtLQ4UKFTB48GCEh4dj4cKFRTp3ptSqVQvh4eHo168ffHx88OjRI+m86KfOfvvttxEZGYk33ngDjRs3hoODA5KSkuDi4oKWLVvinXfewbFjxzBq1Chpm379+mHVqlWYMGECgoKC4O7ujuTkZLi6uqJVq1ZYsGABrl69irp161r1uCwxfPhwXL16FVOnTkWNGjWQnZ0NW1tbNGnSBPPnz8eVK1dQr149o9t+9913mDdvntSSd/v2bcTExCAhIeFJHsIzQSVEybXl5eXl4fjx4zhw4AD279+PU6dOGXywdcdHVapUCd26dZNSnT/JDC7Pi8zMTMyYMQOLFy+W9eclIiIqCzQaDZKTk+Hu7v7ExpoQESlVokGUvrS0NBw8eFDKrx8REWFYof8fVKlUKjRq1Ajnz59/UtV7LjCIIiKisujevXvYu3cv7ty5g/z8fKjValSuXBndu3dHxYoVS7t6REQyTzSI0hcXFycFVAcOHDCYn0CboIKsh0EUERGVNWfPnpUN6tf3wgsvoHnz5k+wRkRE5ll1sl2l/Pz80LVrVwghkJ+fj99++03K4FeKsR0RERE9Iffu3cPOnTvNrrNr1y74+/uzRYqIyownHkSlp6cX2qWPiIiInn1CCOzatcvkJK1aGo0Gu3btwsSJEznXJBGVCSUeROXn5+PkyZPYt28f9u/fj5MnT0rJJUy1NmnHQ1lrQjYiIiIqe27evGnQld+U2NhY3Lx50+hcSkRET1qJBFGRkZHYv38/9u3bh0OHDslmydYGTvpPkqpUqYKuXbsiNDQUoaGhzMxHRET0DBNC4ODBg4q2OXjwIKpXr87WKCIqdVYNoiZMmID9+/fj3r170mu6QZNuy5OHhwc6d+4sBU01a9a0ZlWIiIioDFPSCqXF1igiKiusGkT9+uuvUrCkHzTZ29ujffv2UtDUrFkzPkkiIiJ6DhWlFUrreW6NOnjwIDp37gzA+gm4Vq5ciQkTJqBq1apGJ/0lIjmrd+fTbXlq3ry5NHFuhw4dYG9vb+3dERER0VOmKK1QWk+6NWrlypWIjo5GSEgIQkJCnsg+qWxLSkrC4sWLAQAzZsyAh4dHqdaHSodVg6iaNWtKLU1dunThh4qIiIgMnD59utjbP8kg6tChQwBQ6kGUk5MT6tSpUyJlu7u7o06dOggICCiR8p8lSUlJmD9/PgBg/PjxvN99Tlk1iLp+/bo1iyMiIqJnTH5+Pm7dulWsMqKjo5Gfnw+1Wm2lWj0dWrVqhcjIyBIpe+DAgRg4cGCJlE30LHq+rj5ERERUqlJSUpCTk1OsMrKzs5GSkmKlGhERKccgioiIiJ4YV1fXYieFUKvVcHNzs1KNjFu5ciVUKpXUlW/+/PlQqVSyf7oJGLSvHTx4EA8fPsTMmTNRu3ZtODk5yY43MzMTf/zxB1566SU0adIEPj4+sLe3R8WKFTFgwADs2rXLZJ0OHjwo7cdUfQMDAwEAZ8+exbBhw+Dv7w97e3tUr14dM2fORGJiotnj1W6va968eVCpVFJ3xgMHDqBPnz7w8fGBg4MD6tWrh/nz5yMrK8vsOd22bRu6du0KDw8PuLi4ICgoCJ9//jlyc3MN9qHUxo0b0atXL/j5+cHOzg4eHh6oVasW+vXrh++++85k3ZKTk/Hxxx+jdevW8PT0hL29PSpXroyRI0fixIkTBuuHhISgWrVq0nK1atVkn4nS7vJJT06JT7ZLREREpGVjY4MKFSrg/v37RS7Dz8+vxLvyOTo6ws/PD48fP0Zubi6cnZ3h4uIiW8fGxsZgu6ioKIwYMQJxcXFwcHCAnZ2d7P0NGzZgwoQJsv3Y2tri/v372LZtG7Zt24Y333wTX3zxRZHrvm7dOowfPx65ublwd3dHXl4ebt26hUWLFmHv3r04ceKEwbFYauHChXj33XcBFIyjysnJQWRkJObNm4dDhw5h3759Rs/LW2+9hS+//FJa9vDwQEREBN59913s2LEDHTp0KNrBApg0aRJ++eUXadnFxQW5ubmIiopCVFQU/vzzT/Tp08cgQDx58iT69++PuLg4AAW/TycnJ8TGxiIsLAwbNmzAxx9/jPfee0/apnz58vD29kZCQgIAwNvbW3a85cuXL/Jx0NPlibRE5efnIzExEXfu3MHt27cV/SMiIqJnS/fu3Ut1e0sMHz4cDx48QLt27QAUBAEPHjyQ/atcubLBdm+88QY8PDxw4MABpKenIyUlBdeuXZPe9/DwwJQpUxAeHo6EhARkZGQgPT0d9+7dw/z582FnZ4cvv/wSf/zxR5HqHR8fj4kTJ2LcuHG4ffs2kpKSkJqaiiVLlsDOzg5Xr17F559/XqSyL168iFmzZmHWrFl4+PAhEhMTkZSUhA8//BAAEB4ejl9//dVgu7CwMCmAGjVqFGJjY5GYmIjU1FQsX74cp06dwtKlS4tUp6NHj+KXX36BWq3GZ599hkePHiE1NRXp6elISEjAnj17MG7cOJQrV062XXR0NHr27Im4uDgMGTIEZ8+eRVZWFlJSUhAXF4c5c+bAxsYGs2fPxu+//y5tt2XLFllilNOnT8s+E1u2bCnScdDTp8Raog4ePIhVq1bh+PHjuHHjRpHmM1CpVMjLyyuB2hEREVFpCQwMRKdOnXD48GHF23bq1Mlol7OyQq1WY//+/ahUqZL0Wu3ataWfBwwYgAEDBhhs5+/vjw8//BBOTk54++238c0336Bfv36K95+RkYFx48bhxx9/lF5zcnLCK6+8gps3b+Krr77C+vXr8dFHHykuOykpCXPnzsW8efOk19zc3DB//nxcuXIFW7Zswfr16zFx4kTpfSGEFGR169YNa9askbojOjg44KWXXoKdnZ2sdU6JY8eOAQBCQ0PxzjvvyN7z8vJC9+7djQbdb7/9NpKSkjBmzBisWrVK9p6vry8++ugjeHp6YubMmZg3b57R3xk936zeEnXv3j306NEDXbt2xa+//opr164hPz8fQogi/SMiIqJnT+fOnTFmzBgEBAQU2jVPrVYjICAAY8aMkSabLavGjBkjC6CU6tOnDwDg+PHj0Gg0RSrjgw8+MPp6//79ARR0OczIyFBcrr29Pd566y2zZV+6dEn2+oULF3Djxg0AwOzZs42O5xo3bhyqVKmiuD4ApPTi8fHxFp+vx48fSy1Gs2bNMrne2LFjARS0wGm7/BFpWbUlKiEhAZ07d0ZUVBSEEM/lbOJERERkmerVq6N69erIy8vD48ePjT48ValUKF++PGxtn45h3O3bty90nbi4OHz//ffYu3cvrl+/juTkZIMAICMjA4mJifD29la0//Lly6NmzZpG36tYsaL0c2JiIpycnBSV3aBBA5NjqbRlP378WPb6uXPnAAB2dnZS10h9KpUKwcHBWL16taL6AAUtUA4ODjh//jw6duyISZMmoUuXLrLkD/qOHz+O/Px8AECXLl0s2k9MTAz8/PwU14+eXVa9Ir333nu4ceOGlKFECIEWLVogODgYgYGBcHZ2ZmBFREREMra2tvD19S3talhFYcdx/Phx9O7dG0lJSdJrLi4uUhY/jUYjJS1IT09XHES5urqafE83EM3NzVVUrqVl6w/DiI+PB1DQtU5/XJKuok7yW716dfz000+YNm0ajh8/juPHjwMAfHx80LlzZ4waNQr9+vWT3X/eu3dP+tnSFqaitNzRs81qQVRycrKUHlMIgYCAAKxfv75Y2VaIiIiInibGMtNp5eXlYeTIkUhKSkKTJk3wySefoEOHDrLg5N9//5Vakp6FYQ3aYyjsIXpxjvXFF19Er169sGnTJoSHh+PYsWO4c+cONm7ciI0bN6Jjx47Yvn27lBZf2+rn6OjI4IiKzGpjog4ePAiNRgMhBGxtbYudrpKIiIjoWXL8+HHExMTAxsYG27dvR69evQxadx48eFBKtSsZ2pa5hIQEs5Ms67YOFUX58uUxdepUhIWF4fbt24iKisKsWbOgUqlw5MgRWTKMChUqACiYsysqKqpY+6Xnl9WCKG06cpVKha5du6Jx48bWKpqIiIioVGiTXlijVejOnTsACrqameq+tn///mLvpyxp1qwZgILug9pMevqEEEXK1GhOjRo18Omnn2LUqFEAgH379knvtWvXTmoZCwsLU1y2biKUZ6G1kIrGakGUbnNokyZNrFUsERERUanRdgHTHcNUVO7u7gAKxuEYG4sTGxuLb775ptj7KUuaNGkidU/83//+ZzToWLNmDWJiYopUfnZ2ttn3HR0dAci7Wfr6+krZBBcuXIjr16+bLUM/WYb2MwFY53NBTyerBVG6GV+elgw6REREROY0bNgQALBz507cvXu3WGV16NABzs7OEEJg2LBh0s27RqPBnj17EBIS8swl4FKpVJg/fz4ASBPfarvuZWVl4eeff8bUqVPh6elZpPJfffVVDBs2DL/99hsePnwovZ6WloYffvhBmgOqd+/esu2+/PJLeHl5ISUlBR06dMAvv/yC5ORk6f2EhARs2bIFgwYNwsiRI2Xbenh4SC2JK1as4JymzymrBVF16tSRfo6NjbVWsURERESlZty4cXBwcEBUVBSqVKmCChUqIDAwEIGBgYrvd9zd3fHFF18AAA4fPow6derA1dUVLi4u6NmzJ5KTk7FixYqSOIxSNWrUKMyYMQMAsHr1alSqVAnly5eHm5sbJk+ejLZt22LatGkACibgVSI3NxebNm3CkCFD4OfnB1dXV3h6esLV1RXTp09HTk4OOnTogPfff1+2XfXq1bFv3z4EBgYiPj4ekyZNgqenJ8qXLw9XV1f4+Phg8ODB2Lp1q5QOXZe2vt9++y1cXFxQpUoVBAYGYsSIEUU4Q/Q0sloQ1apVK9SsWRNCCBw4cIB9RImIiOipV6tWLYSHh6Nfv37w8fHBo0ePEBMTg5iYmCK1QEybNg07duxASEgIXFxckJeXh4CAALz22mu4ePEiGjVqVAJHUfoWLVqELVu2ICQkBK6ursjOzka9evWwcOFC7NmzB+np6QD+b/JcS82ZMwfffPMNBg4ciLp168LW1hZpaWnw9fVFt27d8Msvv+DgwYNwdnY22LZp06aIiIjAkiVLEBoaCm9vb6SmpiI/Px+1atXCqFGjEBYWJk3Mq2v27Nn4+uuv0aJFC9jZ2SE2NhYxMTHPXGIQMk0lrBjt/PLLL5g8eTJUKhV+/PFHTJw40VpFk5VkZmZixowZWLx4sdRPmIiIiKg0tW/fHseOHcNHH32EOXPmlHZ1iApltZYoAJg4cSKGDh0KIQRef/11HDp0yJrFExEREdEz5tChQ1Lmvp49e5ZybYgsY9UgCijIsDJhwgRkZGSgW7dumDlzJv79919r74aIiIiInhKvvPIKVq5ciQcPHkhDPpKSkrBs2TIpU16XLl3QsmXL0qwmkcUsTqPXpUsXRQWXK1cOOTk5+Prrr/H111/D398fAQEBRvukmqJSqXDgwAFF+yUiIiKisuXvv//G999/DwCwt7eHk5MTkpKSpICqfv36UiY9oqeBxUHUwYMHFafdVKlU0pfj3r17uH//vsXbCiGeuTSfRERERM+jjz76CFu3bsWpU6cQFxeH5ORkeHp6okGDBhg0aBCmTJkCJyen0q4mkcUUT+hUnDwUzNhHRERE9Pzp168f+vXrV9rVILIai4OoTp06sWWIiIiIiIiee4q68xERERERET3vrJ6dj4iIiIiI6FnGIIqIiIiIiEgBBlFEREREREQKKM7OZ0716tUBAHZ2drh27VqRy2nUqBHS09OhUqk4US8REREREZUpVg2ioqOjCwq1LV6x0dHRUhBFRERERERUlrA7HxERERERkQJWbYmisk874XFWVlYp14SIiIiIyLocHByeSG+2MhdECSGkG3xHR8dSrs2zJzs7GwAwa9asUq4JEREREZF1LV68+InEECqhbZqwArW6oHegra0tcnJyilRGZGQk6tevD5VKBX9/f8TGxlqregQgPz8fycnJsLe355gzIiIiInqmPJctUampqfjggw+k5Xr16pVibZ5NarUanp6epV0NIiIiIqKnluIg6tdff8Wvv/5qdh2NRoMuXbpYXKZGo0FiYiKuX7+O3Nxc6fWuXbsqrR4REREREVGJUhxERUdH4+DBg2abyYQQOHTokOLKCCGkcj08PDBx4kTFZRAREREREZWkInfnMzeUSqVSmX2/sHIrVKiA9evXw9fXt6jVIyIiIiIiKhGKg6jAwEAEBwcbfU+39cnUOsbY2dnB1dUVgYGBaNu2Lfr27Qt7e3ulVSMiIiIiIipxVs/Op1KpYGNjU+TsfERERERERGWZ2toFWjEmIyIiIiIiKnOsmuJ8xYoVAP5vvigiIiIiIqJnjVW78xERERERET3rrNoSpZ0bytbWFnv37i1yOX369EFmZiZUKhUOHDhgreoREREREREVm9UTSwAFQVRxEku4uroiPT0dKpUKGo3GWtUjIiIiIiIqNg5eIiIiIiIiUoBBFBERERERkQJlMojKzs4GAE64S0REREREZU6ZC6Kio6ORl5cHlUoFd3f30q4OERERERGRTJkLoj7//HPp59q1a5diTYiIiIiIiAwpTnG+bds2bNu2zew6+fn5mDhxosVlajQaJCYm4ty5c7h//770eqdOnZRWj4iIiIiIqEQpTnE+f/58zJ8/HyqVyuA9bVHG3rOEEAIqlQpCCDg6OuLq1asIDAwsUlmkTHp6OqKiopCUlITMzEy4u7vDx8cH1atXl1LXP6uio6MRFxeHpKQklCtXDh4eHqhWrRo8PDyKXGZmZiYiIiLw6NEjqNVq+Pn5oW7durCzs1NcVk5ODvbu3QshBOzs7NC9e/dn/ndCZIn4+Hjcvn0biYmJEELAw8MDFStWREBAQGlXDQCQl5eH6Oho3L9/H+np6cjPz4ejoyO8vb0REBCA8uXLF7ns3NxcREVF4fHjx0hNTYWzszM8PT1Rq1atYo0nfvz4MSIjI5GSkgIHBwdUrlwZ1atXL9Lf9fv37+PMmTMAAF9fX7Ru3brI9SKyhsePHyM6OhqPHz9GdnY27Ozs4ObmBj8/P1SqVKlIf6OBsnstSklJwa1bt5CYmIjMzEzY2NjAyckJ/v7+qFy5MsqVK1fsfTzP16JiTbZrLv4q6vRT2gBqxYoVDKCegLi4OGzZsgVXrlxBXl6ewfvu7u7o2LEjevXqBVtbq87NrFh+fj4++eQT3LlzR/b6uHHj0K5dO8VlhYeH46+//kJCQoLB+yqVCnXr1sWAAQMUfQ7z8/Oxa9cu7Nq1C7m5ubL3nJ2dMXjwYLRv315RXXfu3Ildu3YBAHr16sUAip5aYWFhCA8Pl73Wtm1bjB8/XlE5V65cwY4dO3Dz5k2j71eqVAndu3cvtZv2hIQE7Nq1C2fOnEFWVpbJ9Tw8PNCwYUO8+OKLFn+vMzMz8ccff+DEiRPIyMgweN/e3h7NmjXDwIEDFY0rTk9PR1hYGE6dOmXwnr+/P8aMGYMaNWpYXB4ArFmzBlFRUQCAGTNmKNqWyFry8/Nx8uRJ/PXXX7h9+7bJ9WxsbFCjRg307NkTDRo0sKjssnotOnv2LPbv32+yXgBgZ2eH5s2bo1evXqhQoYLiffBaVISWqK+//hqLFy82+l5MTExBoSoVqlSpYnGZdnZ2cHV1RWBgINq2bYvRo0cX6RdKypw4cQLr1q2TsiGaU7lyZUybNg3e3t5PoGbG7dmzB1u2bDF4XWkQlZaWhuXLl+PatWuFrmtjY4OBAweiW7duFpW9atUq/P3332bXGThwIHr27GlRefHx8Zg3bx7y8vLg6emJjz76yCpPjoietJs3b+Lzzz83eMCmJIgSQmDjxo3466+/LFq/ZcuWGDduXJGfLhfFX3/9hd9++83oQylTlixZYlEdb9++jR9++AGPHj0qdF1XV1dMmjQJ9erVK3TdzMxMfPnllwYPqHTZ2dnhtddeQ506dQotDwBOnjyJX375BQDQtGlTTJs2zaLtiKwpISEBP/30E27dumXxNj169MCgQYPMrlNWr0XZ2dn46aefcOnSJYu3sbW1xdChQxESEmLxNrwWFVDctPCf//wH//nPf4y+p1aroVKpYGNjo+gDS0/e5cuXsXLlStkNja+vL+rWrQsnJyfEx8fj0qVLUmvKnTt3sGTJErz77rtwdHR84vWNj4/Hn3/+WexyNBoNfvjhB9y4cUN6Ta1Wo1GjRvD390dWVhaioqIQGxsrrb9582Y4OjqiQ4cOZss+deqULIDy9/dHo0aNkJeXh/PnzyMxMREA8Pvvv6NOnTqoVq1aofXdsGGDdDM2ZMgQBlD0VNJoNFi9enWReyhobd261eCmpUaNGggMDIRarUZsbCwiIyOl/Zw+fRpqtVrRGN3iWL9+PQ4ePCh7zdnZGXXr1oWnpyfs7e2RlpaGu3fvIiYmxqC12pzHjx/j22+/RUpKivSak5MTGjduDE9PT6SkpODy5cvS+6mpqVi6dCnefvttVK5c2WzZv/32m+ympV69eggMDERycjLOnj2L7Oxs5Obm4pdffsG8efMK/RuQlZUlPfCys7PD0KFDLT5OImu5d+8eFi1aJPvOqFQqBAYGolKlSnBzc0Nubq7Uxc9YrxRTyuK1KD8/H999953BA2IvLy/UrVsX7u7u0Gg0iI+PR0REhNRKnpeXh/Xr10OtVluUi4DXov9j9f5Zxf0jSSUvOTkZP/30k2wM2+DBg9G1a1dZl5LU1FQsX74c169fB1DQp3Tt2rWYPHnyE6/z2rVrpRsOd3d3JCcnF6mcrVu3ygKogIAAvPzyywYtbCdPnsSqVaukAGbdunWoVq2a2f7N27dvl35u3rw5Jk2aBBsbGwBA//79sXjxYty6dQtCCPz55594/fXXzdb1ypUruHz5MoCCTJUtWrRQdrBEZcTu3btx7949AEX//l66dAl79uyRlp2cnDBlyhSDp5u3b9/G999/Lz20OHnyJGrWrFniiYp27dolC6CcnZ0xdOhQtGrVSroO6MrOzsalS5csepIthMCyZctkNy0tW7bE6NGj4eDgIL2Wm5uLrVu34sCBA9I+li5divnz55t8Av748WMcPXpUWh4+fDi6dOkiLffo0QOff/450tPTkZSUhEOHDhXakr5jxw4kJSVJ23t5eRV6jETWlJqaim+++Ub2nWnSpAmGDh1qskfN3bt3cezYsUJvzMvqtejw4cOyAMrW1hYjR45Eu3btDLoLZ2ZmYvPmzbLv/ubNm9GoUSN4enqa3AevRXJWHVwxd+5czJ07F3PmzLFmsWRlO3bskPXT79u3L7p162bwJXN1dcXrr78Of39/6bUzZ86Y7VNcEo4dO4Z//vkHQEHQo3T8k1ZiYqJsPIarqytmzpxp9ILaunVrjB49WlrWaDRms1Jqk1MAgIODA0aPHi27cXJwcMC4ceOkAZERERFITU01WV5eXh42bNgAoKClbMSIERYeJVHZ8uDBA+zcuRMAUK5cOQwYMEBxGUII/P7779KySqXC9OnTjXYPqVKlCt544w3ZH+rt27cjJydH8X4tde/ePVlLuYeHB2bPno22bdsaDaCAgvECLVu2xLvvvltoF59z584hOjpaWq5Xrx4mTZoku2kBCp60Dhs2THaNfPToEQ4dOmSy7DNnzkgP1GrWrCm7aQGAChUqyH5nxsYp6Hrw4IF04+Tl5YUePXqYXZ+oJGzcuFEKXoCC8cTTp083OyQhICAAQ4cORa9evUyuU5avRfrjTYcPH44OHToYHW/p6OiIMWPGoFmzZtJr2dnZOHHihNl98FokxyDqOZOSkiLrcubj42M2krezs5PdwAshpBuiJyElJQWbN28GUHCxevHFF03elBRm7969snEKAwcOhIuLi8n127Zti1q1aknLFy9elLr56dPtvtq4cWM4OTkZrOPv7y8lqRBCyC5E+vbt24eHDx8CAEJCQko9ww9RUQghsHr1aul716dPnyI9Cbxw4QLu3r0rLbdu3drsPIJ+fn7o3r27tJycnCx7wmlta9asgUajAVBwnTLWul0cutdclUqFUaNGmc1QNWTIENlNzZ49e6T66dO9drVp08boOq1atZJuxO7du2c2WcaGDRukfQ0dOpRdkOmJ++eff2Q32EFBQUV6eGNMWb0WJSUl4cGDB9Kyu7t7oUMQgIKH6LoKGyvOa5Ec03w9Zy5cuCALJDp27FhoUFK3bl1Zoo8rV65YlIzCGjZs2ID09HQABXVVmpFFSwiBc+fOSctOTk5o1apVodsFBwfLlnXL0KXbl7pSpUomy9PtD2yq/3ViYqKUjc/V1dXgIkf0tDh8+LCUEalixYoWJ2jRd/bsWdmyJQOgO3bsKHsCq1+GtURFReHff/+Vltu3b4+qVatarfy4uDjZw5sGDRrA19fX7DbOzs6y61tKSoqsG7Mu3euQqfEKDg4O0j6FEHj8+LHR9c6fP4+IiAgABU+omzZtaraeRCVh9+7d0s+2trYYNmyY1couq9cibZc1Le3YrMJUrFhRFuTol6OL1yJDDKKeM/oZW3Sbcs3RXS83N1f6cJaky5cvS3n93dzcMHDgwCKXFRMTI7s4NG7c2KIsOUFBQbIg8+LFi0bX030a4uzsbLI83fcyMzONrrN582YpSB04cKDRVi2isi4pKQlbt24FUPDEUr+Lq6U0Gg2uXr0qLXt6elqUlMXT0xPVq1eXlm/evIm0tDTF+y+M/lNlJRmuLKF/zSnKNdtYOVrWunbl5uZi06ZNAAqymrILMpWGhIQEWWtKw4YNrdYqXJavRfr5CJS0uujO5WSuVYnXIkMMop4z2qfCQEFg4uPjY9F2+i1App4kWEtWVhbWrVsnLQ8dOrRYwYTucQOQXdDMKVeunOyJSGxsrNH5EHRvDk01Veu/ZyyIu379uhQ4BgYGFnn8F1FpW79+vfQHrkOHDkVuRb57967sO6ekHN3veX5+vsF1oLg0Go2sdVo7gaU16dfZ0uOvVq2a7Em0qWu27rXLXFp23WuXsTkDd+/eLaU77tKlC6cpoVJx+vRpWUDRsmVLq5Vdlq9F+t2kTbXQ6MvJyZGNzzZ3T8hrkaESDaL27duH//znP2jTpg0qVaoEZ2dn2NjYWPyvtCd3fdYkJSXJonYlf+z1171//77V6mXM77//Ll0E6tWrZ1HXO3P066tkHjP9dXX7HWvpjq0yN2+C7nv6T1ry8/MRFhYGoOBp0IgRI4o0MzdRaTt37hwuXLgAoKBLanFakfW/b0quW5Z8d4vj7t27sq7NNWvWtGr5gPzaZWdnBz8/P4u2c3BwkN0QPXjwwGj2XGtcuxISEqRsZW5ubnjhhRcsqiORtelPr6M7rrm4yvK1yM3NTVafW7duWRRInTt3Dvn5+dJy48aNTa7La5GhEolSTp8+jQkTJkgZ1QCmPi8L9L+05cuXt3hbNzc32NraSk8HtJnoSsLNmzelVMF2dnYYNWpUscsszrHrp/t88OCBQUuW7jgoUwMz8/PzpXTx+tsAwMGDB6UBq+3atbOomwBRWZOZmSlllgQKWpHNdc0oTHG+u/rrWvvGRTvBvJY2cYxGo8GFCxdw6tQp3Lt3D8nJyShXrhzc3d1Ro0YNBAUFoUGDBoWWr9FoZOMEypcvr+jBSvny5aVrdW5uLh49emTQtalSpUrSk+Fr166hYcOGBuXcvXtXelrt6Oho8NR706ZN0hQUgwcPNsjURfSk6H4nPTw84O7uDgB4+PAhjh07hoiICDx+/Bg5OTlwcXGBn58f6tevj9atW8PNzc1s2WX5WgQUZCBcvnw5gIL7jZUrV+LVV1812bUvLi5OStwFFCTBaN26tdF1eS0yzupB1LZt2zB8+HDk5ubKAifdk23qdf33yLr0BwyamwtAn0qlgoeHh/Ql0k0dak36k3L26tWr0IGLltA9drVaXejFUpf+eTJ27LVr15aCzOjoaERERKB+/fqydQ4dOiR9+d3c3GQZ91JTU6UUyY6OjlbLJET0pG3evFn6vtWrV8/kH2VL6V+3ivMAxNrXLf0Wbm9vb9y9exe//PKLQSbP7OxspKamIjY2FocOHUK1atUwduxYVKxY0WT5ycnJsqfESq7ZxtZPSkoyuHGpV6+elBr56NGj6Natm8H1cceOHbL1df9uR0RESK2ONWrUKPbvm6ioMjMzZdcLb29vaDQa7Ny5Ezt37pR9l4CC7+SjR48QERGBP//8Ez169EDv3r1NBgdl+VoEFMxPGRwcLKURv3btGj7++GP07NlTmmw3Pz8fDx8+xPnz57F3715pHJKLiwumTp1qcqw4r0XGWTWIunXrFsaOHYucnByoVCrY2NigZ8+eaNSoET777DMIIaBSqTB37lykpaUhLi4OZ86cQWRkJICCG3UXFxdMnz6dg+lLgH5GPd3BhJbQjejz8/ORm5trUXIGJXQn5fT397daXn/dY7e3t1f0BEX/SYaxzIQuLi5o3rw5Tp48CQD48ccfMWLECAQFBUGj0eDEiRPSrNmAYVbErVu3Sn2t+/btqyjIIyorrl+/Lk2hoJ3osbj0U9gquW7pr2vtrKLazKFaKSkpWLp0qdm0u1q3bt3CZ599hpdffhl16tQxuo41r9mA4bkEgEaNGsHLywuPHj1CRkYGFi1ahFGjRiEwMBDJycnYtWuXLJuYbuIMjUYjtTqyCzKVNv3vo7u7O1atWlXo3EdAwXftjz/+wJ07d/DSSy8ZTYJTlq9FWiNHjoSvry/+/PNPZGVl4cGDB1i5cqXZbRo3boyRI0eaDQp5LTLOqkHUp59+KnvSvmvXLinX+8KFC6XBYHPnzpVtd+XKFSxYsACbNm1Ceno6tm/fjt27d1t9gO7zTv9DqzQA0h+jlp2dbdUgSndSTgAYNWqU1cbF6V4AlNZZf31TF79Bgwbh8uXLyMjIQEZGBn755Rej6/n6+srm5rp16xaOHTsGoCDdqLWzexE9Cbm5uVizZo3Uity7d2+L+8ybo/99U/L9tfS7W1T6maHWrVsnXWerVauGHj16oEaNGnByckJKSgquXLmCnTt3Sk+hs7KysGzZMnzwwQdGb2CKc+yA8Wu2PrVajZEjR+K7776DEAL37t3DF198YbS8Vq1ayQK+/fv3S92SOnbsqGisKZG16Sd9ioiIkL6jdnZ2CA0NRcuWLeHj4wONRoO7d+/iyJEjOHnypHTdOn/+PH7//XcMHjzYoPyyfC3SUqlUCA0NRYsWLbBmzRpcvnzZ5Lq2trbo2bMnunfvXmhQxGuRcVYLonJzc7F27Vop8vv2229NTpalr2HDhtiwYQO6d++OKVOmIDIyEj179sSpU6eK1Zee5PSznSgNUPTXt+as2/qTcrZr187sBHZKafvIAlCcZln/uHXL0uXh4YH//Oc/+Pbbb02mL/X19cWMGTOkPspCCISFhUkX8OHDhxvU7+rVqzh8+DBu3ryJjIwMODk5oVq1aujUqZPRPsNEpWHHjh1Sn3c/Pz+rtSLrf9+UfH9L8poFGD6Y0s1GOHr0aNmT0PLly6NTp05o0aIFvvnmG2kAfHp6OjZu3Ihp06YZlK9/7Eqv2fo3OqauXY0aNcKoUaMQFhZmMrto48aNMXbsWGk5OTlZeujl5OSE/v37y9bXaDT4+++/pXFh2dnZcHd3R926ddGtWzf4+/srOhaiwujfmGu/j87OznjjjTcMHszXrFkTNWvWRP369bFixQrp7/C+ffvQunVrg3HLZflapJWfn48DBw5g165dBi1z+vLy8rB9+3YcPHgQ/fv3R6dOnUyuy2uRcVYLos6cOSN9YH19ffHiiy8qLmPSpEm4ffs2FixYgMjISHz66af473//a60qPvf0P/TmUkgao7++NWd/1p2U09nZ2ehToOKws7OTLlrmUpAbo3/c5p7ABAYGYv78+di/fz8uXbqER48eQa1Ww9fXF82bN0fnzp1l2//999+Ijo4GUNCfuW7dutJ7+fn5WLt2rcE8NCkpKbh48SIuXryIDh064MUXX7RoUj2iknL37l3s3btXWn7xxRet1oqs/31T8v0tyWsWYPxaULlyZYwaNcpkVxInJydMmTIF8+fPl4KwCxcuIC4uzqDlTr98pdds/RsVc9euTp06oWbNmti/fz+uXbuGlJQUODg4oHLlymjXrh1atGghW3/z5s1S/fv37y/LrJWWloZvv/1WurZpPXr0CH///TdOnDiBUaNGoUOHDoqOh8gcU5/v0aNHm+3Z1Lp1a8TExODAgQMACh5u7tu3DxMmTDBbflm6Fmn38cMPP8hanypWrIjQ0FDUqVMH7u7uUoKIS5cu4a+//kJqairS0tKwdu1a3L1712QXbF6LjLNaEKU7rqljx45m+yJqNBqTEfzs2bOxZMkSJCYmYsWKFViwYAH7WFuJfp9UU08CTNH/0ijtE2uK7qScADBkyBDZl8Aa7O3tpSBK6XHrr1/Ycbu4uGDAgAGFJofIyMjA77//DqDggjpkyBDZ+3/88YcUQNna2qJVq1bw8/PDw4cPcfLkSeTl5eHo0aNwdXVlIgoqNfn5+Vi9erV0Q9G2bVuTY3yKQv/7puT7q/S7q5Sx8nr27FnoE+ry5cujXbt2+OuvvwAU3LRdvnzZIIgqzrEDyq/ZFStWlD3hNSUqKgqnTp0CUJBRS/cJdn5+PpYuXSrdtLi5uaFVq1ZwcXFBVFQUrly5Ao1GgzVr1qB8+fIGCXiIisrY59vf39+iSWF79uyJQ4cOSd+ZS5cuIT8/X/aAsixfiwBgw4YNsgCqTZs2GDt2rOx6ZGdnh0qVKqFSpUpo3749vv32W9y5cwdAQYbgSpUqoWPHjgZl81pknNWCKN1MI8Ym4LKxsZH+yGZnZ5tMHGFvb4+ePXti/fr1ePDgAY4dO4b27dtbq5rPteIObNTtuqJWq2VPEo4fP45Vq1YVWoaXl5dB66LupJy1a9cukQlm7e3tpfF62dnZBhdHc4ozmNScbdu2SXXq2bOnbExEfHy8NNeBnZ0d3njjDdn3ql27dli0aBHy8vKwZ88etG/f3uKJk4msKTw8XOqa5uzsbPAwoLgsGZBsiiWDoVetWoXjx48XWlbbtm0N/qjr102lUlncxbZx48ZSEAUA//77L0JDQ83WV8mxG1vfGul+deezA4ARI0bIrqUnT56UehX4+vrinXfegaurq/T+vn37sHnzZgghsG7dOnz00UdsSSerMPb5btSokUXburm5ITAwUPrsZmRk4P79+7IsumX5WqQd36VVqVIlgwBKn7u7O6ZPn465c+dKQdHvv/+ONm3aGLQU8VpknNWuXLofEGPjmHQrHh8fb7Ys7VwbgOHEaVR0Hh4esmUlKTaFELL0nvrpKoUQyM/Pt+ifrqioKCklpa2tbZG6gVpC99jz8/NlM3QXRv88KU3taczdu3dx+PBhAAVpWLt37y57/+jRo9K5Cg4ONngwUbNmTQQHBwMoOB5tRjSiJyknJwfbtm2TlgcPHmz1VuTiXLf0J5s09t219Lqlf+0yVp6Xl5fFNwe6N2eA8eNyd3eX/VHXT7FcGP0y9c9lURw+fFh6ct2qVSuDyUy11zWgoFeB7t9+AOjWrZvUtSo+Pt7kvHpESrm5uRkEDfrfM3P019X/vpXla9Hx48dlUwR169bNojFbXl5eaNWqlbSclpaGK1euGKzHa5FxVmuJ0q2cfoYUoOCEaWcZjo6ORtWqVS0qtyQmJHte6Q+eMzcjtL6UlBRZc2yFChWsUifdBAx5eXmYP39+odvozyW2atUqrF69WloeO3Ys2rZtK1vH399feiIBFBy7dhK+wuh/+a0xIDosLEy6EA4bNszgqY/upLzNmzc3Wkbz5s2lPty66xM9KXl5ebIHaGvWrMGaNWvMbqP//T1x4oQ0NQBg+JRV/1qjfzNijv5311rXLS39a4GSqTn0HzYa+7tpY2MDHx8fKWHH48ePpalCLKF7rsqVK2cwMaVSaWlp+OOPPwAUPJnWH7uak5MjdZ2xt7c32SrXvHlz6ebn2rVrqFevXrHqRQQUfF98fX1l87cp+U7qr6ufmKEsX4tu3rwpW1aSmKtWrVqyB7HR0dFo2rSpbB1ei4yzWhClO2jP2M15nTp18O+//wIo+KOpfYpuTEREhPQzm/mtx93dHY6OjlLXOe0HxxK3b9+WLetfANq1a2eVbnjGnrAURgghuzEzNmGzfn3v3LmD6tWrW1R+Yceu1KlTp6Sgp0GDBggKCjJY5+HDh9LPpoI23Uk6ddcnKi3W+P7ql6H/+df/PppjyXd3/PjxGD9+vMVl6tKfKFfJYGtLB1pXqFBBunHJzc3FgwcPLHqQk5WVJbsu+Pn5FXt88e+//y7dWPbu3dvgaXJCQoL0+/P19TX5JFy3/rx2kTX5+/vLgigl38nCkkiV5WuRfu8aSx8SG1vXVHZhXosMWS1C0Y3ejDWJ6Q7sM/ekMjY2Frt375aW9VNMUvHUrFlT+jk1NbXQrpVa2gBYS7/ZtKzTPW7A8HhMycnJkQWblSpVgqOjY5HrkZWVhd9++w1AQffF4cOHG11P96m0qSw+uq8be4pN9CyoWLGi7Amx/hNXc3S/52q12uA6UFwVKlSQ9cJQ0sVFf11T3SCLeu26deuWLDgt7jU7JiZGSnTj5+dnMH4LsOy6pf8er11kTfotMEq+k/qtRfrdv8rytUg/4FOSQl1/XVPjvnktMmS1lqjatWvD29sbCQkJuHjxosHA/SFDhkgJBSIiIvDmm2/iiy++kEWj8fHxGDRokNQ9RJvpj6wnKChIlr3l7NmzsolfTTl37pz0s52dndUyKjVp0gTLli1TtM2ff/6J7du3S8vjxo0rtBWsatWq8PDwkC6oly9fRm5ubqETxl24cEGWxtRYq5ESO3fulOrQtWtXk5OR2tnZSd+DlJQUo/2nk5OTZesTPWlOTk6Kv7/Xrl3DV199JS23bdvW7NNXGxsbNGzYUMrAlJiYiFu3bqFatWpm96NdT6t69epWH6+lVqsRFBQk/UHXDka35Oms/g2IqRTMQUFB0oMXoOBabEk63rNnzxqUU1T689kNGzbMaAp73ddSUlJMlqf7Hq9dZE1NmjTBhg0bpM9qVFQUunTpUuh2QghZUKRWqw3GSJXla5F+wPfgwYNC66V17949s2Vp8VpkyKp95bQf1NTUVFkfd6AgE5Fu6sHFixejTp06ePXVVzFnzhyMGTMGtWrVkk62SqVC3759OSGflQUFBck+XEePHi10roPIyEipCRcomBy5JNJzliSVSiVrDc3IyJAuhOboDkwEYFGqVFPi4uKkMUweHh7o3bu3yXV1L2Kmmph1WxFNXfSIngX64wIPHjxY6DZHjhyRdQ00NbawuPTnLLE0yYv+/G+mHkz5+fnJemREREQU2u0kPT0dZ86ckZbd3NyK9fT3xIkT0g1mUFCQyfEFbm5u0s+JiYkm0yDr1p/XLrImT09PWSKmS5cumeyepisiIkI2bicwMNBor5Oyei3STcgGAKdPn7ZoOyGE7FoBwORQB16LDFk1iNKmthVCYO3atQbvf//997LBtFFRUVi6dCk++eQTrFu3ThYRenp6YtGiRdasHqHgg6X75CA+Pl7WfVJfbm6uLIWkSqUye/NflnXv3l0WQG7dutXsxfX48eO4ceOGtBwUFFSs7qUbN26U+lwPHjzYbBYv3cQrpm7KdG/CLE3UQvQ0CgoKkj0VPnnypNlkKnFxcbLJf93d3UtsYtd69erJurmEh4cXOt70yJEjsqfe/v7+som29elec7XpeI2N/dTavHmzNPYVAHr06GFRpi5jMjMzsWXLFgAFT3eHDh1qct3y5ctLNyJ5eXkGD1OBgjFvJ06ckJZ57SJr69u3r/Rzbm4u1q9fb3b9rKwsbNiwQfaaqdarsnot0m/dOXz4MGJiYgrdbv/+/bh796607OzsbHSaIi1ei+SsGkT16dMHixYtwqJFiwyezgEFT9p27doFX19fkyddCIFKlSph7969BpE1WUevXr1kN/B//vkn9u3bZzCgOzU1Fd98841skGaLFi1QpUqVJ1ZXa/L09ETnzp2l5dTUVHz11VdISEgwWPfkyZOysXs2Njbo379/kfd98eJFKW1ozZo1ZSlFjdHNjHPmzBn8888/svf/+ecf2dOd4rSQEZV1KpVKNqG0EAJLly41+F4ABQO4Fy1aJHvy+MILL5jtF19cgwcPlrqv5+Xl4euvv5YlSNLKz8/HX3/9hXXr1sleHzhwoNkkSs2aNZP9Pfznn3/w888/G8y9kpubi40bN+LYsWPSa15eXmYTORXmzz//lB5wdu/evdD56HSvXdu3bzcYZ7Jz504p+ZRarUaTJk2KXDciY+rWrSubH+rMmTNYsWKF7GZeKz4+HosXL5b1tgkMDDTZWlRWr0XVq1eX5SbIzc3F4sWLcfbsWaP329nZ2fj9999l3fOAgvtDY93jtHgtklMJcyFkCUlLS8P333+P7du349q1a0hKSoKrqysaNGiAAQMGYOrUqYrSUpJyly9fxnfffSf7cvn6+qJu3bpwdnbGw4cPcenSJdmX39/fH++++26xEitYQ1HGRGnl5eVh0aJFsnTnNjY2aNSoESpUqICsrCxERUUhNjZWtt2YMWOK/PQoNzcX8+bNQ0JCAtRqNWbPnm1y/IOWRqPBggULpABWrVajadOmUnacc+fOSUFvxYoV8cEHHxT56Q7Rk6R0TJSuLVu2SJNQa9WoUQOBgYFQq9WIjY1FZGSk7LrWunVrTJw40Sp1N+fgwYMGT7yrVKmCmjVrwtHREcnJyfjnn38Mstf26NEDgwYNKrT8x48f45NPPpFl4XJyckJQUBA8PDyQkpKCK1euyMZK2tvb4+233y70emPKvXv3sGDBAuTn58PT0xMfffRRoTeAcXFxWLBggfS3w8nJCS1atICzszOioqJkrfudOnUqsbkB6fmWnp6Ozz77TBYcOTo6okGDBvDx8YFGo8Hdu3cRGRkpG9Lg6uqK2bNno3z58mbLL4vXokePHuGzzz6TXQMAwMfHB7Vr14aHhwc0Gg0ePnyIyMhIg0QK9evXx6uvvlrovQSvRf+nVIIoKhtOnDiBtWvXWpTFpXLlypg2bRq8vb2fQM3MK04QBRQE8cuWLbNobiW1Wo0BAwagR48eRaorAOzYsUOazyA4OBijRo2yaLvbt2/jq6++Mvr0TMvJyQkzZ84s8oWJ6EkrThCVn5+PTZs24a+//rJo/RYtWmD8+PFPLHnBvn378Pvvv1uUVlk77rdPnz4Wlx8TE4Nly5ZZNMefi4sLJk2aVKwkQIsWLUJkZCQAYMqUKRaP5Th69CjWrFljtptPpUqV8Pbbb1s8OTGRUgkJCVi2bJnFqcgrVKiAV199tdAWDqDsXovu37+P5cuXGySLKEyLFi0wZswYi7+PvBYVYBD1nHvw4AG2bt2Ky5cvG00woe2/27t3b7NNvE9ScYMo4P+61YSHhxvtzqdSqVC3bl3079/f4gw3xjx+/Bhz585FTk4OnJ2dsWDBAoNJNs2JjY3F6tWrpUnjdAUGBmLs2LGKZmQnKm3FCaK0rly5gu3bt8syXukKCAhA9+7d0aZNm+JUtUju3LmDbdu2ISIiwug1Va1Wo0GDBujbt2+R+uBnZmZi27ZtOHnypNGUvOXKlUOzZs0waNAgRXPF6Dt79iyWL18OoGCex5kzZyra/ty5c9iwYYPRFNOtWrXCiBEjFF0LiYpCo9Fg7969OHLkiMkbfg8PD3Tp0gWdO3dW3NWuLF6LcnNzceTIERw6dAgPHjwwuZ5KpUKdOnXQtWtXNG7cWPF+eC1iEEX/X1paGv79918kJiYiKysLbm5u8Pb2Rs2aNZ/pCY+FEIiOjkZcXBySk5NhZ2cHDw8PVKtWzWhacaX++ecfqetgjRo1ivwk5vbt27h16xbS09Ph7OyMatWqPbVj04is5eHDh7h9+zaSkpKQn58PDw8PBAQElIkHC9pralJSkvS99fDwQO3ata3SJTo3NxdRUVF49OgRUlNT4ezsDE9PT9SqVcsqrTtHjx6VxhC0atXK5HQM5mg0Gly/fh33799HTk4O3NzcULdu3UK7ShFZm/7fepVKBVdXV1SqVMkqPTnK6rUoMTER0dHRSE5ORmZmJtRqNRwdHeHj42MyA6FSz/O1iEEUERERERGRAs9uEwMREREREVEJYBBFRERERESkgKJMAU96LhqVSoWzZ88+0X0SERERERGZo2hMlFqthkqlMpsq0Bq0+1CpVEazGxEREREREZUWq+SsVqlU1iiGiIiIiIiozCtSEKVSqaQUiURERERERM+TIgVRQghkZmaicuXKGDduHIYNGwZXV1dr142IiIiIiKjMUTQmyt7eHrm5uQUb6nThc3BwwIABAzB27Fh0796d3fuIiIiIiOiZpSiISkxMxPr167Fq1SqcOnVKXtD/D5z8/PwwevRojBkzBo0aNbJubYmIiIiIiEqZonmiPD098fLLL+PEiRP4559/8O6776Jy5coACrr4CSHw4MEDfPnll2jSpAmaNWuGr7/+GvHx8SVSeSIiIqKSFBgYCJVKBZVKhfHjx1u9/OjoaKl8lUqFlStXWn0fz4qQkBDpPIWEhJR2deg5V+TJduvUqYNPP/0U0dHR2LdvH1588UU4OTkB+L+A6uLFi5g5cyYCAgLQt29fbN68GTk5OVarPNHTRP8PZVH/NWnSpLQPhYiIiOi5VuwU5yqVCl27dkXXrl2Rnp6OjRs3YvXq1Th06JA0n1ReXh527tyJnTt3wt3dHcOHD8fYsWPRtm3bYh8AERERET0dFi9ejKSkJABAkyZNMGDAgBLZz7x586SfQ0JC2HJFVmeVeaK0nJ2dMWHCBEyYMAG3b9/Gr7/+itWrVyMqKkoKqJKSkrB8+XIsX74cNWrUwLhx4zB69GhUrVrVmlUheirY2Ngo3sbW1qpfWyIioidm8eLFiImJAQCMGzeuxIKo+fPny5YZRJG1Fbk7X2GqVKmCOXPm4Pr16zh69CheeukleHh4APi/7n5RUVH48MMPUaNGDaxataqkqkJUJgUHByMvL0/xvzNnzpR21YmInhvR0dHSfQvHK5WugwcPSr+LgwcPlnZ16DlXYkGUrnbt2mHZsmW4f/8+1q9fj169esHGxgYqlUr6MmibdomIiIiIiMqyJ9ovyN7eHv369UNOTg4ePHiA8+fPP8ndExERERERFdsTC6LCw8OxatUq/Pbbb0hPTwcAWUsUERERERHR06BEg6hr165h1apVWLNmDWJjYwHAIGBq0qQJxo4di9GjR5dkVYieexqNBkePHsW///6LuLg4uLm5oXr16ggODpamJ3ieZGdn49ChQ4iJiUF8fDw8PDzQsmVLtGzZ0ux2cXFxOHLkCKKjo5GXlwd/f3+EhIQUOzlObm4uTpw4gX///Rfx8fFQqVTw8fFBo0aN0LRpU2lC85KWlpaGc+fOITIyEklJScjJyYGTkxO8vb1RrVo1NG7cGO7u7kUqOz4+HseOHcP9+/fx+PFjuLm5oUKFCujQoQMqVKhQrHrfuHEDJ0+exL179+Dk5ISAgAC0a9cOfn5+xSr3SUtJScHRo0dx9+5dJCQkwMnJCX5+fmjTpg0CAwOttp/Y2FicOHECsbGxyM3Nha+vL9q0aYM6deoUu+xbt27hzJkziI+PR2JiIhwdHVGhQgU0aNAAjRo1glqtfCTBkzov1vbo0SOEh4cjNjYWGo0GAQEBCAoKQr169ay6H+3v8+HDh0hKSoKnpycCAgLQsWNHeHp6Wm0/ly5dwuXLl3H37l2UK1cOFStWREhICHx9fa22D6KnhrCyx48fi++++060bt1aqNVqoVarhUqlkv3z9/cXb731lrh8+bK1d09UZt26dUsAkP4FBwdbfR9Vq1aVyh83bpwQQgiNRiM+++wzUbFiRdn+tf8cHBzE22+/LdLS0syWPW7cOKPbK/mnlZGRIcqXLy+9HhoaqvhYV6xYISt78+bNFp2PlJQUMXPmTOHp6Wm0jo0bNxbHjh0zKCs6OloMHTpU2NjYGN3uhRdeELdv31Z8HDdv3hRjx44Vrq6uJs+br6+v+OSTT0R6erri8i1148YNMXLkSOHg4GD2d6hSqUSjRo3EggULLC5727ZtonXr1kKlUpkst02bNmLPnj2K6338+HHRqlUro2Xa2tqK/v37i1u3bgkhDD8z2tf16X9XV6xYYXF9jH3mLHH48GHRtWtXYWtra/IcNWjQQISFhYn8/PxCyzN1rFevXhU9e/YUarXa6D6aNWsmDh48aHG9tdLT08UXX3whatSoYfbzU758eTFu3Dhx5syZUjkvRVGU32lsbKwYOnSoyXq3atVK7N+/XwhR9M+bRqMRK1euFA0bNjR5bmxsbET37t3FyZMnLSpT9zpftWpV6fVt27aJoKAgk9eEQYMGiZs3b5osd+7cuYr/Xhj7GxkcHGz2/aL8ndL+TmNjY2W/r8mTJ1t0zswdp6Wfc3o6WSWIys3NFVu3bhUDBw4U9vb2RoMnR0dHMXLkSLFr1y6h0WissVuip0ppBFGJiYkiJCTEoj8kbdq0EcnJySbLtmYQJYQQb775puyP8I0bNxQda+vWraXt/f39RW5ubqHnIzY2VtStW7fQetrb24vdu3dL5Rw+fFh4eHgUul3lypVN3pgbs2jRIlGuXDmLz1+dOnVEVFSUovNkia1btwp7e3tFv0t7e/tCy01MTBTdu3dXVO60adOM/i6NWbRokclgQPefp6enOHDgQJkMorKyssSYMWMUnaP+/fsXGlAbO9Z169YJJyenQsu3tbUVa9eutfiYjx07ZvIhjbljKI3zUhRKf6dHjhwRbm5uhdZXrVaLhQsXFunzdufOHdGsWTOLz41KpRL//e9/Cy3XWBA1Y8YMi/bh6+srLl26ZLTcpyGIEkKIwYMHS687Ozub/ZuoLy8vT1SqVEnavkWLFhZvS0+nYnXnO336NFatWoWwsDA8fvwYAGTd9VQqFdq3b49x48Zh2LBhcHV1Lc7uiEiBvLw8DBs2TEoDW7NmTYSEhKBChQrIzMzEyZMncfToUWn9EydOYObMmfjpp5+MlmdjY6N4XiuNRmPyvenTp+Orr76SxkUuW7YMCxcutKjcixcv4uTJk9LypEmTCp0/Kzs7G3369EFkZCQAoFmzZmjfvj08PDzw4MED7Ny5E3fv3pXWHTVqFK5fv46HDx+iT58+SE1NhZ2dHTp16oSgoCA4OTkhKioK27dvR1paGgDgzp07mDBhAsLDwws9hrfffhtffPGF7LW6deuibdu28Pf3B1CQWnn//v14+PAhgIIu0h06dMC5c+ekdYrr2rVrGDFiBLKzs6XX/P390bFjRwQGBsLJyQkZGRl4+PAhrly5gosXLyI3N7fQcuPj4xEcHIx//vlHeq1cuXJo3749GjVqBA8PD6SlpeHixYs4dOgQ8vLyAAA//PADMjIy8Ouvv5ot/+eff8Ybb7whe83Hxwe9evVClSpVkJKSguPHj+P06dNITEzE0KFDMWPGDAVnpuRlZWWhe/fuOHLkiPSaWq1G69at0bRpU3h5eSErKwsRERH466+/kJmZCQDYtm0bXnjhBezbt8/i7+SBAwcwbdo05OXlwcHBAZ07d0a9evXg7OyMmJgY7Ny5EwkJCQAKrh2TJ09GixYtULt2bbPl/vnnnxg6dKjs86NWq9G8eXO0aNEC3t7eyMrKwt27d3H69GncuHGjTJ0Xa7tw4QJ69+6N1NRU6TUHBwf06NEDdevWhUajwdWrV7F//37k5ubinXfegYODg6J9XL9+HZ07d8a9e/ek15ydndGhQwfUr18frq6uSE5OxunTp3H8+HHpGvvBBx8gJyfHYP4kc+bPn4/FixcDALy9vREaGorAwECoVCpERERg9+7d0u/+4cOHGDFiBM6dOwd7e3tZOWq1Wvqd6P5dUKlUJrt3FuV3qPt3qij7eeWVV/Dbb78BANLT07F69Wq88sorFu37zz//lIauAMC0adMU15+eMkqjrjt37ohPPvlE1KtXz2R3vRo1aoh58+aZbdolet486ZYobQuHl5eX2LJli9H19+3bJ3tiqlKprNbS8cMPP8iONygoyGCdXr16Se97e3uLrKwsi8qeNm2a7GluTEyM0fV0z4ednZ0AICpVqmS0u1JmZqYYPXq0rM6zZs0STZs2FQBEp06djJ6bu3fvisaNG8u227dvn9n6r1271uDcHD161Oi6WVlZ4uOPP5Z1I+zcubMFZ8kyEyZMkJ3LJUuWiLy8PJPrJycni3Xr1omuXbuaXCc/P1/06NFD9rmaPn26iIuLM7r+zZs3RefOnWXn5NdffzVZ/q1bt4Szs7Ns/ffee09kZ2cbrBseHi61kui3tpV2S9TkyZNl+xkyZIjJOsXFxYnhw4fL1p8/f77JsvVborTXgxEjRogHDx4YrJ+ammpQ/pgxY8zWPzIy0qDFpX///mavIdeuXRPvvfeeGD9+vMl1SvK8FIWlv9Pc3FyDLm+9e/c2er5v3bol2rdvb/Rzae7zlpmZKbve2NnZiTlz5oikpCSj61+8eFFWJ7VaLcLDw02Wr9uSY2dnJ1QqlVCr1eLjjz82en2+efOmaNCggaz+P//8s8nyhSh6l1chCm+J0qVbp7lz51q8j/r160vbNWzY0OLtevbsKW3n7u5eot2vqWxQFER17dpV2NjYGA2cPDw8xEsvvSSOHDlSUnUleqrp35gBBf3Vlf67cOGCyX3o/nECIFxcXMSVK1fM1mvVqlVF/mNjytatW2U3/VWrVhX37t0zWG/79u2yfa9Zs6bQslNTU2Xjh1544QWT6+qfDw8PD7MPd7KyskRgYKDshgOAaN26tdkALyIiQtatzNyNQVJSkuzGs0OHDhb9sV22bJnsWA4cOFDoNpaoXLmyVOakSZOsUqb+Dfw333xT6DY5OTmiU6dO0jbVqlUzGcyNGjVKVv4HH3xgtuyIiAijY85KM4gKDw+X7WPmzJkWla977K6uriIxMdHoevq/AwBi4sSJZsvOzs6WdXd1dHQUGRkZJtfv0qWLrPzXXnvN4nFJptYr6fNSFJb+TpcvXy6re5cuXUROTo7J9VNTU0WTJk0Mfk/mPm+63eLUarX4/fffC61/UlKS7PfasWNHk+sa6w5n7oGGEAXjKXUDwcKCm7IeRC1ZskS2rakHXLpu3bol+xvw6quvWrw/enopCqL0AydbW1vRp08fsWHDBoufIBM9r4wFUUX5d/78eZP70A8avvrqq0LrlZeXJypUqCBt071792Id55EjR2TJCby8vERkZKTRdTUajahWrZosoCiMfgvX9u3bTa6rfz6+++67QsufN2+ebBuVSlVoICqE/I977dq1Ta736aefSus5ODiI6OjoQsvWatOmjbTtkCFDLN7OHN0xWUuXLi12efn5+aJevXpSmb169bJ424iICFnyCWO/2/j4eNkNW506dczeqGp98cUXZSqI0m2pa9SokcXjwOLj42WtcEuWLDG6nn4QVaFCBbMBkZb+DaSxJCtCCHHy5EnZeq1atbL4GMwp6fNSFJb+TnXHKJUrV86iVv3Tp09bHESlp6cLLy8vab3p06dbfAy7du2S7cPUNU0/iOrXr59F5Q8ZMkTaxsnJyWxrdlkPolJSUmQPXUaPHl3oNrNmzbLo/NKzRfGYKO3cTs7Ozujbty8qVKiAEydO4MSJE0qLsshXX31VIuUSPevKlSuHl156qdD1bGxs0L59e6kfuO4YFqUiIiLQr18/ZGVlAQCcnJywfft2k2mT1Wo1pk2bhnfffRcAcPToUVy9ehUNGjQwuY9ly5ZJP1etWhW9evWyqG4ODg4YP358oeu1bt1attyxY0ez9dFq06YNDh06BKAg3XZubi7s7OwM1lu1apX0c//+/RWlRh82bJh0rT148CCEEMVOfe7i4iKNabXGBOinT5+WfYZef/11i7etV68eGjVqhEuXLgEomF+wT58+snV27twpG38zffp0o+dZ35QpUzBnzhxp/ExpiouLw549e6Tll19+udAxfVre3t7o2rUr/vjjDwAF58iSMRsTJ06Eo6NjoesFBwfLlv/55x+0bdvWYL3169fLlj/88EOLj8GU0jgv1nL79m2cO3dOWu7duzdq1KhR6HYtWrRA+/bt8ffffxe67vbt2/Ho0SNpWcl3q3v37vD09ERiYiKAgvNjyXXt1Vdftaj84OBgbN68GQCQkZGBmJgYVK9e3eL6lSWurq4YPXo0li5dCgDYvHkzFi9eDC8vL6Pr5+bmYsWKFdKypX8z6OlX5CteRkYGNmzYYM26GMUgip5VwcHBUtKHktC8eXO4uLhYtK7uPCvaP7JK3blzBz169JC2t7GxwYYNG9CmTRuz202aNAlz586VAq8ffvgB3377rdF1T506JbvRnzJlisVzzjRr1syi+bAqVaokW+7QoYNF5etuJ4RASkqKwR/d+Ph4WYDRsWNHi8rWqlWrlvRzQkIC7t69a1BfpVq1aoXdu3cDAH766Sc0bNgQU6dORbly5YpUnjaQBAoeull6/rRq1aolBVEXLlwweF//gd0LL7xgUbmurq4ICQnBrl27FNWnJBw+fFi2XJzPgbFzZIx+cGSK/pxLpq4Hutcud3d3ix9mmFMa58Vaivq5BIB+/fpZFETpfre8vb1Rt25di/ehVqtRvXp1nD17FoBl50f7gM0Sln5unhavvPKKFERlZWVh5cqVePPNN42uu3XrVsTFxUnLTCjx/FA+4x3wRCZ9FHqT8hKRMgEBARav6+zsLP2szTSnRGJiInr27CnLTLRs2TKLbiS8vLwwfPhwaXn16tXIyMgwuu4PP/wg/WxnZ4dJkyZZXEdLz4fuuSjOdsbOo35Lz+uvvw5bW1uL//Xv31+2ve5T6aLSzViXn5+P119/HQEBAZgwYQJWr16NmzdvKipP9xiFEPDw8FB0jFu2bDF7fLpBqIuLi6Kn3U2aNFF0LCVF/3MQFBSk6BzpPly09DNgzc8xAFy9elX6uVWrVkWaQFdfaZwXa9FvwQ8KCrJ4W0s/l7rnJyEhQdG5sbW1lQIowLLz4+npafFE7JZ+bp4WDRo0kD14WLZsmcn7Ut2/S97e3hg8eHCJ14/KBsVXPfH/U2WW9D8iKh79P2rmFOfBSGZmJvr27YuIiAjptQULFigKcHS73SQnJxt0FdK+rtv6PWDAAPj5+Vm8D0tvBvTPRVG3M3Yd06aQ1srPz4dGo7H4X35+vmz75ORki+pmTo8ePfDZZ5/JboITEhKwcuVKjB07FjVq1IC/vz9GjhyJdevWIT093Wx5+seo5Pg0Go3svBk7Pt0n3D4+Poo+u0o+LyWppM+RMZZeDyz5HKekpMjS3FesWNGisgtTGufFWvRbXnx9fS3e1tLPZXHPjy5Lzk9x/oY8C/dxun+Xbty4gb/++stgnevXr8umtJgwYYJBend6dinqznfr1q2SqgcRPYU0Gg1Gjhwp64oybdo0fPDBB4rKadmyJVq2bInTp08DKHiypx+ErVq1StZC9TR2mUhKSrJqefpBVVG98847CA4OxoIFC7B7926DG64HDx4gLCwMYWFh8PT0xNtvv423337b6HgVax6jsePTfcKt5CavKOuXFGueo9K4WdWdAwmAxd2GC/M0nxf9lhclnzVL1y3p7xbJDRw4EBUrVpTm4/rhhx/QtWtX2Tq6Y3RVKhWmTp36ROtIpUtREKVkADQRPftefvllbNu2TVoeOHAgvvvuuyKV9corr0iJH86cOYOzZ8+iefPm0vu6f6xq166NLl26FK3SpUi/VWvfvn0IDQ0tpdrItW7dGtu3b0d8fDwOHDiAo0eP4tixY7h48aLshisxMRGzZ89GeHg4tm/fbjB2SvcYAwICZF08rUH3hr2wVjF9StcvKbrnSK1WIzMzs8hj0EqDm5ubbNlaXbee5vOiH0imp6ebTESgz9LPpe75ad++vWyydLI+W1tbTJkyBfPmzQNQMJnzgwcPUKFCBQAFY6V0JwUPDQ21KJkIPTuK34mZiJ5L8+bNw/Lly6Xljh07Yt26dUUeGzF8+HDZTYduP/MjR47IxmA8rU/7vL29Zcv//vtvKdXENB8fH4wYMQJLlizBuXPnkJiYiE2bNmHgwIGy3+2+ffvw+eefG2yve4z37t2TEoZYi6enp/RzfHy8ohYH3cHf5hSna5IlAYXuOcrPz3/qenm4urrKgpu7d+9apdyn+bzofi4B4OHDhxZva+nnUvf8lMVrx7NoypQpUvbP3Nxc/Pzzz9J7mzZtko0texp7R1DxMIgiIsWWL1+O+fPnS8sNGjTAtm3b4ODgUOQyHRwcZF340t+RgQAAEe1JREFU1q9fj5SUFADygMrSVOVlUf369WXLutm2yio3NzcMGTIEW7Zswc6dO2Vd+H788UeD9XWPUQhhkHGtuOrVqyf9nJaWpijxxcWLFy1aT797lalEJ/oyMzMt6nL1NH4O9DVs2FD6+fTp01bpHvY0nxfdzyVg+WdNybq65+fBgwe4fv26xfugovH398fAgQOl5R9//FH6rOv+XapYsSL69ev3xOtHpYtBFBEpsm3bNrz88svScqVKlbB7926DJ7FFMW3aNKm1Iz09HatXr8ajR4+kOayAgrmSypcvX+x9lYbq1aujWrVq0vLOnTuf+AD44ujRo4csQ+Dt27cNggb9MQPr1q2zah30U+bv2LHDou1SU1MtnlLA3d1d1hoVExNj0XZ///23wXgyY/S7cFr7HD0JISEh0s/JycnYuXNnsct8ms9LUT+XAKS5rQpT0t+tJ0V3XjdLvi9FpfvApzj70f17FxMTg127duHKlSs4duyY9PrkyZOLPU8aPX0YRBGRxY4ePYoRI0ZIf5A8PT2xe/fuYs9VpFWtWjXZfDPLli3DihUrZJOrPu1dJkaOHCn9nJycjE8//bQUa6Ocfp//nJwc2XL79u1l42fXrl2LK1euWG3/vXv3lnUlW7p0KfLy8grd7scff7S4RcnOzk4255ClLSLff/+9RetVqVIF7dq1k5WvnavraTFq1CjZ8oIFCyz6PZjzNJ+XKlWqoGnTptLyjh07LGolPXv2rEVzRAFA37594erqKi1//fXXePDggfLKljLdYyjJ+aSstZ/g4GBZy+sPP/wga4WysbGxaGJ7evYwiCIii0RERKBfv37SGBcHBwf88ccfVp+ZXTet7OXLl/Hf//5XWm7cuDHatm1r1f09aTNnzpT9cf/8888VT1weFxdnlcAkNTXVYH6bwhw5ckT62cnJCT4+PrL3bW1tMXv2bGk5Ly8PAwcOlDJcWero0aOy4FlLfx6WyMhI2WfEmGvXrsm6n1pC92b+1KlTOH78uNn1w8LCsHXrVovLnzt3rmx5zJgxsmkCLHH58mXEx8cr2sZamjdvjm7duknLp06dwptvvmnx+DFT6z3N50X3AU9OTg6mTp1qNrBMT0/HlClTLD5n5cuXx6uvviotJyUlYdCgQYpbsw8cOKBofWvTnZj33LlzJZYpUHc/2syvRaXbGrVz505ZQok+ffpY7UEiPV0YRBFRoVJTU9GjRw/paZ5arca6devQoUMHq++rZ8+estYO3RuEp70VCiiYXFg3g6EQAiNHjsSMGTPMDjDXaDT466+/8NJLLyEwMBD79+8vdl0ePXqEBg0aoGfPnli7dq00Bs2Y1NRUTJ8+XRZMDBw40Og8TZMmTZLdYEdFRaFZs2ZYu3at2W41iYmJ+OWXX9CxY0d07NgRmZmZRtf7+OOPZeOW5s+fjw8++MCgVQwADh8+jNDQUKSkpCiav2Xs2LGy5eHDh8uSm2jl5OTgyy+/xJgxY6BSqWRdlczp3r07Jk6cKC0nJCSgTZs2WLJkidlkHOnp6diwYQN69+6Nxo0bWy2pQ1F8//33skx933zzDQYNGmQ26UFUVBTef/992bHreprPy4QJE2ST7O7fvx8DBw40mmQiJiYGvXr1wrlz5xR9Lt9//300btxYWj5+/DhatmyJ7du3mw3G7t+/j2+//RZBQUGlPhms7oOw+/fv45VXXimRJCK6+zl16hTmz5+P+/fvF6msMWPGSJ/1/Px8WQKZZ+HvEhUNO3ASlZJDhw4VuQ/1v//++0SnHHj06JEsVXV+fj6GDh2quBxLuvuoVCpMnz4db731lux1FxcXjB49WvE+y6IxY8bgxo0bWLBgAYCCQOrrr7/G999/j1atWqFJkybw8vJCTk4OEhMTERkZifPnz5sNcopKCIE9e/Zgz549sLOzQ4MGDRAUFARfX184OzsjPT0d165dQ3h4uGx+IDc3N3z88cdGy7SxscGGDRsQEhKCS5cuAShoPRs9ejRmzJiB4OBgBAYGwsXFBampqXj48CEuXLiAf/75x6KxC9WqVcPXX3+NyZMnS699/PHH+PHHH9GrVy9UrlwZqampOH78OE6dOgWg4Cn+jBkz8OGHH1p0Xjp37oxu3bph3759AIA7d+6gSZMm6N69Oxo3bgy1Wo2YmBjs379fCn4//PBD/PrrrxaPoVq6dClu374tBcSpqal47bXX8P7776Njx46oXbs23NzckJ6ejoSEBFy+fBmXL182GiyWhpo1a2LNmjUYMmSIVKfff/8df/zxB1q2bIkWLVrAy8sL2dnZuHv3Ls6ePSu1fOqOrdP3tJ4XOzs7rFy5Eh07dpRusrdv347AwED07NkTderUgUajwdWrV7Fv3z5pwuIvvvgCr732mkX7cHZ2xrZt29CpUyfcuXMHQMFEsH379kVAQAA6deqESpUqwcnJCcnJybh//z7Onz+PGzduSEGWu7t7CRy95UaPHo25c+dKD0m03eNsbW1lAWXHjh2xa9euIu9n0qRJ+OGHH6SWrnnz5mHevHmws7OTdQkePXq0rHueMS4uLhg7diyWLFkiez0wMBA9evQoch3pKSeI6Im4deuWAGCVf7du3TK6j6pVq0rrjBs3zuK6zZ07V1Z+SdXdUo8fPxaOjo6ybadMmWLx9lpFOR/6x7pixQqLtluxYoVFvyNdq1evFs7OzkU6l99//71F9TKnqL9XPz8/cerUqULLT0tLE6NGjSrSPmxtbUVKSorZ8r/66iuhVqsLLcvDw0Ps379f8e8oNjZW1K5d26L6vvzyyyI/P1/xZy43N1e88cYbQqVSKT5HKpVKXLlyxWi5Rfk8auluN3fu3ELXP3LkiPDz81NU9/79+5fKeSkKpb/Tw4cPCzc3t0LrqVarxWeffVaka87Dhw9FaGhokb5bvr6+JssdN26ctF7VqlUtPkfh4eGyfYSHh5tdPywsTDg4OJitZ3BwsMF2wcHBZt/X9+WXXxZ6jbD0b0NERITBtp988olF29Kzid35iKjM8fT0RO/evWWvPYtdJkaPHo3o6GjMnj0bAQEBha5fq1YtvPbaazhx4gSmT59e7P0HBARg06ZNGDt2rEV9+itWrIj33nsP165dQ8uWLQtd39nZGWvXrsXx48cxYMAAODo6ml2/XLlyCA4OxpdffonY2FjZ2DFj3njjDRw9ehStWrUy+r6NjQ369OmDM2fOGGQ2s0RAQAD+/vtvTJo0CTY2NkbXqVu3LjZs2IDvvvvOaNfGwtja2uKrr77ClStXZF2GTLGxsUGrVq3w0Ucf4ebNm1Yfk1gUHTp0wI0bN/DRRx8V+jny8fHBSy+9hI8++sjsek/zeenYsSOuXr2KIUOGmOxt0LRpU+zevRvvvPNOkfbh4+ODffv2YdeuXejatWuh3UidnJzQo0cPLFu2rEykRtd2j509ezY6dOgAX19fRd0aLTVz5kycP38eM2bMQKtWreDl5VXkCZzr1asnmwDezs7OZLdUej6ohFAwiyAR0ROQl5eHqlWrSskIWrdujRMnTpRyrUretWvXcOHCBSQkJCApKQn29vZwd3dH9erV0aBBA1SoUKFE9x8bG4uIiAhER0cjKSkJOTk5cHFxgZ+fHxo1aoT69esXeTJlAMjOzsbJkydx8+ZNPHr0CFlZWXBxcYGPjw9q166NBg0aFBpomXL9+nWcOHEC9+/fh5OTEypWrIj27dvLztnKlSsxYcIEafnWrVuywefmJCYm4uDBg7h9+zYyMzNRsWJF1K9fHy1atChSfU3RaDQ4e/Ysrl27hkePHiEtLQ3Ozs7w8vJCrVq10KBBg0IDitIWERGBCxcuID4+HqmpqXB2dkbFihXRoEEDNGjQoEjB5tN6XhISEhAeHo47d+4gPz8fAQEBCAoKMpgTq7jS09Nx/Phx3L59G48ePUJubi5cXFxQoUIF1KlTB/Xq1Sty8EAFEhMTUbFiRWls3vDhwxEWFlbKtaLSxCCKiMqcrVu3YtCgQdLyihUrntoJdqnsKE4QRUTPt0WLFmHmzJnScnh4uGy+NHr+sDsfEZU5X3/9tfSzt7c3RowYUYq1ISKi55lGo5FlVW3QoAEDKGIQRURly+7du2WTm06dOhUODg6lWCMiInqe/fjjj7LU/TNmzCi9ylCZwRTnRFQmpKenY8eOHbLJdt3c3GTdJ4iIiJ6U5ORkhIWFyf4OVatWDePGjSvFWlFZwSCKiErNkSNH0KtXLwAFQZS+jz/+GOXLl3/S1SIioufU2rVrMXXqVAghkJGRYfD+119/bfGk2vRsYxBFRKVGo9EYDZ4AYOzYsbJWKSIiopKWm5tr8u/SnDlz0Ldv3ydcIyqrGEQRUZmgUqng4eGBpk2b4qWXXmIyCSIiKlVqtRpeXl5o3bo1Xn31VfTo0aO0q0RlCFOcExERERERKcDsfERERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISAEGUURERERERAowiCIiIiIiIlKAQRQREREREZECDKKIiIiIiIgUYBBFRERERESkAIMoIiIiIiIiBRhEERERERERKcAgioiIiIiISIH/B4OHaeLpJt6TAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] @@ -1211,247 +1238,244 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 19, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "2291" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "1.1891218873731868e-21\n", + "0.038216075117062855\n" + ] } ], "source": [ - "len(set(list(df_test[\"Uniprot ID\"])))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting the results of hyperparameter optimization ESM-1b and ESM-1b_ts:\n", - "Boxplots for 5-fold CV (accuracy) and test set" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "accuracy_CV_ESM1b_ECFP = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ECFP.npy\"))\n", - "ROC_AUC_CV_ESM1b_ECFP = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ECFP.npy\"))\n", + "df_test_40 = df_test.loc[df_test[\"identity\"] == \"<40%\"]\n", + "error_40 = abs(df_test_40[\"Binding\"] - df_test_40[\"pred\"])\n", + "#plt.hist(error_40)\n", + "#plt.show()\n", "\n", - "accuracy_CV_ESM1b_ts_ECFP = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ts_ECFP.npy\"))\n", - "ROC_AUC_CV_ESM1b_ts_ECFP = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ts_ECFP.npy\"))\n", + "df_test_60 = df_test.loc[df_test[\"identity\"] == \"40-60%\"]\n", + "error_60 = abs(df_test_60[\"Binding\"] - df_test_60[\"pred\"])\n", + "#plt.hist(error_60)\n", + "#plt.show()\n", + "\n", + "df_test_80 = df_test.loc[df_test[\"identity\"] == \"60-80%\"]\n", + "error_80 = abs(df_test_80[\"Binding\"] - df_test_80[\"pred\"])\n", + "#plt.hist(error_80)\n", + "#plt.show()\n", "\n", - "accuracy_CV_ESM1b_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ECFP.npy\"))\n", - "ROC_AUC_CV_ESM1b_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ECFP.npy\"))\n", + "from scipy.stats import mannwhitneyu\n", "\n", - "accuracy_CV_ESM1b_ts_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ts_GNN.npy\"))\n", - "ROC_AUC_CV_ESM1b_ts_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ts_GNN.npy\"))\n" + "U1, p = mannwhitneyu(error_40, error_60)\n", + "print(p)\n", + "\n", + "U1, p = mannwhitneyu(error_60, error_80)\n", + "print(p)" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.662319397108857e-19\n", + "0.0847863743066931\n" + ] + } + ], "source": [ - "y_test_pred_esm1b_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ECFP.npy\"))\n", - "test_y_esm1b_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ECFP.npy\"))\n", - "\n", - "y_test_pred_esm1b_ts_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_ECFP.npy\"))\n", - "test_y_esm1b_ts_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_ECFP.npy\"))\n", - "\n", - "y_test_pred_esm1b_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_GNN.npy\"))\n", - "test_y_esm1b_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_GNN.npy\"))\n", + "df_test[\"sub_train_count\"] = np.nan\n", "\n", - "y_test_pred_esm1b_ts_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_GNN.npy\"))\n", - "test_y_esm1b_ts_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_GNN.npy\"))\n", + "for ind in df_test.index:\n", + " df_test[\"sub_train_count\"][ind] = len(df_train.loc[df_train[\"Binding\"] == 1].loc[df_train[\"ECFP\"] == df_test[\"ECFP\"][ind]])\n", + " \n", + "sub_count = np.array(df_test[\"sub_train_count\"])\n", "\n", + "df_help = df_test.loc[df_test[\"sub_train_count\"]>=1]\n", "\n", + "df_test_40 = df_help.loc[df_help[\"identity\"] == \"<40%\"]\n", + "error_40 = abs(df_test_40[\"Binding\"] - df_test_40[\"pred\"])\n", + "#plt.hist(error_40)\n", + "#plt.show()\n", "\n", + "df_test_60 = df_help.loc[df_help[\"identity\"] == \"40-60%\"]\n", + "error_60 = abs(df_test_60[\"Binding\"] - df_test_60[\"pred\"])\n", + "#plt.hist(error_60)\n", + "#plt.show()\n", "\n", - "acc_esm1b_ecfp_test = np.mean(np.round(y_test_pred_esm1b_ecfp) == test_y_esm1b_ecfp)\n", - "acc_esm1b_ts_ecfp_test = np.mean(np.round(y_test_pred_esm1b_ts_ecfp) == test_y_esm1b_ts_ecfp)\n", + "df_test_80 = df_help.loc[df_help[\"identity\"] == \"60-80%\"]\n", + "error_80 = abs(df_test_80[\"Binding\"] - df_test_80[\"pred\"])\n", + "#plt.hist(error_80)\n", + "#plt.show()\n", "\n", - "roc_auc_esm1b_ecfp_test = roc_auc_score(test_y_esm1b_ecfp, y_test_pred_esm1b_ecfp)\n", - "roc_auc_esm1b_ts_ecfp_test = roc_auc_score(test_y_esm1b_ts_ecfp, y_test_pred_esm1b_ts_ecfp)\n", + "from scipy.stats import mannwhitneyu\n", "\n", - "acc_esm1b_GNN_test = np.mean(np.round(y_test_pred_esm1b_GNN) == test_y_esm1b_GNN)\n", - "acc_esm1b_ts_GNN_test = np.mean(np.round(y_test_pred_esm1b_ts_GNN) == test_y_esm1b_ts_GNN)\n", + "U1, p = mannwhitneyu(error_40, error_60)\n", + "print(p)\n", "\n", - "roc_auc_esm1b_GNN_test = roc_auc_score(test_y_esm1b_GNN, y_test_pred_esm1b_GNN)\n", - "roc_auc_esm1b_ts_GNN_test = roc_auc_score(test_y_esm1b_ts_GNN, y_test_pred_esm1b_ts_GNN)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (a) Accuracy:" + "U1, p = mannwhitneyu(error_60, error_80)\n", + "print(p)" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n", "text/plain": [ - "(0.8732035928143712, 0.9051646706586827)" + "
" ] }, - "execution_count": 61, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "acc_esm1b_ecfp_test, acc_esm1b_GNN_test" + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "splits = [\"<40%\", \"40-60%\", \"60-80%\"]\n", + "colors = [\"black\", \"steelblue\", \"firebrick\"]\n", + "\n", + "for i, split in enumerate(splits):\n", + " \n", + " help_df = df_test.loc[df_test[\"identity\"]== split]\n", + " y_true = np.array(help_df[\"Binding\"])\n", + " y_pred = np.array(help_df[\"pred\"])\n", + " \n", + " \n", + " fpr_esm1b_ecfp, tpr_esm1b_ecfp, threshold = metrics.roc_curve(y_true, y_pred)\n", + " roc_auc_esm1b_ecfp = metrics.auc(fpr_esm1b_ecfp, tpr_esm1b_ecfp)\n", + " \n", + " plt.plot(fpr_esm1b_ecfp, tpr_esm1b_ecfp, colors[i],\n", + " label = 'Sequence identity level = %s' % split, linewidth=3.0)\n", + " \n", + "\n", + "\n", + "\n", + "plt.legend(loc = \"lower right\", fontsize=18)\n", + "plt.plot([0, 1], [0, 1],'--')\n", + "eps = 0.01\n", + "plt.xlim([0-eps, 1+eps])\n", + "plt.ylim([0-eps, 1+eps])\n", + "plt.ylabel('True Positive Rate')\n", + "plt.xlabel('False Positive Rate')\n", + "ax.yaxis.set_label_coords(-0.16, 0.5)\n", + "ax.xaxis.set_label_coords(0.5,-0.1)\n", + "\n", + "ax.locator_params(axis=\"y\", nbins=5)\n", + "ax.locator_params(axis=\"x\", nbins=5)\n", + "\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.9063622754491018, 0.9028443113772455)" + "2286" ] }, - "execution_count": 62, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "acc_esm1b_ts_ecfp_test, acc_esm1b_ts_GNN_test" + "len(set(list(df_test[\"Uniprot ID\"])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting the results of hyperparameter optimization ESM-1b and ESM-1b_ts:\n", + "Boxplots for 5-fold CV (accuracy) and test set" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 23, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "fig, ax = plt.subplots(figsize= (10,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "labs = [\"ESM-1b/ \\nECFP\", \"ESM-1b/ \\nGNN\", \"ESM-$1b_{ts}$/ \\nECFP\", \"ESM-$1b_{ts}$/ \\nGNN\"]\n", - "\n", - "\n", - "plt.scatter(1, acc_esm1b_ecfp_test, c='blue', marker='^', linewidths= 8)\n", - "plt.scatter(3, acc_esm1b_ts_ecfp_test , c='blue', marker='^', linewidths= 8, label = \"test set\")\n", - "plt.scatter(2, acc_esm1b_GNN_test, c='blue', marker='^', linewidths= 8)\n", - "plt.scatter(4, acc_esm1b_ts_GNN_test , c='blue', marker='^', linewidths= 8)\n", - "\n", - "\n", - "plt.boxplot([accuracy_CV_ESM1b_ECFP, accuracy_CV_ESM1b_GNN,\n", - " accuracy_CV_ESM1b_ts_ECFP, accuracy_CV_ESM1b_ts_GNN], positions=[1,2,3,4], widths=0.6, whis =2)\n", - "\n", - "plt.ylim(0.75, 1)\n", - "ticks1 = [1,2,3,4]\n", + "accuracy_CV_ESM1b_ECFP = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ECFP.npy\"))\n", + "ROC_AUC_CV_ESM1b_ECFP = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ECFP.npy\"))\n", "\n", - "ax.set_xticks(ticks1)\n", - "ax.set_xticklabels([])\n", - "ax.tick_params(axis='x', which=\"major\", length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "ticks2 = [0.99, 1.99,2.99,3.99]\n", + "accuracy_CV_ESM1b_ts_ECFP = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ts_ECFP.npy\"))\n", + "ROC_AUC_CV_ESM1b_ts_ECFP = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ts_ECFP.npy\"))\n", "\n", - "ax.set_xticks(ticks2, minor=True)\n", - "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", - "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 0)\n", + "accuracy_CV_ESM1b_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_GNN_pretrained.npy\"))\n", + "ROC_AUC_CV_ESM1b_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_GNN_pretrained.npy\"))\n", "\n", - "plt.ylabel(\"Accuracy\")\n", - "ax.yaxis.set_label_coords(-0.15, 0.5)\n", - "plt.legend(loc = \"upper right\")\n", - "plt.show()" + "accuracy_CV_ESM1b_ts_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"acc_CV_xgboost_ESM1b_ts_GNN_pretrained.npy\"))\n", + "ROC_AUC_CV_ESM1b_ts_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"ROC_AUC_CV_xgboost_ESM1b_ts_GNN_pretrained.npy\"))\n" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 24, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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mzZrlttLWRx99pA0bNqiiosKtX35+vl555RVlZGSY711zzTWKi4vza307d+50W6Tp6quvbvDKeKWlpXrqqaf01ltv6cCBA3WOSl+8eFErV67UX//6V7f3R40apR49ejTo+gD+KygoSLNnz3abLQIA+Pbh7wPAe/yvpBZRUVG65557tGTJEhmGIcMw9N5772nTpk1KSEhQWFiYsrKytG/fPrlcLvO4Ll26aMGCBX6vz+qpxxUVFUpMTFRiYqJCQkLUvXt3denSRW3btlVgYKAKCwt16tQppaamum1bJF1+lrcp7hn4NnA6nZozZ05zlwEAaGb8fQB4j1Bbh0GDBunOO+/UypUrzVHRrKwsZWVl1di/e/fuWrhwod+XXc/OznZbabl9+/ZKSEiw7PwlJSU6evSojh49Wm/fa6+9Vrfccgv7xwIAAABoFoTaeowePVo9e/bU6tWrlZycXOPU3MjISI0bN06zZs1qkikiX3/9tdsCTY3dm7ZNmzaaOXOmjhw5opMnT1YbifUUHBysoUOHavLkyYqPj2/wdQEAAACgsRxG1XSEOhUUFCglJUUXLlxQSUmJIiIiFBsbq969eysgoHU8nlxeXq7MzExlZ2eb91leXq7Q0FC1bdtWXbt2Vbdu3VrN/QIAAACwN0ItAAAAAMC2GG4DAAAAANgWoRYAAAAAYFuEWgAAAACAbRFqAQAAAAC2RagFAAAAANgWoRYAAAAAYFuEWgAAAACAbRFqAQAAAAC2RagFAAAAANgWoRYAAAAAYFuEWgAAAACAbQU1dwEAgObz+OOP69y5c4qJidEzzzzT3OUAAAD4jFALoNndf//9DTrupZdeUtu2bWv87MKFC/r66691+PBhnTlzRkVFRaqoqFBISIiio6PVuXNnxcXF6aqrrlJcXJwCAqpPXNm6dav+8pe/uL23aNEiDRw40Kv63n77be3YscPtvaVLl3p5d7WrqKhQRkaGTp48af6cOnVKLpdLkjR69Gj98Ic/bPR1rPDmm29q586dhGYAaAEOHz6sF198UZI0e/ZszZkzp5krAqxBqAXQ6nz22Wf66KOPzJBXVWFhoQoLC3Xq1Cnt3LlTkjR9+nTNnTvXq3Nv3brVq1BbXFysPXv2+Fa4l5YtW+a3c1uprKxM+/fvlyQNGTKkmasB0JL445eZ6enp2rZtm44fP67s7GwVFxcrICBAoaGhiomJUdeuXRUfH69+/fqpQ4cOXtV1zTXX6N577/WqtgMHDuiPf/yj23t33nmnxowZ49XxtbHTLzL9YdOmTVq5cqUk6emnn1ZsbGwzV4SWiFALoEV54IEHvO4bHBxc7b3Vq1fr008/Ndu9evXSgAEDFBsbK6fTqeLiYp09e1bHjx/XiRMnVFFRIcMw6r1WQECAKioqlJSUpMLCQoWFhdXZf8eOHeY/OCqPtYrnucLCwhQWFqasrCzLrmGFw4cPq6SkRJI0dOjQ5i0GQKt16dIlrVixQtu3b6/2WXl5uVwul/Ly8nTixAl99dVXkryfdbN3716v/syXLv/S0x/s8otMf0lKSpIkdevWjUCLWhFqAbQojQk/6enp+uyzzyRJQUFBuueee3T11VfX2r+goEDbt29XUFD9fxQOGjRISUlJKisr0/bt2zV58uQ6+1f+wykuLk55eXnKzc31/kbqER8fry5duiguLk49evRQbGxsjVOlm1vlP0TCwsLUu3fvZq4GQEvVmF9mlpeX65VXXtGxY8ckXf4l4uDBg9W7d29FRUVJuvxn/ZkzZ3T06FFlZGRIqv7LQU+Vv4wsKyvTjh07NGnSpDr7FxYWau/evW7HWsUuv8j0h5KSEh0+fFgSM35QN0ItgFZjx44d5qjrlClT6gy0ktSuXTtNmTLFq3N37dpVubm5OnnypLZu3VpnqD19+rROnjwpSRo7dqw++eQTL+/AO7NmzbL0fP5gGIYZagcNGqTAwMBmrghAS9WYX2Zu2rTJDLQxMTFatGiRunTpUmv/zMxMbdmypdYpzJUiIiIUERGhtLQ0bd26td5QW3V2TuUvQa1il19k+sP+/ftVVlYmiRk/qBuhFkCrcfbsWfN13759LT//2LFjdfLkSaWnpystLU1xcXE19qscpXU6nRo5cqTlodafXC6XvvzySyUmJio7O1uXLl1SVFSU+vfvrylTpqhTp05enefkyZPm6DS/XQfgL1WnHP/gBz+oM9BKUqdOnbxeQ2HMmDFKS0tTWlqaTp06pW7dutXat3LqcXx8vLp27WppqLXDLzL9pfJ7bN++fa1/5wISoRZAK1J1ilZeXp7l5x8xYoTee+89uVwubd26tca/YMvLy81/ZA0dOrTe0YCW5MKFC3rttdd06tQpt/ezs7O1adMmbd26VQsWLNDo0aPrPVflNDyn06kBAwb4o1wA8OsvM0eOHKl//vOfcrlc+uqrr3TrrbfW2O/UqVNKS0uTdDkIX7hwwdI6/O3s2bPauHGjDh48qAsXLigoKEidOnXSNddco2uvvVZOp7NZ6iovLzcXGxw8eHCz1AD7qL6HBQDYVNXVLDdt2mROWbJK27ZtzSnNO3bsqPH8SUlJKigokKRGr3jZlMrLy7V06VKdOnVK3bt319y5c3Xvvffq1ltvVZ8+fSRdHsX9y1/+YgbWulT+dj0hIaHGBb0AwArl5eXma6t/mRkWFmZOed2+fXutf6dUnZ0zYsQIS2vwt8TERD399NP64osvlJmZqdLSUhUVFenEiRN677339Nvf/lbnzp1rltqOHDmioqIiSUw9Rv0ItQBajar/mEhNTdXTTz+tTZs2KScnx7JrVAbVqouCVFX5j5v27dsrISHBsuv6W25urk6cOKGJEyfq8ccf1/Tp03XNNddo8uTJ+p//+R/dfPPNki4/K/vOO++ouLi41nNlZ2frzJkzkph6DMC/OnbsaL7+/PPPLT9/1T/za5pSXLl4oCRdffXVtpqdc/LkSS1fvlwul0sjR47UHXfcobvvvlszZ85UZGSkpMujuC+++GKdf+b7S+XfsW3btvXLI0VoXZh+DKBF8Xbvwpr25bvyyis1ZcoU8x82Z86cMfe2a9euneLi4hQfH6/evXurb9++DZpSlZCQoNjYWOXk5Oirr77SNddcY36Wm5urAwcOmPUFBNjr94Y9evTQrbfeWmPd1113nY4fP669e/cqPz9f27Ztq3WxrMp/iDgcDkItAL8aOXKkVq9eLUn6z3/+o6ysLI0dO1ZXXXWVV9vw1CchIUHt27fX+fPntXXrVg0fPtzt88pt3iR7zc6RpOTkZLVp00YPPfSQrrrqKrfPpk+frldffVXHjx9XTk6OPvjgAy1YsKBJ66v8JcLAgQNZbBD1ItQCaFXmzZunrl276qOPPnLbRqegoEAHDhwwQ2doaKjGjBmjWbNmqV27dl6f3+FwaPTo0froo4/M54+io6MlSdu2bVNFRYUcDoft/nEjSdOmTasziF933XVmYN21a1etobbyHyI9e/ZURESE5XUCaF0a88vMKVOmaN++fUpJSZF0ebXcyucwY2NjzV9m9u3bV/Hx8XI4HD7VFhAQoDFjxmjt2rU6cOCA25/50n9n58TExNhqdk6lm266qVqglS6Pjt5333164okndOnSJW3dulXXX3+9wsPDm6SutLQ089lkph7DG4RaAC2Kt/sVtm/fvtbPxo0bp1GjRunAgQPav3+/UlJSdObMGbeFpIqLi/X5558rMTFRP/rRjxQfH+91jZX/wDEMQ9u2bTNXpqxc/bJv37623CC+X79+dX7es2dPhYSEqKSkRGlpaaqoqKgWggsKCsx/XDJKC8DfnE6nHn74YX344YfatGmTSktLzc9ycnKUk5Oj3bt3S7ocPKdMmaKJEyf6NPI3ZswYffzxx6qoqNDXX3+tmTNnSrq8uF7V2Tm+Bubm1rbt/9fe/cVUXf9xHH8B5wDyR0FMFFRAQPCUZkIpMFep6Wz+WUzTZpvdlLO1al1VW1utZasu7MIy3dq0zd8WXWjLaalTMfX4Z6ZinKNyFAz8E4oCcgDPEc7vgp1vIIjncJDDkefj6sv5fs/3vKNNzuvz/bw/nxjNmjXrgecTExM1Y8YMHTx4UPfu3VNZWZmKiooGpDbvAKrJZGKxQfiEUAtgUOmvEVmTyaSpU6caKya63W7V1NSooqJCJ0+eVFVVlaSOhUW+++47ffrppz5PVfP2y9rtdiPUVlRUqLa2VpJ/U9CuX7/eZfXO+40ZM0Zjxozx+X59FRMT89An1mFhYXriiSdUXV0tl8ullpaWbr+zsrIyY/CA0XUAvgh0MNNsNmvp0qVasGCBTp8+LbvdrsrKym7rKdTV1amkpEQnTpzQO++84/MsnaSkJOXk5OjcuXM6cuSIEWqtVqs8Hk/Izs7JzMx8aBtObm6uDh48KKljrYqBCrXeGT85OTmKjo4ekM9EaCPUAhgSzGazMjIylJGRoXnz5uno0aPavHmzPB6PGhsbVVpa6tdegEVFRbLb7aqtrdWFCxeMp7TDhg3T9OnTfb7PiRMntGPHjgeeX7hwoRYtWuTz/frK1xWKIyMjjePW1tZuodY7up6cnDwgYRxA6OuvAbDY2FgVFRUZwcvpdOry5cs6f/68jh8/rlu3bkmSKisr9eOPP+q9997z+d5FRUU6d+6camtrVVFRoezsbFmtVkkdwSspKcmn+wyWgUyp6yJbvlzTuaXHa/Xq1UpKStLatWv7ra6bN28aW8sxOApfEWoBDEkzZ86Uw+HQn3/+KUmy2+1+hVrvHrTNzc3av3+/ysvLJXWswNw5+IWKu3fv+nRd56l994+eu1wu2e12SXwRARB8sbGxslgsslgsWrx4sX7++WeVlpZKkmw2mxwOh7Kysny6l3dl4+bmZh05ckQej8eYnePP08vBMpAp+TaY2fma1tbWR1mOgcUG0ReEWgBD1uTJk41Q29DQ4Nd7zWaznnvuOR04cMDo15L8X/1y0aJFA/YFpjfNzc1yOp29TsH2eDy6ceOGpI4ntsOGDety3m63G6GXLyIABpOIiAgtX75cFy5c0LVr1yR1/Jvla6j17kFbWlqqkydPGlvcdN6/PNT4MpjZ+ZqBmgbsnXqcnp5ubC0EPAyhFsCQ1XmhEF+n33ZWVFSkAwcOGD+npKQoIyOjP0oLCpvN1mWv3/tVVVUZI/VpaWndFonyjq4PHz48pH8PAB5PERERmjRpkhFq/R3MLCwsVGlpqe7evatTp05J6pid48/2cINlIFOSMUjp6zUJCQnG8ZEjR7RlyxZJHb3KnVew7jwd+fr16/rjjz/kcDh0+/Ztmc1m42/ECy+80G2RRqfTKYfDIYnBUfiHUAvgsdHY2OjXFjLeECZJqampfn/ehAkTlJeXZ/Rp9baKZCjYu3ev8vPzH7iC5+7du43j+/uG29vbdfbsWUnS1KlTQ26PXgBDQyCDmenp6Ro3bpzR7ymF3t60nTkcDrnd7l5DubelRFKXADp69GgVFBTIarUqKiqqy98E7wJc//zzj77++mu53W6lpKRo6tSpamtr061bt3Ts2DE98cQT3UJt58UGCbXwB6EWwGOjpKREjY2Nmj17tp566imZTD3/E+fxeLRv3z4dPXrUeG3GjBl9+sy33nqrT+8bjKqqqlRSUqJly5Z1C6V79uwxplnHx8eroKCgy/lLly7pzp07kuinBTBw/BnMbGtrM9Y/kPo2mDl37lyjLzchIcGv7eAGm+bmZh06dEgvvvhij+cbGhp0/PhxSf/tKOCVlZWlrKwsWa1WxcXFdds/WJL27dsnt9ut4uJizZ8/v9u9nU5nt/d4px6PHj1aKSkpff1PwxBEqAUwqHR+evowGRkZXfptPB6Pzp8/r/PnzysmJkaTJk1SWlqaEhISjP1Vr127prKysi6rTxYVFT10j9bB5ObNmzp06FCX165cuWIcV1dXa/v27V3O5+bmKjc394H3TEhIUGJiovbt26eKigrNmDFDiYmJunPnjv766y9duHBBUsfCHa+//nq3flrv/7eoqKhePwcA+tPatWuVm5urWbNmKTMz84HXtba2auvWrfr3338ldaxU35cngQUFBd0G9ULZtm3blJqaqkmTJnV5vaWlRZs2bTJaTgoLCxUfH+/Xvb0DnRaLpdu5ESNGdOuXdbvdxr6/PKWFvwi1AAaVDRs2+HztmjVrujwVHDNmjEwmk+7du6fm5madPn2615BsMpk0f/58LVy4MICKB15dXZ127dr1wPM1NTVdpsdJHVPuegubERERWr16tdavX6/q6mpVV1d3u8ZkMmnlypU9Pon1jq4/+eSTfvWXAUAgg5ltbW2yWq2yWq1KSkpSdna2xo8fr/j4eJlMJjmdTlVXV+vUqVNGyAoLC9Py5ct93ps82B7FQKYkTZkyRXa7XevWrVN+fr5yc3MVGRmpa9eu6fDhw8YWPqNGjVJxcbHfdaelpenvv//W//73Py1ZskTZ2dldpn/fz263GwtTMeMH/iLUAnhsLFq0SC+99JLOnTuniooKVVdX68aNG2pqapLb7VZUVJTi4uKUkpKiSZMm6dlnn+2y8MVQl5iYqA8//FAHDx7UiRMnVFtbq7t37yohIUEWi0Vz585VcnJyt/ddvXrV2NqC0XUA/gpkMHPcuHGy2+3yeDyqq6tTXV1dl9aS+w0fPlwrVqxQXl5eICUPqEcxkCl1hM6ZM2dqy5YtOn78uDHVuLPk5GS9++673Wbn+GLevHm6dOmSEZzNZrPS0tJksVhUUFCgkSNHdrneOzgaHx+viRMn+v15GNoItQCCbuPGjf12r+joaE2bNq1fRnkLCwv7ZRGQr776KuB7dJaTk9NvvzPvCpVeZrNZc+bM0Zw5c3y+h/cpS3h4uKZMmdIvdQGAL9577z3V19fLZrPp4sWLunLliurq6tTc3Kz29nZFR0crISFBqampeuqpp/TMM8/0abX7x1V+fr7GjRun/fv3y2azqb6+XhEREUpOTlZ+fr5eeOGFPs++iY6O1vvvv6/KykqdPXtWFy5cUGVlpRwOh3bt2qU333zTGAhtb29XWVmZJBYbRN+EeTweT7CLAACEri+//FJVVVXKycnRBx98EOxyAAADZPXq1V228HmY1tZW/fHHH9q5c6eGDx+ub775RpJ08eJFff3115Kkt99+m1k/8BvDIACAPmtoaNDly5clMfUYAIaaiIgItbW1+Xx9dHS0lixZIrPZrMbGRqPP2TvjJzIyMqQWbsTgwfRjAECfjRgxQj/88EOwywAABEFCQoJu374tp9PZbeGt0tJSTZ48WaNHj+7y+vnz5+V2uxUdHa2YmBhJ//XTWiwWRUZGDkzxeKww/RgAAACA337++Wft27dPSUlJyszMlNlsVlxcnIqLi/X555+rpqZGycnJGjt2rMxms27duqVLly7J4/FoxYoVD9wjF/AXoRYAAACA31wul7Zt26YzZ87o9u3bam9vN3psz5w5o7KyMlVWVqq+vl4ul0sjRozQhAkTNGfOHGVlZQW7fDxGCLUAAAAAgJDFQlEAAAAAgJBFqAUAAAAAhCxCLQAAAAAgZBFqAQAAAAAhi1ALAAAAAAhZpmAXAADBsHr16j69b926dcZm8Q/i8XhUXl4um80mh8OhhoYGNTU1KTw8XDExMRo7dqwyMjI0ffp0jR8/vsd7HDlyRFu2bPGrtlWrVqmwsLDLax9//LHq6up6vN5sNis2NlZjx45Vbm6uCgsLNXz4cL8+EwAAINgItQDQj86cOaPt27fr6tWrPZ53uVyqr6+X3W7Xzp07lZ6eruLiYuXk5AxwpZLb7VZ9fb1Rz65du/Taa69p5syZA14LAABAXxFqAQx5a9as8fnaqKioHl9vb2/Xr7/+qt9//914LS4uThaLRenp6YqLi5MkNTY2qqqqSjabTc3NzaqqqtKGDRv07bffPvAzc3JyNHv27IfWNmHChF7Pr1y5ssuTWJfLpatXr+rYsWO6deuWWltbtXnzZkVGRmr69OkP/TwAAIDBgFALYMibNm1awPfoHGgjIiK0aNEizZkzR5GRkT1ef+/ePR0+fFi7du1Sa2trr/ceOXJkv9RosVg0atSobq+//PLL2rRpk86ePSuPx6OSkhI9/fTTioiICPgzAWAwoxWFVhQ8Hgi1ABCgs2fPdgm077zzjiwWS6/vMZlMev7555Wfn69ffvllIMp8oMjISL3xxhv66KOP5HK5dPv2bV26dEnZ2dlBrQsAQhWtKMDAItQCQAA8Ho+2b99u/Lxw4cKHBtrOYmNj9cYbb/R/YX6Ki4tTZmam7Ha7JKmmpoZQC2BIoRWFVhSELkItAATg3LlzqqmpkdQRUOfOnRvkivouPj7eOG5paQliJQAw8GhFoRUFoYt9agEgAOXl5cZxXl7eA7+4hII7d+4Yx8OGDQtiJQAQenpqRVmwYEGvfxe8rSiffPJJvwTWQHhbUbz1eltRgFBAqAWAADgcDuM4MzMziJUEpqmpSRcvXjR+Tk1NDWI1ABBaHrdWFC/vTCRgsGP6MYAhz9fVLwsKCrp96aivrzeOR48e3Y9V/cdqtcpqtfZ6zbJly/o89dnlcumnn36Sy+WSJCUmJoZ0QAeAgUYrChBchFoACEBTU5Nx/LDtHYLNZrN1Wxzk+vXrOnr0qLHVQ1hYmJYtW0YPFQD4gVYUILgItQCGPF9XvBw5cuQjrqRnvqx46ct04a1bt/Z6PioqSsuXL1deXp5f9QHAUEcrChBchFoAQ14gi3PExsYaU5Cbm5v7p6D79NeKl/czmUyKiYlRSkqKJk+erIKCAo0YMaLfPwcAQgGtKLSiIHQRagEgAAkJCcaXmdraWk2cODG4BfXiiy++6HEbBwBAYGhFAYKLUAsAAcjOzlZVVZUk6eLFi5o5c2ZwCwIA9AmtKB1oRUEoItQCQAAsFov27NkjSTp58qReffVVmc3mIFcFAPAXrSi0oiB0EWoBIACTJ09Wamqqrly5IqfTqb1792rBggXBLgsAMIBoRQGCKzzYBQBAKAsLC9OSJUuMn3/77TfZbDaf39/S0qItW7Y8itIAAAMkOzvbOO68ejCAgUGoBYAAPf3005o3b54kqa2tTevXr9fvv/9urCDZk7a2Nh06dEifffaZTp06NVClAgAeAYvFYhyfPHlSbrc7iNUAQw/TjwEMeadPn/b52oyMjB57jV555RV5PB7t2bNHbW1t2rZtm/bs2aMnn3xS6enpiouLkyQ1Njbq8uXLKi8vl9PplMTm9gAQ6mhFAYKLUAtgyNuwYYPP165Zs6bHhTrCw8O1dOlSZWVlafv27bp27Zqampp07NgxHTt27IH3y8zMVHFxcV/KBgAMEt5WlO+//15SRytKWlpalye4vWlpaVFJSYlWrVr1KMsEHluEWgDoR9OmTdPUqVNVXl4um80mh8OhhoYGOZ1OhYeHKzY2VmPGjNHEiROVl5fn0/YLAIDBz9uKsnv3bqMVZfHixZo9e7YiIyN7fE9bW5usVqt27Nih1tZWQi3QR4RaAEPSxo0bH9m9w8PDNWXKFE2ZMqXP9ygsLFRhYWHAtaxduzbgewDAUEArChC6CLUAAAAY8mhFAUIXoRYAAADoR7SiAAMrzOPxeIJdBAAAAAAAfcE+tQAAAACAkEWoBQAAAACELEItAAAAACBkEWoBAAAAACGLUAsAAAAACFmEWgAAAABAyCLUAgAAAABCFqEWAAAAABCyCLUAAAAAgJBFqAUAAAAAhCxCLQAAAAAgZBFqAQAAAAAhi1ALAAAAAAhZhFoAAAAAQMj6P97/yO1ZPrDFAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "fig, ax = plt.subplots(figsize= (10,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "labs = [\"ESM-1b/ \\nECFP\", \"ESM-$1b_{ts}$/ \\nECFP\"]\n", + "y_test_pred_esm1b_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ECFP.npy\"))\n", + "test_y_esm1b_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ECFP.npy\"))\n", "\n", + "y_test_pred_esm1b_ts_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_ECFP.npy\"))\n", + "test_y_esm1b_ts_ecfp = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_ECFP.npy\"))\n", "\n", - "plt.scatter(1, acc_esm1b_ecfp_test, c='mediumblue', marker='^', linewidths= 8, label = \"test set\")\n", + "y_test_pred_esm1b_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_GNN_pretrained.npy\"))\n", + "test_y_esm1b_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_GNN_pretrained.npy\"))\n", "\n", - "plt.scatter(2, acc_esm1b_ts_ecfp_test, c='mediumblue', marker='^', linewidths= 8)\n", + "y_test_pred_esm1b_ts_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_GNN_pretrained.npy\"))\n", + "test_y_esm1b_ts_GNN = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_GNN_pretrained.npy\"))\n", "\n", "\n", - "plt.boxplot([accuracy_CV_ESM1b_ECFP, accuracy_CV_ESM1b_ts_ECFP,], positions=[1,2], widths=0.6, whis =2)\n", "\n", - "plt.ylim(0.75, 1)\n", - "ticks1 = [1,2]\n", "\n", - "ax.set_xticks(ticks1)\n", - "ax.set_xticklabels([])\n", - "ax.tick_params(axis='x', which=\"major\", length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "ticks2 = [0.99, 1.99]\n", + "acc_esm1b_ecfp_test = np.mean(np.round(y_test_pred_esm1b_ecfp) == test_y_esm1b_ecfp)\n", + "acc_esm1b_ts_ecfp_test = np.mean(np.round(y_test_pred_esm1b_ts_ecfp) == test_y_esm1b_ts_ecfp)\n", "\n", - "ax.set_xticks(ticks2, minor=True)\n", - "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", - "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 0)\n", + "roc_auc_esm1b_ecfp_test = roc_auc_score(test_y_esm1b_ecfp, y_test_pred_esm1b_ecfp)\n", + "roc_auc_esm1b_ts_ecfp_test = roc_auc_score(test_y_esm1b_ts_ecfp, y_test_pred_esm1b_ts_ecfp)\n", "\n", - "plt.ylabel(\"Accuracy\")\n", - "ax.yaxis.set_label_coords(-0.15, 0.5)\n", - "plt.legend(loc = \"upper right\")\n", - "plt.show()" + "acc_esm1b_GNN_test = np.mean(np.round(y_test_pred_esm1b_GNN) == test_y_esm1b_GNN)\n", + "acc_esm1b_ts_GNN_test = np.mean(np.round(y_test_pred_esm1b_ts_GNN) == test_y_esm1b_ts_GNN)\n", + "\n", + "roc_auc_esm1b_GNN_test = roc_auc_score(test_y_esm1b_GNN, y_test_pred_esm1b_GNN)\n", + "roc_auc_esm1b_ts_GNN_test = roc_auc_score(test_y_esm1b_ts_GNN, y_test_pred_esm1b_ts_GNN)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### (b) ROC-AUC scores" + "#### (a) Accuracy:" ] }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -1459,36 +1483,36 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", + "fig, ax = plt.subplots(figsize= (10,8))\n", "plt.rcParams.update({'font.size': 28})\n", - "labs = [\"ESM-1b/ \\nECFP\", \"ESM-1b/ \\nGNN\", \"ESM-$1b_{ts}$/ \\nECFP\", \"ESM-$1b_{ts}$/ \\nGNN\"]\n", + "labs = [\"ESM-1b\\n + ECFP\", \"ESM-1b\\n + GNN\", \"ESM-$1b_{ts}$\\n + ECFP\", \"ESM-$1b_{ts}$\\n + GNN\"]\n", "\n", "\n", - "plt.scatter(1, roc_auc_esm1b_ecfp_test, c='blue', marker='^', linewidths= 8)\n", - "plt.scatter(3, roc_auc_esm1b_ts_ecfp_test , c='blue', marker='^', linewidths= 8, label = \"test set\")\n", - "plt.scatter(2, roc_auc_esm1b_GNN_test, c='blue', marker='^', linewidths= 8)\n", - "plt.scatter(4, roc_auc_esm1b_ts_GNN_test , c='blue', marker='^', linewidths= 8)\n", + "plt.scatter(1, acc_esm1b_ecfp_test, c='darkblue', marker='o', linewidths= 8)\n", + "plt.scatter(1.8, acc_esm1b_GNN_test, c='darkblue', marker='o', linewidths= 8)\n", + "plt.scatter(2.6, acc_esm1b_ts_ecfp_test , c='darkblue', marker='o', linewidths= 8, label = \"test set\")\n", + "plt.scatter(3.4, acc_esm1b_ts_GNN_test , c='darkblue', marker='o', linewidths= 8)\n", "\n", "\n", - "plt.boxplot([ROC_AUC_CV_ESM1b_ECFP, ROC_AUC_CV_ESM1b_GNN, ROC_AUC_CV_ESM1b_ts_ECFP, \n", - " ROC_AUC_CV_ESM1b_ts_GNN], positions=[1,2,3,4], widths=0.6, whis =2)\n", + "plt.boxplot([accuracy_CV_ESM1b_ECFP, accuracy_CV_ESM1b_GNN,\n", + " accuracy_CV_ESM1b_ts_ECFP, accuracy_CV_ESM1b_ts_GNN], positions=[1,1.8, 2.6, 3.4], widths=0.5, whis =2)\n", "\n", "plt.ylim(0.75, 1)\n", - "ticks1 = [1,2,3,4]\n", + "ticks1 = [1,1.8, 2.6, 3.4]\n", "\n", "ax.set_xticks(ticks1)\n", "ax.set_xticklabels([])\n", "ax.tick_params(axis='x', which=\"major\", length=10)\n", "ax.tick_params(axis='y', length=10)\n", - "ticks2 = [0.99, 1.99,2.99,3.99]\n", + "ticks2 = [1-0.01,1.8-0.01, 2.6-0.01, 3.4-0.01]\n", "\n", "ax.set_xticks(ticks2, minor=True)\n", "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", - "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 0)\n", + "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", "\n", - "plt.ylabel(\"ROC-AUC score\")\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "plt.legend(loc = \"lower right\")\n", + "plt.ylabel(\"Accuracy\")\n", + "ax.yaxis.set_label_coords(-0.15, 0.5)\n", + "plt.legend(loc = \"upper right\")\n", "plt.show()" ] }, @@ -1501,22 +1525,29 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ - "y_test_pred= np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_ECFP.npy\"))\n", - "test_y = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_ECFP.npy\"))" + "y_test_pred= np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_GNN_pretrained.npy\"))\n", + "test_y = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_GNN_pretrained.npy\"))" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 27, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1526,7 +1557,6 @@ } ], "source": [ - "\n", "plt.rcParams.update({'font.size': 28})\n", "\n", "#plt.rc('font', **font)\n", @@ -1542,12 +1572,14 @@ "ax.yaxis.set_label_coords(-0.08, 0.5)\n", "ax.xaxis.set_label_coords(0.5,-0.13)\n", "\n", + "plt.legend(loc = \"upper right\", fontsize=18)\n", + "\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1556,16 +1588,18 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 29, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "text/plain": [ - "0.04842814371257485" + "0.0441340367040642" ] }, - "execution_count": 5, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1576,98 +1610,97 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_sure = y_test_pred[~unsure[0]]\n", + "y_test_sure = test_y[~unsure[0]]\n", + "\n", + "y_test_pred_unsure = y_test_pred[unsure[0]]\n", + "y_test_unsure = test_y[unsure[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.93361694, 0.9979367 , 0.08058584, ..., 0.02723099, 0.5507995 ,\n", - " 0.00139621], dtype=float32)" + "(0.9343179168286048, 0.5673400673400674)" ] }, - "execution_count": 6, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "y_test_pred" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "y_test_pred_sure = y_test_pred[~unsure[0]]\n", - "y_test_sure = test_y[~unsure[0]]\n", - "\n", - "y_test_pred_unsure = y_test_pred[unsure[0]]\n", - "y_test_unsure = test_y[unsure[0]]" + "np.mean(np.round(y_test_pred_sure) == y_test_sure), np.mean(np.round(y_test_pred_unsure) == y_test_unsure)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.9245654054904429, 0.5486862442040186)" + "(0.9607938619791544, 0.6071803830719178)" ] }, - "execution_count": 8, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.mean(np.round(y_test_pred_sure) == y_test_sure), np.mean(np.round(y_test_pred_unsure) == y_test_unsure)" + "roc_auc_score( y_test_sure, y_test_pred_sure), roc_auc_score(y_test_unsure, y_test_pred_unsure)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.9555710779275287, 0.5793224540384219)" + "(0.8282749736529739, 0.10725390349723644)" ] }, - "execution_count": 9, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "roc_auc_score( y_test_sure, y_test_pred_sure), roc_auc_score(y_test_unsure, y_test_pred_unsure)" + "matthews_corrcoef(y_test_sure, np.round(y_test_pred_sure)), matthews_corrcoef(y_test_unsure, np.round(y_test_pred_unsure))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Plotting only wrong predictions:" + "#### Plotting only wrong and correct predictions:" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "y_test_pred= np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_ECFP.npy\"))\n", - "test_y = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_ECFP.npy\"))" + "y_test_pred= np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_GNN_pretrained.npy\"))\n", + "test_y = np.load(join(CURRENT_DIR, \"..\" ,\"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_GNN_pretrained.npy\"))" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1676,12 +1709,12 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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i2NhYPfTQQ/L391fdunXl6+urJk2aaNy4cebrLVW1fPlybd++Xe3bt1dgYKD8/Pz00EMPKTk5Wc2aNdPq1autTu6pjODgYH399deKiIjQlStXNHv2bNWvX19BQUEKCAhQYGCg7rvvPq1YsUK5ubml2i9dulQRERHKycnR5MmTVadOHQUFBal58+b69NNPtXDhwirvs/K///1Pw4cPV2FhoV555RU1bdpUAQEB8vb2VuPGjfWHP/xB+/bts2rTunVr/fa3v5VUtILH39/f/I4sXLiwQuMOGTJECxYskMVi0dq1a9WyZUsFBQXJz89PY8eOVXp6ujp37qxPPvmkWleqGIahHTt26PHHH1erVq3k5eWlunXrysPDQ4MGDVJ8fLwaNWqkpUuXVtuYAICaiUAGAADUKG3atFF0dLQeeOAB1atXT0lJSTp37pzOnTtXpX1KJOmFF17Qe++9p+7du8vX11cFBQX6zW9+o8WLF+uzzz4r9w/uAQMGKCYmRi+//LJ69eqlgIAApaamysXFRR06dNCkSZO0fv16vfHGG1WanyQNHTpUO3bs0IMPPigvLy8ZhqGwsDDNnj1bBw8eVFhYWJX7lqSwsDDt3btXK1as0JAhQxQaGqorV64oNzdXYWFhGjZsmJYuXWr39a2QkBDt2LFD8+bNU/v27eXi4iI3NzcNGjRIX3/9dZmBVnl8fHz06aefasOGDRo2bJgaNWqknJwc+fn5qUuXLvrjH/+ot99+u1S71atXa+bMmWrbtq3y8vLM70hlXmOaOXOm9u7dq7Fjx6pp06bKysqSt7e3unfvrgULFmj37t1q1KhRlZ/NngceeEArVqzQY489poiICAUEBCgtLU116tRRt27d9OKLL+ro0aNq3759tY4LAKh5LEZ17ZgHXAfZ2dmaMWOGFi5caPVOdVVYLPOqaVb2GcZz17V/AEDts3XrVt1zzz2Sqm8TYwAAcHNghQwAAAAAAICTEcgAAAAAAAA4GYEMAAAAAACAkxHIAAAAAAAAOFnVzk4EAADANevbty+b+QIAcItihQwAAAAAAICTEcgAAAAAAAA4GYEMAAAAAACAkxHIAAAAAAAAOBmBDAAAAAAAgJMRyAAAAAAAADgZgQwAAAAAAICTEcgAAIAap6CgQAsWLNBtt90mX19fWSwWWSwWrV279pr7fv7552WxWNS3b99rnygqbMKECbJYLJowYUKpe3379pXFYtHzzz/v1Dlt3brV/G4BAOBsbjd6AgAA4AZwxh+ghlHlpjNmzNCbb74pSfLw8FCDBg0kSV5eXtUyNdRuqampWrhwoaSi71JgYOANnhEAAKURyAAAgBolIyNDS5YskSTNnz9fTz/9NCsYarlmzZqpXbt2qlu3brX0l5qaqnnz5kkqWpnjKJDx8fFRu3btqmVMAAAqi0AGAADUKCdOnFBeXp4kaerUqYQxt4AVK1bckHG7deumEydO3JCxAQBgDxkAAFCjZGVlmb/7+fndwJkAAABcPwQyAACgRli2bFmpzXaLN1y1vX7x4kUtXbpUw4cPV3h4uAICAuTt7a3WrVvr97//vY4ePVrleWzatEnDhw9XkyZN5OHhIX9/f7Vs2VIDBw7Ua6+9puTkZLvtcnJy9O9//1t9+vRR3bp15eHhoYYNGyoqKkobN26s8nyKn3/r1q1KSEjQ9OnTFRYWJi8vLzVs2FCPPPKIw1UeZ8+eNdufPXtWp0+f1uTJkxUWFiZPT0+1aNGiVJu1a9cqKipKjRo1koeHh4KCgtS7d28tXrzYXLnkyAcffKC77rpLderUUUBAgCIjI/X222/LKGc/oYps6nv8+HE98cQT6tChg+rUqSM/Pz+1a9dOo0eP1qeffqrCwkKzr7CwMLNdWFiYw+9RRTb1TUhI0DPPPKOOHTvKz89Pvr6+6tixo5599lklJibabWP7uScmJuqpp54y/90aNGig0aNHl7k6Jz4+XjNnzlTHjh3l6+srT09PNWrUSLfffrtmzpypPXv2OGwLALg58MoSAACoEby9vdWgQQPl5uYqJSVFkszNfCUpODjY/P3ZZ5/V8uXLzbK/v7/y8/N1+vRpnT59Wu+//74++OADPfjgg5WawwsvvKDnnnvOLPv4+MgwDMXGxio2NlZff/217rjjjlInNJ06dUqDBw/WqVOnJBWFKP7+/kpMTNS6deu0bt06TZ06VW+99Val5lNSbGysxowZo4SEBHl7e8vd3V2JiYn68MMP9dlnn2nNmjUaNGiQw/Y7duzQlClTlJmZKR8fH7m7u1vdz8zM1JgxY7Rhwwbzmr+/v9LS0rR9+3Zt375dK1as0BdffKGgoCCrtoZhaNKkSXr33XfN5w8MDNTevXu1e/duRUdHy9PTs8rP/uqrr+ovf/mLGbp4eXnJ3d1dJ0+e1MmTJ7Vy5UqlpKQoMDBQwcHBqlu3ri5fvixJqlu3rlxdXc2+Sn6PyvPdd98pKipKqampkoq+DxaLRceOHdOxY8f0zjvvaP369erVq5fDPo4ePaqJEyfq4sWL8vHxkVQUKK5cuVJfffWVtm3bpoiICKs2hw4d0j333GP+/8DV1VX+/v5KSEjQr7/+qv379yslJUXLli2r8LMAAGoeVsgAAIAaYdSoUUpISNBnn31mXktISDB/Sl4PCwvT3/72Nx04cECZmZlKS0vT1atXdeTIET3yyCO6evWqxo8fr19++aXC4587d87cCHbWrFm6cOGCrly5ooyMDKWmpmr79u2aNm2a6tSpY9UuNTVVAwcO1KlTp9SvXz9t27ZN2dnZSk1NVWpqqhYsWCA/Pz8tWrRIr7/+epU/n5kzZ8rDw0ObN28257Vr1y517txZOTk5GjVqlOLj4x22nzJlijp27Kg9e/boypUryszM1ObNm83748aN04YNG9S6dWt9+OGHSk9PV1pamrKysrRu3Tq1bNlSO3fu1MSJE0v1/cYbb5hhzPTp03Xx4kUlJycrOTlZzz//vFauXKl169ZV6bkXLVqkOXPmqLCwUA888IAOHDig7OxspaenKykpSZs3b9aoUaPk4lL0n7WfffaZ1eqRPXv2OPweleX8+fNmGNOhQwd9//335ue2bds2tWvXTikpKRo6dKguXLjgsJ9x48apTZs2Vp/7119/rdDQUKWnp+vJJ58s1Wb27NlKSUlR165dtXPnTuXl5Sk5OVk5OTk6efKkXnvtNXXs2LGSnyQAoMYxgBosKyvLmDx5spGVlXXNfUnPX9cfALipFB1KfX1/qig6OtqQZFzLf6YMHjzYkGS8+OKLpe4999xzhiSjT58+VtdXrlxpSDLatm1bqbGefvppQ5LRr18/Iy8vz26dzz77zJBk1K1b12EdR4o/Cw8PD+PYsWOl7icmJhrBwcGGJGPatGlW92JjY832zZs3NzIyMuyOsWHDBkOS0bBhQyM+Pt5unfPnzxu+vr6GJOPAgQPm9ezsbHP8cePG2W07Z84ccx7jx48vdb9Pnz6GJOO5556zup6cnGzUqVPHkGSMHj3aKCwstNu/rZLPHRsb67BeWd+1xx9/3JBkBAUFGb/++mup++fPnzf8/f0NScYTTzzhcPz27dvb/e+Y9evXm3XOnz9vdc/b29uQZOzYsaNCzwsAuDmxQgYAANQ6gwcPliR9//33FW5TfDRyRkaGrly5UqE2hmFo6dKlkopWNbi52X8bPCoqSv7+/rp8+bL27dtX4TmV9NBDDyk8PLzU9fr16+vxxx+XJK1cudJh++nTpzvcJPmdd96RVLSao3HjxnbrNGnSRPfcc4+kon12im3evNncV+fvf/+73bZz5syRl5eXw7k5snr1amVkZMjd3V0LFixw2olbhmFo1apVkqTHH39cDRs2LFWnSZMm5uf+8ccfO+xr9uzZ8vb2LnX9vvvuk4eHhyTp8OHDVveKv4u//vpr1R4AAHBTIJABAAA3pUOHDmnatGnq0qWL/P395eLiYm6kOm3aNEkq8xUeW926dVPdunX166+/KjIyUm+++aZOnDhR5oa0x44dM8OICRMmqGHDhnZ/QkNDlZmZKano1aiq6NevX7n3kpKSFBsba7fOXXfd5bB9cXD19ttvO3yGhg0b6ptvvin1DHv37pUkNW3aVK1bt7bbf0BAgG6//fYyns6+HTt2SJJuv/12hYaGVrp9VcXGxpr/rv3793dYb8CAAZLK/twjIyPtXndzc1O9evUkqdRG0UOGDJEkjR8/XrNnz9Z3331ndfoYAKB2YFNfAABw03nzzTf11FNPmZu8WiwWBQQEmBvHFu8xUtGVLlLRqoSPPvpIDz/8sI4ePWru7REQEKDevXtr5MiRGjVqlNVmuCX3qLl06VKFxqnqH9aOVq7Y3rt48aLVKUPF6tevb7dtXl6euQFuWlqa0tLSyp1LyWe4ePFiufOTilaUVFZCQoIkqXnz5pVuey2Kn0kq+7lKPpOjz912z6GSildU2Z5eNX/+fMXExCg6OloLFizQggUL5Orqqt/85jcaPHiwJk+eXO7nDQCo+VghAwAAbirHjx/XjBkzVFhYqIceeki7d+9WTk6OUlJSzI1bFyxYIEnlHrdsq3///oqNjdWKFSs0fvx4tWnTRmlpafr88881btw43XbbbVYbuBYUFJi/JyQkyDCMcn8mTJhQpee+1td1Sp40VFLJZ/j4448r9Az2Tve5nq8TOetVpWsZuzrnGBgYqC1btmj79u169tlnddddd8nNzU379u3TCy+8oDZt2uijjz6qtvEAADcGgQwAALiprF69WgUFBQoPD9fHH3+sO++809yLo1jxyoqq8PX11bhx47Rs2TKdPHlS8fHxevXVV+Xl5WW1ckaS1d4itvuAVLeyXr8qGRI5WgnjiJeXlwICAiRV7RmKxyvv9bCyTiJypPg1pbNnz1a67bUo+RmeP3/eYb2Sz1z8+lF16tWrl1599VV9//33Sk1N1bp169S5c2dlZ2dr4sSJSkxMrPYxAQDOQyADAABuKsV/IEdERJhHHdsq3uukOjRu3FjPPvusZs+eLUn6+uuvzXudOnWSv7+/pLI3dq0O0dHR5d4LDg62+9pMeYr3l/nkk0/M18Aq6o477pBU9O9y+vRpu3XS09OrtJlxz549JRXtU1OZDW5Lfi8qu0pKKjpWPTg4WJL07bffOqxX/D0LCQmp0udeGV5eXnrggQfMY7tzcnIqtWk1AKDmIZABAAA3lZKrOez9sf3VV19p69atle736tWrZd4vPimn5Ks/bm5umjhxoiRp+fLl5f6BbLt5a2V88skn+vnnn0tdv3z5spYsWSJJGjVqVJX6njx5siTp5MmT+uc//1lm3StXrig3N9csDxgwQEFBQZKkF1980W6b+fPnKzs7u9Lzeuihh+Tv76/8/HzNnDmzwuFKcUgmSampqZUe12KxmJ/lkiVL7K64+uWXX8zPfcyYMZUew5H8/PwyQ7GSJzY5eg0NAHBzIJABAAA3lUGDBkmSjh49qieeeMIMOa5cuaIlS5ZoxIgRCgkJqXS/r776qu677z699957Vq+iXL16VatWrTKDivvvv9+q3dy5c9WqVSvl5+dr0KBBWrBggdUGv2lpadq4caPGjx+vu+++u9LzKubl5aVBgwbpm2++MYOJPXv2qH///rp8+bLq1KmjOXPmVKnvoUOHatiwYZKKjqieOnWqTp48ad7Pzc3Vrl279Kc//UnNmze32vTW29tbc+fOlVQUSs2YMUNJSUmSilbGvPjii/rHP/5hHuVcGQEBAZo/f76koiO9hw0bpoMHD5r3U1JS9MUXX2jo0KFKT083rwcGBpqb3r777rvKz8+v9Nh/+ctfFBgYqOTkZPXv39888UmSfvjhB/Xv31+pqakKDg6u8uduT3x8vNq0aaOXXnpJBw4csJr7Tz/9pLFjx0oqerWud+/e1TYuAMD5CGQAAMBN5be//a1Gjx4tSVq0aJFCQkIUFBSkgIAAPf744woPD9fzzz9f6X4LCwu1ceNGPfroo2ratKl8fHwUEhIib29vjRo1SmlpaQoPDzc3DC4WHBysr7/+WhEREbpy5Ypmz56t+vXrm3MKDAzUfffdpxUrVlitLKmsBQsWKCcnRwMGDJCfn5/q1Kmjbt266dChQ/L09OKQ0qMAACAASURBVNRHH32kZs2aVbn/999/3/xcFy9erHbt2snPz0/BwcHy9vZW9+7dNX/+fCUlJZXawPapp57SuHHjJEmvv/666tevr+DgYAUHB+vvf/+7Ro0apaFDh1ZpXlOmTNE//vEPubi4aN26dbrtttvk4+Mjf39/BQcHa8iQIVq/fn2pVSWPP/64JOmNN96Qn5+fmjVrphYtWpjPWJ4mTZpo7dq1CggI0NGjR3XXXXfJz89Pfn5+6tWrl44fP67AwECtXbu22k88OnPmjObOnauuXbvKy8tLISEh8vT0VEREhLZu3SoPDw8tW7bMfK0KAHBzIpABAOBWZBjX/+c6+uCDD7Rw4UJ16dJFnp6eKigoUOfOnfXyyy/rhx9+kJ+fX6X7nDx5st5++22NGTNGnTp1ko+Pj9LT0xUUFKS7775bCxcu1P79+6028i0WFhamvXv3asWKFRoyZIhCQ0PNV3vCwsI0bNgwLV26VDt37qzyM7ds2VIHDhzQE088oXr16ik3N1f169fXmDFjdODAAQ0ePLjKfUuSj4+PPvroI0VHR2vcuHFq2bKlCgsLlZmZqfr166tfv36aP3++Tp06VSqAcHFx0YoVK7RixQp1795d3t7eys/PV9euXbV48WJ9+OGH1zS3P//5zzp06JD+8Ic/qHXr1pKK9oZp166dxowZo88++8zqNSWpaIXL66+/rjvuuEPu7u6Kj4/XuXPnKrXhc58+fXTixAnNnj1b4eHhKiwslGEYCg8P19NPP63jx49f06onexo3bqz169dr5syZ6t69u0JDQ5WZmSk3Nzd16NBBTzzxhI4cOaIRI0ZU67gAAOezGFXZ6QxwkuzsbM2YMUMLFy60eme6KiyWedU0K/sM47nr2j8A4NZUvBolOjpaffv2vbGTAQAA1YYVMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZG43egIAAABwjPMXAAConVghAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZAQyAAAAAAAATkYgAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZAQyAAAAAAAATkYgAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZAQyAAAAAAAATkYgAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZAQyAAAAAAAATkYgAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZAQyAAAAAAAATkYgAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZAQyAAAAAAAATkYgAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZAQyAAAAAAAATkYgAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABO5najJ3Cru3TpkuLi4pSSkiLDMBQYGKhGjRqpcePG1TbG2bNnlZiYqNTUVHl4eCgwMFBhYWEKDAyslv7z8vIUExOj5ORkZWRkyNfXV0FBQWrTpo08PT2rZQwAAAAAAGoTAplK+vjjjxUdHW11rUePHpowYUKl+jly5Ii++OILnTlzxu79Jk2aaODAgYqMjKzSPAsLCxUdHa0tW7bo8uXLpe5bLBa1b99eUVFRatGiRZXGyM7O1vr16/Xjjz8qKyur1H1PT0917dpVw4YNU0BAQJXGAAAAAACgNiKQqYQzZ85o69at19SHYRhatWqVtmzZUma9+Ph4LV26VIcPH9b48ePl7u5e4TEyMzP19ttv6+effy5zHsePH9fJkyc1bNgwDRgwoML9S1JcXJwWL16spKQkh3WuXr2qnTt36siRI5o0aZLCw8MrNQYAAAAAALUVgUwFFRQU6L333pNhGNfUz5o1a0qFMa1atVKLFi3k4uKi+Ph4nThxwhxnz549cnFx0cSJEys8z8WLF+vUqVPmNRcXF3Xu3FmhoaHKyclRTEyM4uPjzfqrV6+Wt7e3evXqVaExkpOT9cYbbyg9Pd285uPjoy5duigoKEjp6ek6fPiweT8jI0OLFi3SM888o6ZNm1ZoDAAAAAAAajMCmQrauHGjfvnlF0lSQECA0tLSKt3HTz/9pE2bNpllHx8fTZ48udTKkbi4OL311ltKSUmRJO3atUutW7dW7969yx1jzZo1VmFM48aNNW3aNNWtW9eq3q5du7RixQrl5+dLkj788EOFhYWVu3eNYRhasmSJVRhz5513auzYsfLy8jKv5eXlac2aNfr2228lFa2WWbRokebNm1ep1T4AAAAAANRGnLJUAQkJCfryyy8lSR4eHoqKiqp0H4ZhaO3atWbZYrFo6tSpdl/jadasmWbOnGkVXGzYsEG5ublljpGSkmK1v02dOnU0a9asUmGMJEVGRmrs2LFmuaCgQOvWrSv3Ofbv36+zZ8+a5fDwcE2aNMkqjJEkd3d3jRw5Uj179jSvJSUl6bvvvit3DAAAAAAAajsCmXIYhqH33nvPXEkyePBghYSEVLqfgwcP6sKFC2Y5MjJSbdu2dVi/QYMGGjhwoFlOS0vT999/X+YYmzdvNucpScOGDZOfn5/D+j169FCbNm3M8qFDh8xXmRwpDqakolDp4YcflsVicVh/xIgRVmHNpk2bVFBQUOYYAAAAAADUdgQy5di2bZtiYmIkSY0aNar05rfF9u3bZ1Xu27dvuW3uvvtuubj83z+RbR8lGYah/fv3m2UfHx9169at3DH69OljVS7Zh63ExESrwKZjx46qX79+mf37+vpazSM9Pd3qlSoAAAAAAG5FBDJlSE1N1Zo1ayQVrQYZO3asXF1dK91PQUGBjh49apaDgoIUFhZWbrugoCC1bNnSLJ85c0aZmZl26547d06pqalmuUuXLhXaqyUiIsLqmQ4dOuSwru29rl27ltu/vXpljQEAAAAAwK2AQKYMH330kbKzsyVJvXr1UqtWrarUz4ULF5SVlWWWK9NPyUCmsLDQXK1jy/Z6yXZl8fDwsDr5KD4+3mquZY1R0ecICwuzWunDChkAAAAAwK2OQMaB/fv36+DBg5KKNscdNmxYlftKSEiwKlfm6OdmzZqV2VexX3/9tcx21T2Gu7u7GjRoUKH+vby8VK9ePav+r/X4cAAAAAAAbmYEMnZkZ2dr5cqVZvmhhx6Sr69vlfuzDTiCg4Mr3Na2rqOw5FrGCAoKKneMgoICXb582ar/sjbztVVyPnl5eUpKSqpwWwAAAAAAahsCGTtWr15t7scSHh6uyMjIa+qv5N4u0rWFJSkpKeWO4eLiIn9//2odIy0tTYWFhQ7bVHYM288EAAAAAIBbCYGMjZMnT+qHH36QJLm5uWnMmDHX3GdOTo5V2dPTs8JtbetevXrVbr2S1z09PSu1eqXksdSOxrC9VplnsDeG7WcCAAAAAMCthECmhLy8PL3//vvm/ib3339/hfdJKYttmFGR048c1a1IIFOZ/is6xrU8g1QUbpU3BgAAAAAAtwoCmRK++OILJSYmSpIaNGige++9t1r6zcvLsypX5uhs2yAjNze33DEqezS37Ri287V3zbZNeWwDHHtjAAAAAABwqyCQ+f9duHBBmzdvNsuPPPJIpUMHR2zDiIKCggq3zc/Ptyp7eHiUO0Zl+rc3hr3VL7bXbNuUxzaAqewKGwAAAAAAahMCGUmFhYV67733zCCjR48eateuXbX1b7vfSmVWh9jWdbR3S8nrlV19UpExruUZpNIBTmX3oAEAAAAAoDYhkJEUHR2t2NhYSZKvr69GjBhRrf1fy4a2Fd1Mt+T1q1evWp2IVJ6KbDpse62ym/La1rf9TAAAAAAAuJXc8oFMbm6u1q1bZ5YffPBB+fn5VesYgYGBVmVHR1fbk5ycbFV2dNx0yTEKCwuVkZFR4TFs52NvjICAALm4/N/XpbLHVtuOYfuZ2MrLy1N2drYZ5OTk5Cg7O5u9ZwAAAAAAtUL1bJJyE8vPz7dahfL+++/r/fffL7NN8SlMxX788Uft2rXLLPfo0UOPPvqoWW7YsKFVfduQpSy2QYZtX8VCQ0MVExNjlpOSkhQQEFClMUJDQ0vVcXV1Vb169cxNj5OTk2UYRoWP1y75zB4eHgoJCSmz/saNG7VhwwazPGfOHEnSkCFD9Lvf/a5CYwIAAAAAUFPd8oGMrcq86lPMMAyrkMa2D9uAIy4ursJ929Z1FMjYXj9//rxatmxZ7WMUBzJ5eXlKSEiwG97YysnJ0cWLF81ygwYNyg1yBg0apP79+ysnJ0dz5szRK6+8Ii8vr2rbaBkAAAAAgBvpln9lyRkaNWokHx8fs3zmzJkKtz19+rT5u4uLi1q3bm23nu31ku3Kkpubq/Pnz5vlJk2ayNvbu1rHiI2NtQqs2rRpU24bd3d3eXt7m3vNeHl5ydvbm9OZAAAAAAC1wi2/3MDHx0dLliypVJuff/5ZCxYsMMs9evTQhAkTHNZ3dXVVp06dtHv3bklFrwjFxsYqLCyszHGK6xVr2bKlw/1tmjdvrsDAQHNvl8OHDysvL6/cAOPgwYNWx2RHREQ4rBsREaFPP/3ULO/fv1+9evUqs39J2rdvX6l+AAAAAAC4lbFCxkluv/12q/LWrVvLbbN9+3ar159s+yjJYrGoa9euZjkrK8sMgMqybds2q3LJPmw1aNBATZo0McvHjh2zehXJnitXrmjv3r1m2d/fv0IrZAAAAAAAqM0IZJwkIiJCjRs3Nsu7du3SyZMnHdZPTEzU5s2bzXJAQEC5q1EGDhxotcfKmjVrlJmZ6bD+zp07derUKas5lgxc7Ln//vvN3w3D0Icfflhqk+OSVq9erezsbLN87733ytXVtcwxAAAAAACo7QhknMRisSgqKsosG4ahRYsW6fjx46XqxsXF6V//+pfVEc9DhgyRh4dHmWMEBQXpnnvuMcsZGRlasGCBLl++XKrurl27rE6TcnV11dChQ8t9jq5du6pFixZm+fjx4/rf//5nHk9dLC8vT6tWrdKOHTvMayEhIerTp0+5YwAAAAAAUNvd8nvIOFOXLl107733atOmTZKKXitauHChWrVqpRYtWsjFxUXx8fE6ceKE1aqTyMhI9e7du0JjREVFKTY21jwC+8KFC/r73/+uzp07q2HDhsrJyVFMTIzi4+Ot2j388MNWK3gcsVgsmjJliv7xj38oIyNDkrRnzx4dPXpUERERCgwMVHp6uo4cOaK0tDSznaenp6ZOncqmvAAAAAAAiEDG6aKiopSXl6ctW7aY106fPu3wxKI77rhD48aNq3D/bm5umjp1qpYsWWK+ElVQUKCDBw/are/i4qKoqKgKbc5bLDg4WE8++aSWLFmipKQkSUXh0s6dO+3W9/Pz06RJk9S0adMKjwEAAAAAQG1GIONkLi4uGjVqlDp27KgNGzZYnaJUUuPGjTVw4EB179690mP4+flp5syZ2rJli6Kjo+2+smSxWNS+fXsNHTq03NOe7GnevLnmzp2rdevWadeuXcrKyipVx8PDQ127dtXw4cMVEBBQ6TEAAAAAAKitLEZZO7Liurt48aLi4uKUmpqqwsJCBQYGqnHjxhV6fagiDMPQ2bNnlZiYqLS0NLm7uyswMFBhYWEKCgqqljHy8vIUExOjpKQkZWRkyNfXV0FBQWrTpo28vLyuqe/s7GzNmDFDCxculLe39zX1ZbHMu6b25TGM565r/wAAAACA2oMVMjdY/fr1Vb9+/evWv8ViUVhYWJVWwVSUu7u7wsPDr1v/AAAAAADUNpyyBAAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZAQyAAAAAAAATkYgAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZAQyAAAAAAAATkYgAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZAQyAAAAAAAATkYgAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZAQyAAAAAAAATkYgAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkBDIAAAAAAABORiADAAAAAADgZAQyAAAAAAAATkYgAwAAAAAA4GQEMgAAAAAAAE5GIAMAAAAAAOBkbtXd4bZt2yRJFotFd999d5X7+eGHH1RQUCBJ6t27d7XMDQAAAAAAoCao9kCmb9++slgscnNz09WrV6vcz6BBg5SVlSWLxaL8/PxqnCEAAAAAAMCNVe2BjCQZhiHDMKqlHwAAAAAAgNqGPWQAAAAAAACcrMYGMoWFhZIkF5caO0UAAAAAAIAqqZFpR05OjrKzsyVJvr6+N3g2AAAAAAAA1atGBjJbt26VVHRSU2ho6I2dDAAAAAAAQDWr0qa+6enpSk1NLbfe+fPnK7wxb0FBgVJSUrRnzx699NJL5vXf/OY3VZkiAAAAAABAjVWlQOZf//qXXnjhhTLrFBQUqEWLFlXp3irEGT58eJX6AAAAAAAAqKmqfOx1eStfqnpktcVikcVikWEY6tWrl0aMGFGlfgAAAAAAAGqqGreHjGEY8vf314wZM/TVV1/JYrHc6CkBAAAAAABUqyqtkImKirL7OpJhGJo4caIkydXVVe+8806F+3R3d1edOnXUokULdejQQa6urlWZGgAAAAAAQI1XpUAmIiJCERERdu9NnDjRfO1o/Pjx1zQ5AAAAAACA2ui6vLJU1f1jAAAAAAAAbgVV3tTXkcLCwuruEgAAAAAAoFapcZv6AgAAAAAA1HYEMgAAAAAAAE5GIAMAAAAAAOBk1b6HjK1t27Zp586dOn78uFJSUpSZmVmpTX8tFou+/fbb6zjD0goLC3Xp0iVdunRJKSkpysrKUn5+vjw9PeXn56cmTZqoUaNGcnG59jzr0qVLiouLU0pKigzDUGBgoBo1aqTGjRtXw5MUOXv2rBITE5WamioPDw8FBgYqLCxMgYGB1dJ/Xl6eYmJilJycrIyMDPn6+iooKEht2rSRp6dntYwBAAAAAEBtct0CmQ8//FB/+9vfdO7cuSr3YRiGLBZLNc7KsczMTG3atEmnT5/W+fPnlZubW2Z9Hx8fde/eXQMGDFBwcHClxzty5Ii++OILnTlzxu79Jk2aaODAgYqMjKx031JRqBQdHa0tW7bo8uXLpe5bLBa1b99eUVFRatGiRZXGyM7O1vr16/Xjjz8qKyur1H1PT0917dpVw4YNU0BAQJXGAAAAAACgNrIY1+GM6unTp2vRokXmSpjiUKUqQ1ksFhUUFFTr/Ow5e/asXn755Uq38/T01OjRo9WzZ88K1TcMQ6tWrdKWLVsqVP/OO+/U+PHj5e7uXuE5ZWZm6u2339bPP/9cbl1XV1cNGzZMAwYMqHD/khQXF6fFixcrKSmp3Lp16tTRpEmTFB4eXqkxpKLQZ8aMGVq4cKG8vb0r3b4ki2XeNbUvj2E8d137BwAAAADUHtW+Quadd97RW2+9JakoTDEMQ4ZhKCQkRC1atJCvr6/TVr1cC39/fzVu3Fj169eXj4+PXFxcdOXKFZ0/f15nzpwxw6WrV69q+fLlys/PV+/evcvtd82aNaXCmFatWqlFixZycXFRfHy8Tpw4Yfa/Z88eubi4aOLEiRWad0FBgRYvXqxTp06Z11xcXNS5c2eFhoYqJydHMTExio+PN+uvXr1a3t7e6tWrV4XGSE5O1htvvKH09HTzmo+Pj7p06aKgoCClp6fr8OHD5v2MjAwtWrRIzzzzjJo2bVqhMQAAAAAAqM2qNZApLCzUc889ZxW4TJw4UbNmzVKHDh2qc6hq5+LiojZt2qhr167q0KGDGjZs6LBuUlKSPvroIx0+fNi8tmrVKoWHh6tevXoO2/3000/atGmTWfbx8dHkyZNLrRyJi4vTW2+9pZSUFEnSrl271Lp16woHPiXDmMaNG2vatGmqW7euVb1du3ZpxYoVys/Pl1T0illYWFi5e9cYhqElS5ZYhTF33nmnxo4dKy8vL/NaXl6e1qxZY+7/c/XqVS1atEjz5s2r1GofAAAAAABqo2o9ZWnPnj369ddfzfI///lPvfPOOzU+jJGkZs2a6emnn1a/fv3KDGMkKSQkRNOmTbMKUvLy8hQdHe2wjWEYWrt2rVm2WCyaOnWq3dd4mjVrppkzZ1oFFxs2bCh3X5uUlBSrOdSpU0ezZs0qFcZIUmRkpMaOHWuWCwoKtG7dujL7l6T9+/fr7NmzZjk8PFyTJk2yCmMkyd3dXSNHjrR6lSspKUnfffdduWMAAAAAAFDbVWsg89NPP5m/t23bVrNmzarO7msUFxcXDR8+3OrasWPHHNY/ePCgLly4YJYjIyPVtm1bh/UbNGiggQMHmuW0tDR9//33Zc5p8+bN5ooXSRo2bJj8/Pwc1u/Ro4fatGljlg8dOmS+yuTIl19+af5usVj08MMPl/kK2ogRI6zCmk2bNjllTyAAAAAAAGqyag1kSm7w2r9//+rsukZq1qyZ1bHOycnJDuvu27fPqty3b99y+7/77rutjta27aMkwzC0f/9+s+zj46Nu3bqVO0afPn2syiX7sJWYmGgV2HTs2FH169cvs39fX1+reaSnp1u9UgUAAAAAwK2oWgOZwMBA83d7r8nURiVXfzg6RaqgoEBHjx41y0FBQQoLCyu376CgILVs2dIsnzlzRpmZmXbrnjt3TqmpqWa5S5cuFdqrJSIiQq6urmb50KFDDuva3uvatWu5/durV9YYAAAAAADcCqo1kGnWrJn5e/GGtLVZbm6uVUDiKIS6cOGCsrKyzHKrVq0qPEbJQKawsFAxMTF269leL9muLB4eHlYnH8XHx1vNtawxKvocYWFhVit9WCEDAAAAALjVVWsg06dPH/n6+koqOsWnttu3b5/VfihdunSxWy8hIcGqXJmjn0uGXPb6KlZyM2V77ap7DHd3dzVo0KBC/Xt5eVmdPpWQkOBwNREAAAAAALeCag1kfH199fDDD8swDO3Zs8fqWOjaJj4+XqtXrzbLvr6++u1vf2u3rm3AERwcXOFxbOs6CkuuZYygoKByxygoKNDly5et+i9rM19bJeeTl5dntd8QAAAAAAC3mmoNZCRp/vz5atasmQoLCzVx4kSHr7/cbAzDUHZ2tmJiYrRy5Uq9/PLL5utKnp6emjJlivz9/e22Lbm3i3RtYYmjV8FKjuHi4uJwLlUdIy0tTYWFhQ7bVHYM288EAAAAAIBbiVt1dxgQEKD169dr8ODB2r9/v+666y4tW7ZMERER1T3UdZWQkKB58+aZZcMw7L5m06lTJ40cObLM13dycnKsyiVPZiqPbd2rV6/arVfyuqenZ6VWr5TcmNjRGLbXKvMM9saw/UwAAAAAALiVVHsgs2LFCknStGnTNG/ePB06dEhdu3ZVz5491adPHzVu3NjcZ6aiHn300eqeZoWUXBFiy2KxqG/fvrr33nvLXS1iG2ZU5PQjR3UrEshUpv+KjnEtzyBJbm7WXzVHzwEAAAAAwK2g2gOZCRMmlFqdYRiGduzYoR07dlSpzxsVyJTFMAxFR0dr+/bt6tu3r6KiohyGFHl5eVblksdMl8c2yMjNzS13jMr0b28M2/nau2bbpjy2n429MQAAAAAAuFVUeyBjqzKvztgyDOOa2l+Lhg0basmSJWY5Pz9fV65c0fnz57V3717t2bNH+fn5ys/P1zfffKMLFy5o+vTpdoMK2zCi5MlM5cnPz7cqe3h42K3n7u5uhjWV6d/eGPaCJdtrtm3KYxvAVHaFDQAAAAAAtUm1b+or/d9+K9f6U5O4ubkpICBAnTp10oQJEzRnzhyrV5WOHz+uzz//3G5b2/1WKrM6xLauo71bSl6v7OqTioxxLc8glQ5wKrsHDQAAAAAAtUm1r5CJjY2t7i5rpKZNm+qPf/yjXnrpJXNFyrfffqsBAwbIz8/Pqu61bGhb0c10PT09lZGRYbYpLCyUi0vF8raKbDpse62ym/La1rf9TGzl5eUpPz/fbFf8v25ubqyuAQAAAADc9Ko9kGnevHl1d1ljNWrUSHfeead+/PFHSUUhwpEjR9S9e3ereoGBgVZlR0dX25OcnGxVdrSBcGBgoC5fviypaDPijIwMBQQEVGgM2/nYGyMgIEAuLi7mRseVPbbadgzbz8TWxo0btWHDBrM8Z84cSdKQIUP0u9/9rlJjAwAAAABQ01z3PWRqu/DwcDOQkaT4+PhSdRo2bGhVtg1ZymIbZNj2VSw0NFQxMTFmOSkpqcqBTGhoaKk6rq6uqlevnhITEyUVPUNl9vgp+cweHh4KCQkps/6gQYPUv39/5eTkaM6cOXrllVfk5eVV6c2EAQAAAACoia7LHjK3En9/f6tydnZ2qTq2AUdcXFyF+7et6yiQsb1+/vz56zpGXl6eEhISKtR/Tk6OLl68aJYbNGhQbpDj7u4ub29v89UmLy8veXt787oSAAAAAKBWIJC5RrYBjI+PT6k6jRo1srp+5syZCvd/+vRp83cXFxe1bt3abj3b6yXblSU3N9cqvGnSpIm8vb2rdYzY2FirTZrbtGlToXYAAAAAANRWBDLXyHZ1SXBwcKk6rq6u6tSpk1lOSUmp0ObHtvVatmxZasPgYs2bN7fal+Xw4cMVOgnp4MGDVsdkR0REOKxre2///v3l9i9J+/btK7MfAAAAAABuNQQy1yA3N1e7d++2uhYeHm637v/H3p1HR13f+x9/fSdkJZCEQAKEJWGTPZVFBFm0yqJiAbW1Vbn+Drhhq2KrkSbbxgAAIABJREFULdrFi9dDvfecW7EuCC7trUpbwSIWrEAFdzaJIChbgBhACEnIvpAh+f7+SPNtvpNtJpl8M5k8H+dwzny+89lm+CSZ73s+y9ixY23pDz74oMn6P/74Y2sT3frqqM0wDI0ZM8ZKl5aW1ulbfT766CNbunYdnhITE9WnTx8r/fXXX9uWItWnpKREn3/+uZXu2rUrM2QAAAAAAB2e33dIHTBggF/rMwzD66UxzeV2u5WVlWULNjSlqqpKb7zxhm2z2pSUlAb3X0lNTVVSUpJOnz4tSdq5c6euuOIKDRkypN78WVlZ2rx5s5WOiYnR5MmTG+3TjBkz9NFHH+nixYuSpHXr1ik1NbXBWTXbt2/X0aNHbX1s6j247rrrtGrVKkmSaZpavXq1HnzwwQb3hFm7dq1tWdfMmTMVEhLSaBsAAAAAAAQ7vwdkMjIyZBiGbc+Q5qipw9tTfFrC7XbrySef1KWXXqqJEydq+PDhjZ7mc+LECb311lu2YIZhGLrlllsaLGMYhubOnavnn39eUnUwY8WKFbr77rvrzKrJzMzUCy+8YFtyNHv2bIWFhTX6OuLi4nTVVVdpy5YtkqSioiL97ne/03333afu3bvb8u7cuVOvv/66lQ4JCdGcOXMarV+qnkGTnJysjIwMSdLBgwf1yiuv6Pbbb7c24JWq39N169bps88+s67Fx8dr2rRpTbYBAAAAAECwa5UzhJsTjKkdeDFNs8UBHV+Zpqm0tDSlpaUpLCxMffr0sTbjDQsL04ULF5SXl6eMjAzl5OTU6fv8+fOVkpLSaBujR4/WzJkztWnTJknVy4qWL1+ugQMHKjk5WS6XS6dOndKhQ4dsr3/ChAmaOnWqV69j7ty5OnHihHUE9unTp/Wb3/xGo0aNUs+ePVVeXq709PQ6x3PfeuutSkpKarJ+wzB0zz33aNmyZSoqKpIk7d69W1999ZVSU1MVGxurwsJCHThwQAUFBVa58PBwLVq0iFOSAAAAAABQKwRk7rjjDp/yFxcX69tvv1VaWpouXLggwzAUHh6u73//+222tKWiokLHjx/36jSk2NhY3XbbbRo9erRXdc+dO1dut1tbt261rh07dqzBZVnjxo3T/Pnzveu4pE6dOmnRokVauXKljhw5IkmqrKzU3r17683vcrk0d+7cJpdD1datWzfdf//9WrlypXJzcyVVB5e2b99eb/7o6GgtXLhQffv29boNAAAAAACCmWE6PRWlAaWlpXr55Zf1m9/8RkVFRbr66qu1Zs0axcTEtHrbVVVV2rVrl7766isdOXJE+fn5TZbp27evJk2apIkTJzZ4THRjDhw4oA0bNjR42lJSUpJmzJihyy+/3Oe6perXtHXrVm3btq3OjB6peqbL0KFDNWfOnCZn9jSkrKxM69ev186dO1VaWlrn+bCwMI0ZM0Y33nhjs/8fy8rKtHjxYi1fvrxZ73NthrG0ReWbYpqPt2r9AAAAAIDgETABmRpHjhzR1KlTlZ2drcmTJ2vbtm1yuZw9DKqgoEDffvutcnNzVVJSoosXLyosLEyRkZGKj49X//79FRUV5Ze2zp07p8zMTOXn56uqqkqxsbFKSkryavmQN0zTVEZGhrKyslRQUKDQ0FDFxsYqJSVFcXFxfmnD7XYrPT1dubm5KioqUufOnRUXF6fBgwfb9pVpDgIyAAAAAIBg1Cp7yLTEkCFD9Morr+iGG27QJ598omXLlulXv/qVo32IiYlxZGaOJCUkJCghIaHV6jcMQykpKc2eBeON0NDQBo/7BgAAAAAAdTk79cRL119/vYYOHSrTNPXcc8+psrKyrbsEAAAAAADgNwEZkJFkHY+cnZ2tjz76qI17AwAAAAAA4D8BG5BJTEy0Hntz2hEAAAAAAEB7EbABmeLiYutxdnZ2G/YEAAAAAADAvwI2IPPxxx9bj2NjY9uwJwAAAAAAAP4VkAGZt956S7t377bSl1xySRv2BgAAAAAAwL8CKiBTWFio//7v/9btt98uwzAkVR9BPXny5DbuGQAAAAAAgP908neFCxYs8Cm/aZoqLS3VN998o3379qmiokKmaUqSDMPQkiVLFBoa6u9uAgAAAAAAtBm/B2T++Mc/WrNbfGWapgzDkGEYMk1Tc+bM0cMPP+znHgIAAAAAALStVluyZJqmz/9qykVFRenJJ5/UmjVr5HIF1KoqAAAAAACAFvP7DJl+/fr5NEPGMAx17txZcXFxGj58uCZNmqQbb7xR0dHR/u4aAAAAAABAQPB7QCYjI8PfVQIAAAAAAAQV1gMBAAAAAAA4jIAMAAAAAACAwwjIAAAAAAAAOIyADAAAAAAAgMP8vqlvfSorK7Vr1y599tlnOnTokPLy8lRUVKQuXbooLi5OQ4cO1aRJk3TZZZcpJCTEiS4BAAAAAAC0mVYNyJSWlurpp5/WihUrdObMmSbz9+7dWz/+8Y/1wAMPKCoqqjW7BgAAAAAA0GZabcnSnj17dOmll+o3v/mNvv32W5mm2Wh+0zR1+vRp/fKXv9SYMWOUlpbWWl0DAAAAAABoU60SkElLS9N3v/tdpaenyzRNGYYhqTroUpOOioqSYRjWNUlW+siRI7rqqqv0xRdftEb3AAAAAAAA2pTfAzJFRUWaPXu2ioqKrGtRUVFauHChNm7cqDNnzujixYsqKirSxYsXdebMGW3cuFF33nmnOnfuLKk6MFNTT3Fxsb+7CAAAAAAA0Kb8HpB56qmndPbsWWu2y4wZM3To0CG99NJLuvbaa5WYmGjLn5iYqGuvvVarVq3SoUOHNGvWLGvGzNmzZ/XUU0/5u4sAAAAAAABtyq8BGdM09dJLL1lLlK6//npt2LBBSUlJXpXv3bu3/v73v+v666+31QcAAAAAABBM/BqQ+fzzz5WTkyPTNBUeHq5XXnlFnTr5dpBTSEiIXn75ZUVEREiScnJytHv3bn92EwAAAAAAoE35NSBz8OBBSdV7wMycOVMJCQnNqicxMVEzZ86sUy8AAAAAAEAw8GtA5ty5c9bjoUOHtqiu2uWzs7NbVBcAAAAAAEAg8WtApmbvGEnWxrz+4M+6AAAAAAAA2ppfAzI9evSwHh8+fLhFdR06dMh63NylTwAAAAAAAIHIrwGZmmVGpmlq8+bNysnJaVY92dnZ2rx5c516AQAAAAAAgoFfAzLjx49XfHy8DMNQeXm57rnnHp+XG1VVVemee+5RWVmZJKlbt2667LLL/NlNAAAAAACANuX3PWQWLFhgBWHefvtt3XTTTV5vypudna2bb75Z69evt+pbuHChP7sIAAAAAADQ5vwakJGkxx57TPHx8ZKqly6tX79eQ4YM0f33368tW7bUWcaUk5OjLVu26Cc/+YmGDBliBWMkqXv37nr00Uf93UUAAAAAAIA21cnfFcbExOidd97R9OnTrWVHBQUFeuGFF/TCCy9Iklwul6KiolRaWqqqqiqrrGmaMgxDpmkqKipK77zzjmJiYvzdRQAAAAAAgDbl9xkykjRx4kRt2rRJffv2tYIsUnXAxTRNVVZWqqioSJWVldY1SVYwpn///tq8ebMmTJjQGt0DAAAAAABoU60SkJGkK664Qvv379evf/1rJSQkNLm5r2maSkhI0OOPP64vv/xSkyZNaq2uAQAAAAAAtCm/L1mqrUuXLlq6dKl+9atfaceOHdq+fbsOHz6svLw8FRUVqUuXLoqLi9Mll1yiSZMmacKECQoNDW3NLgEAAAAAALS5Vg3I1AgNDdWUKVM0ZcoUJ5oDAAAAAAAIaK22ZAkAAAAAAAD1IyADAAAAAADgsGYFZLZv366QkBDr3xVXXCG3292ijlRUVGjSpElWnaGhodq/f3+L6gQAAAAAAAhEzQrI/PznP7eOq05KStLf/va3Fm/GGxYWprfeeku9evWyjsb++c9/3qI6AQAAAAAAApHPAZnPPvtMn376qQzDkGEYWrFihRITE/3SmV69eumFF16w0ps3b9YXX3zhl7oBAAAAAAAChc8Bmddee816PHXqVF1//fV+7dD3vvc922lM//d//+fX+gEAAAAAANqazwGZt99+23r8yCOP+LUz9dW7du3aVmkDAAAAAACgrfgUkDl58qSysrIkSVFRUbrmmmtapVPTp09XVFSUTNPUmTNndOrUqVZpBwAAAAAAoC34FJBJS0uTJBmGocmTJyssLKxVOhUeHm5btlTTLgAAAAAAQDDwKSBz7tw563Hfvn393pnaatdfMysHAAAAAAAgGPgUkMnLy7Me9+zZ0++dqa32yU35+fmt2hYAAAAAAICTfArIuFz/zn7hwgW/d6a2iooK67FhGK3aFgAAAAAAgJN8Csj06NHDelx7+VJryM7OrrddAAAAAACA9q6TL5kTEhKsx19//bXfO1Nb7fqdDsiUlJTo9OnTOnfunEpKSmSapqKiohQfH68BAwYoMjLSL+1kZ2crMzNTeXl5Mk1TsbGx6t27t5KSkvxSvyRlZGQoKytL+fn5CgsLU2xsrFJSUhQbG+uX+t1ut9LT03X+/HkVFRWpc+fOiouL0+DBgxUeHu6XNgAAAAAACDY+BWSGDx8uSTJNU3v27FFOTo66d+/u907l5OTo888/t9IjRozwexu1VVVVKT09XV988YUOHTqkb7/9tsG8hmFo6NChuuaaazRy5MhmtXfgwAFt3LhRx48fr/f5Pn36aMaMGZowYUKz6q+qqtK2bdu0detW5eTk1Hm+5jXMnTtXycnJzWqjrKxM77zzjnbs2KHS0tI6z4eHh2vMmDGaN2+eYmJimtUGAAAAAADByqeATP/+/XXJJZfo8OHDMk1Tr776qn7+85/7vVOvvvqqqqqqZBiGBg8erP79+/u9jdoef/xxr5dgmaapgwcP6uDBgxo/frxuv/12RUREeF32zTff1NatWxvNd+rUKb366qvav3+/7rjjDoWGhnpVvyQVFxdr1apVOnz4cJOv4ciRI5o3b56mT5/udf2SlJmZqRdffFG5ubkN5rlw4YK2b9+uAwcOaOHChRo2bJhPbQAAAAAAEMx8CshI0syZM62AzLJly7RgwQK/zpLJzs7Wb3/7W2sj32uvvdZvdTekqKiozrWEhASlpKSoa9euCg0NVV5eng4dOmQ7aWr37t3Kz8/Xgw8+6FXQZN26dXWCMQMHDlRycrJcLpdOnTqlQ4cOyTRNq36Xy6UFCxZ49ToqKyv14osv6ujRo9Y1l8ulUaNGqVevXiovL1d6erpOnTpl5V+7dq0iIyM1efJkr9o4f/68nn32WRUWFlrXoqKiNHr0aMXFxamwsFD79++3ni8qKtKKFSv0yCOPtPpR6QAAAAAAtBc+B2QWL16sF198UW63W4WFhbrhhhv0z3/+U507d25xZ0pKSjRnzhwVFBRIksLCwvTAAw+0uF5vxcfHa/LkyZo4caLi4uLqPF9VVaWPP/5Ya9askdvtliQdPXpU69ev180339xo3V9++aU2bdpkpaOionT33XfXmTmSmZmpF154wQr87Ny5U4MGDdLUqVOb7P+6detswZikpCTdd999dQJmO3fu1J/+9CddvHhRkrR69WqlpKQ0uXeNaZpauXKlLRhT3ywht9utdevW6f3335dUPVtmxYoVWrp0qU+zfQAAAAAACFY+nbIkScnJybrrrrtkmqYMw9CuXbs0ffr0BvdD8daJEyc0c+ZM7dixQ1L1Pid33nmnUlJSWlSvN+Lj4/Uf//EfevLJJ3XdddfVG4yRqmebTJs2Tffee6/tKO6tW7faZs54Mk1Tb7/9tpU2DEOLFi2qdxlPv3799NBDD9kCFxs2bLAdA16fvLw8bdu2zUp36dJFP/3pT+udvTRhwgTdfvvtVrqyslLr169vtH5JSktLU0ZGhpUeNmyYFi5cWGfJVmhoqH7wgx9o0qRJ1rXc3Fx9+OGHTbYBAAAAAEBH4HNARpKeeOIJDR482Erv2LFDo0eP1tKlS3X69Gmf6jp9+rSWLl2q0aNHa/v27TIMQ4ZhaNCgQfqv//qv5nTPZ4899piuuOIKuVzevR0jR47U+PHjrXRlZaW+/PLLBvPv3bvX9r5MmDBBQ4YMaTB/YmKiZsyYYaULCgr0ySefNNqnzZs3WzNeJGnevHmKjo5uMP/EiRNt/4f79u2zljI15N1337UeG4ahW2+91RaY8nTzzTfbgjWbNm1SZWVlo20AAAAAANARNCsgExcXp3fffdeafWEYhkpLS/XEE08oOTlZ3/3ud7VkyRKtWbNGn376qQ4cOKATJ07owIED+vTTT7V27Vo9+uij+u53v6vk5GQ98cQTKikpkVQ9myQ+Pl7vvvtugzNV/C0kJMTnMrUDMlL1DJ+G7Nmzx5a+8sorm6x/ypQptgCRZx21maaptLQ0Kx0VFaXLLrusyTamTZtmS9euw1NWVpYtYDNixAjbMej16dy5s60fhYWFtiVVAAAAAAB0VD7vIVNj4MCBev/99/X9739fhw8flmEYMk1TlZWV+vDDD71enlKzgW1N+SFDhmjNmjUaOHBgc7vmCM9gRH0bA0vVs2e++uorKx0XF+fVMqy4uDgNGDBA6enpkqTjx4+ruLi43lkv33zzjfLz86306NGjvdqrJTU1VSEhIdaslX379ul73/tevXn37dtnS48ZM6bJ+mvyffTRR7Z6hg4d6lVZAAAAAACCVbNmyNQYOXKk9uzZo//3//6fda32EhbTNBv8V1/+O+64Q3v27NGoUaNa0i1HlJeX29INzbI5ffq0SktLrbQvgaYBAwZYj6uqqqzgjCfP67XLNSYsLMx28tGpU6dsfW2sDW9fR0pKim2mDzNkAAAAAABoYUBGql4e8+qrr+rQoUO66667FBERUW/QpeZfjZo8ERERuuuuu3Tw4EH94Q9/8MtpTU7w3G8lNja23nxnz561pX05+rlfv36N1lXjzJkzjZbzdxuhoaFKTEz0qv6IiAj16NHDVn/tsQEAAAAAQEfU7CVLngYPHqyVK1fq2Wef1a5du/Txxx9r3759ysnJ0fnz51VUVKQuXbqoW7du6t69u1JTUzVlyhRddtllCgsL81c3HLNz505buqFlOJ4Bjm7dunndhmfehoIlLWnDc5+es2fP1plhU1lZqZycHFv9jW3m66lbt27KysqSVH0kdm5ubr2nPwEAAAAA0FH4LSBTIywsTJMnT9bkyZP9XXXAOHLkiI4cOWKlIyMjNXz48Hrz1t7bRWpZsKSho7Vrt+FyudS1a1e/tlFQUKCqqqoGy/jaRn5+PgEZAAAAAECH1uIlSx1NRUWFXn/9ddu16dOn2453rs1zr5nw8HCv2/LMe+HChXrz1b4eHh7u0+wVz37X14bnNV9eQ31teL4nAAAAAAB0NARkfLR69Wpr+Y0k9ezZUzNmzGgwv2cww5vTjxrK601Axpf6vW2jJa9Bkjp1sk/Eauh1AAAAAADQURCQ8cH777+v7du3W+lOnTppwYIFjQYo3G63Ld3QaUz18QxkVFRUNNmGL/XX14Znf+u75lmmKZ7vT31tAAAAAADQkRCQ8dKePXu0Zs0a27Xbb79d/fv3b7ScZzCisrLS6zYvXrxoSze0+XHtNnypv7426gsueV7zLNMUzwCMrzNsAAAAAAAINgRkvHDw4EG9+uqrtuOa582bp4kTJzZZ1nO/FV9mh3jmbWjvltrXfZ194k0bLXkNUt0Ajq970AAAAAAAEGwIyDThxIkTWrFihS2oMGPGDM2aNcur8i3Z0NbbzXRrX79w4YLtRKSmeLPpsOc1Xzfl9czf0AbItbndbpWVlVlly8vLVVZWxnInAAAAAEBQ8Pux18Hk9OnTevbZZ22BkcmTJ+umm27yuo7Y2FhbuqGjq+tz/vx5W7qh46ZjY2OVk5MjSaqqqlJRUZFiYmK8asOzP/W1ERMTI5fLZQV6PI/y9rUNz/ekPu+99542bNhgpZcsWSJJmj1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\n", 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" ] @@ -1700,27 +1733,76 @@ "#plt.title(\"Distribution of predicted probabilities\", fontsize= 30, y= 1)\n", "\n", "\n", - "plt.hist(y_test_pred, density = False, bins= 30, rwidth = 0.8, color= \"navy\")\n", - "plt.hist(y_test_pred[wrong_predictions], density = False, bins= 30, rwidth = 0.8, color= \"red\")\n", + "plt.hist(y_test_pred, density = False, bins= 30, rwidth = 0.8, color= \"navy\", label = \"true predictions\")\n", + "plt.hist(y_test_pred[wrong_predictions], density = False, bins= 30, rwidth = 0.8, color= \"red\", label = \"false predictions\")\n", "\n", "\n", - "plt.xlabel('Predicted probabilities')\n", + "plt.xlabel('Prediction score')\n", "plt.ylabel('Count')\n", - "ax.yaxis.set_label_coords(-0.08, 0.5)\n", + "ax.yaxis.set_label_coords(-0.13, 0.5)\n", "ax.xaxis.set_label_coords(0.5,-0.13)\n", "\n", "ticks1 = [1000,2000,3000,4000]\n", - "\n", "ax.set_yticks(ticks1)\n", - "ax.set_yticklabels([\"1\", \"2\", \"3\", \"4\"])\n", + "ax.set_yticklabels([\"1000\", \"2000\", \"3000\", \"4000\"])\n", "ax.tick_params(axis='x', which=\"major\", length=10)\n", "\n", - "\n", - "#plt.yscale('log')\n", + "plt.legend(loc = \"upper center\", fontsize=18)\n", "\n", "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "#plt.rc('font', **font)\n", + "\n", + "fig, ax = plt.subplots(figsize= (12,6))\n", + "#plt.title(\"Distribution of predicted probabilities\", fontsize= 30, y= 1)\n", + "\n", + "\n", + "\n", + "plt.hist(y_test_pred, density = False, bins= 30, rwidth = 0.8, color= \"navy\", label = \"true predictions\")\n", + "plt.hist(y_test_pred[wrong_predictions], density = False, bins= 30, rwidth = 0.8, color= \"red\", label = \"false predictions\")\n", + "\n", + "\n", + "\n", + "\n", + "ax.xaxis.set_label_coords(0.5,-0.13)\n", + "ax.tick_params(axis='x', which=\"major\", length=10)\n", + "\n", + "ticks1 = [0,300]\n", + "ax.set_yticks(ticks1)\n", + "ax.set_yticklabels([\"0\", \"300\"])\n", + "\n", + "\n", + "\n", + "\n", + "plt.xlim([0.20, 0.80])\n", + "plt.ylim([0, 300])\n", + "#ax.get_yaxis().set_visible(False)\n", + "\n", + "plt.show()\n", + "\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1730,7 +1812,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1743,12 +1825,14 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 39, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1773,18 +1857,86 @@ "\n", "ax.tick_params(axis='y', length=10)\n", "ax.tick_params(axis='x',length=10)\n", - "plt.xlabel(\"Percentage of used training data\")\n", - "plt.ylabel(\"Accuracy / ROC-AUC score\")\n", + "plt.xlabel(\"Fraction of training data used\")\n", + "plt.ylabel(\"Accuracy or ROC-AUC score\")\n", "ax.xaxis.set_label_coords(0.5, -0.11)\n", "ax.yaxis.set_label_coords( -0.15, 0.5)\n", "plt.legend(loc = \"lower right\")\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Spearman rank correlation coefficient:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SpearmanrResult(correlation=0.9761904761904763, pvalue=3.3143960262001043e-05)" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import scipy\n", + "scipy.stats.spearmanr(a = perc_train_UIDs, b=accuracies)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9529478458049888" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "0.9761904761904763**2" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SpearmanrResult(correlation=1.0, pvalue=0.0)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scipy.stats.spearmanr(a = perc_train_UIDs, b=roc_auc_scores)" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1798,7 +1950,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.8.13" } }, "nbformat": 4, diff --git a/notebooks_and_code/4_0 - Training Graph Neural Network (pretraining KM prediction).ipynb b/notebooks_and_code/4_0 - Training Graph Neural Network (pretraining KM prediction).ipynb new file mode 100644 index 0000000..f254ac4 --- /dev/null +++ b/notebooks_and_code/4_0 - Training Graph Neural Network (pretraining KM prediction).ipynb @@ -0,0 +1,793 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "from os.path import join\n", + "import numpy as np\n", + "import pickle\n", + "import random\n", + "import pandas as pd\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.utils.data import DataLoader\n", + "from torch.utils.data import Dataset\n", + "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "print(device)\n", + "\n", + "sys.path.append('.\\\\additional_code')\n", + "from xgboost_training_KM import *\n", + "\n", + "CURRENT_DIR = os.getcwd()\n", + "print(CURRENT_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Loading and preprocessing data:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"KM\", \"training_data_KM_new_with_unchecked_data.pkl\"))\n", + "df_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"KM\", \"test_data_KM_new_with_unchecked_data.pkl\"))\n", + "\n", + "df_train.rename(columns = {\"KEGG ID\" : \"molecule ID\"}, inplace = True)\n", + "df_test.rename(columns = {\"KEGG ID\" : \"molecule ID\"}, inplace = True)\n", + "\n", + "df_train[\"Uniprot ID\"] = [\"Enzyme:train:\" + str(ind) for ind in df_train.index]\n", + "df_test[\"Uniprot ID\"] = [\"Enzyme:test:\" + str(ind) for ind in df_test.index]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (a) Create dictionary with all target values" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "mol_files = list(set(df_train[\"molecule ID\"])) + list(set(df_test[\"molecule ID\"]))\n", + "mol_files = list(set(mol_files))\n", + "\n", + "target_variable_dict_KM = {}\n", + "target_variable_dict_KM = create_target_dict_KM(df = df_train, target_variable_dict = target_variable_dict_KM)\n", + "target_variable_dict_KM = create_target_dict_KM(df = df_test, target_variable_dict = target_variable_dict_KM)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (c) Get list with input combinations of Uniprot ID and metabolite ID" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7580 812\n" + ] + } + ], + "source": [ + "train_IDs = get_uid_cid_IDs(df_train)\n", + "test_IDs = get_uid_cid_IDs(df_test)\n", + "\n", + "print(len(train_IDs), len(test_IDs))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculating input matrices for metabolites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (a) Creating input matrices:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "calculate_atom_and_bond_feature_vectors(mol_files = mol_files)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "More than 70 (75) atoms in molcuele C21471\n", + "Could not create input for substrate ID C21471\n", + "More than 70 (90) atoms in molcuele C06509\n", + "Could not create input for substrate ID C06509\n", + "More than 70 (113) atoms in molcuele C06510\n", + "Could not create input for substrate ID C06510\n", + "More than 70 (77) atoms in molcuele C04702\n", + "Could not create input for substrate ID C04702\n", + "More than 70 (96) atoms in molcuele C02015\n", + "Could not create input for substrate ID C02015\n", + "More than 70 (130) atoms in molcuele C05893\n", + "Could not create input for substrate ID C05893\n", + "More than 70 (91) atoms in molcuele C00853\n", + "Could not create input for substrate ID C00853\n", + "More than 70 (91) atoms in molcuele C00541\n", + "Could not create input for substrate ID C00541\n" + ] + } + ], + "source": [ + "for mol_ID in mol_files:\n", + " calculate_and_save_input_matrixes(molecule_ID = mol_ID)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (b) Removing all datapoints without molecule input file:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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molecule IDESM1bECFPRDKit FPMACCS FPPMIDMWLogPlog10_KMcheckedGNN FPUniprot ID
0C00387[0.100948475, 0.23829113, 0.0027401948, 0.0371...0000000000000000000000000000000000000000010000...1010111010101011101111011100011011000100100110...0000000000000000000000000100000000000010000100...17918964.0283.091669-2.6867-0.728158True[13.362184, 70.41528, 2.7784162, 60.74657, 0.0...Enzyme:train:0
1C00143[-0.09477718, 0.16472308, 0.09403025, 0.007433...0100000000000000000000000000000000000000000000...1111111000110111101101111011100011001011011110...0000000000000000000000000100100000000010000100...21858212.0457.170981-0.5219-0.744727True[18.767265, 151.67131, 25.194078, 66.9493, 0.6...Enzyme:train:1
2C00756[0.12043195, 0.17901447, -0.003300894, 0.07185...0000000000000000000000000000000001000000000000...0000000000000000000000000000000000000000000000...0000000000000000000000000000000000000000000000...19383697.0130.1357652.33920.588832True[0.053105697, 23.302288, 3.7088723, 5.9439626,...Enzyme:train:2
3C00002[0.068544716, 0.23684321, 0.080181114, -0.0251...0000000001000000000000000000000000000000000000...1010111010101011101011111000111010011100100111...0000000000000000000000000000010000000010000100...19509290.0506.995745-1.6290-0.709965True[15.331518, 103.84776, 6.569991, 63.609444, 0....Enzyme:train:3
4C00083[-0.062576994, 0.30821875, 0.101220384, -0.011...0100000001000100000000000000000001000000010000...1010111010101011101011111011111010011100111111...0000000000000000000000000000010000000010000100...17292360.0853.115603-1.8606-2.246545True[19.037132, 187.85568, 16.434797, 90.37692, 1....Enzyme:train:4
.......................................
7575C20925[-0.12106511, 0.16286044, -0.05657043, 0.00162...0100000000000010000000000000000001000000010000...0000000000000011100011000011000011000000001000...0000000000000000000000000000000000000000000000...NaN390.175064-2.1652-1.000000False[14.411318, 104.04242, 21.408749, 26.555807, 2...Enzyme:train:7575
7576C21181[-0.009757707, 0.1251226, 0.011750575, -0.0227...0100000000000000000000000000000000000000000000...0000000010000000000000000000000000000000000000...0000000000000000000000000000000000000001100000...NaN153.993594-1.5660-0.853872FalseEnzyme:train:7576
7577C21310[-0.0037425177, 0.06174834, -0.05052497, 0.063...0000000000000100000000000000000000001000000000...1011111011011111101111111101101011111111111111...0000000000000000000000000100010000000010000100...NaN522.990660-2.9161-3.102373False[12.673787, 120.78082, 6.8376884, 88.71222, 0....Enzyme:train:7577
7578C21563[0.028141364, 0.16967583, -0.118034706, 0.1133...0100010000000000000000000000000000000000010000...0100000100001010100001000011001110000001001101...0000000000000000000000000000000000000000000000...NaN415.104936-0.9324-0.366532False[10.382019, 105.68732, 18.721575, 34.91598, 2....Enzyme:train:7578
7579C21737[-0.017929962, 0.2529225, -0.14529729, -0.0213...1000000000000000000000000000000000000000000000...0000000000010000000000000000000000000000000000...0000000000000010000000000000000000000000000000...NaN238.0333512.0974-0.221849FalseEnzyme:train:7579
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7569 rows × 12 columns

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" + ], + "text/plain": [ + " molecule ID ESM1b \\\n", + "0 C00387 [0.100948475, 0.23829113, 0.0027401948, 0.0371... \n", + "1 C00143 [-0.09477718, 0.16472308, 0.09403025, 0.007433... \n", + "2 C00756 [0.12043195, 0.17901447, -0.003300894, 0.07185... \n", + "3 C00002 [0.068544716, 0.23684321, 0.080181114, -0.0251... \n", + "4 C00083 [-0.062576994, 0.30821875, 0.101220384, -0.011... \n", + "... ... ... \n", + "7575 C20925 [-0.12106511, 0.16286044, -0.05657043, 0.00162... \n", + "7576 C21181 [-0.009757707, 0.1251226, 0.011750575, -0.0227... \n", + "7577 C21310 [-0.0037425177, 0.06174834, -0.05052497, 0.063... \n", + "7578 C21563 [0.028141364, 0.16967583, -0.118034706, 0.1133... \n", + "7579 C21737 [-0.017929962, 0.2529225, -0.14529729, -0.0213... \n", + "\n", + " ECFP \\\n", + "0 0000000000000000000000000000000000000000010000... \n", + "1 0100000000000000000000000000000000000000000000... \n", + "2 0000000000000000000000000000000001000000000000... \n", + "3 0000000001000000000000000000000000000000000000... \n", + "4 0100000001000100000000000000000001000000010000... \n", + "... ... \n", + "7575 0100000000000010000000000000000001000000010000... \n", + "7576 0100000000000000000000000000000000000000000000... \n", + "7577 0000000000000100000000000000000000001000000000... \n", + "7578 0100010000000000000000000000000000000000010000... \n", + "7579 1000000000000000000000000000000000000000000000... \n", + "\n", + " RDKit FP \\\n", + "0 1010111010101011101111011100011011000100100110... \n", + "1 1111111000110111101101111011100011001011011110... \n", + "2 0000000000000000000000000000000000000000000000... \n", + "3 1010111010101011101011111000111010011100100111... \n", + "4 1010111010101011101011111011111010011100111111... \n", + "... ... \n", + "7575 0000000000000011100011000011000011000000001000... \n", + "7576 0000000010000000000000000000000000000000000000... \n", + "7577 1011111011011111101111111101101011111111111111... \n", + "7578 0100000100001010100001000011001110000001001101... \n", + "7579 0000000000010000000000000000000000000000000000... \n", + "\n", + " MACCS FP PMID \\\n", + "0 0000000000000000000000000100000000000010000100... 17918964.0 \n", + "1 0000000000000000000000000100100000000010000100... 21858212.0 \n", + "2 0000000000000000000000000000000000000000000000... 19383697.0 \n", + "3 0000000000000000000000000000010000000010000100... 19509290.0 \n", + "4 0000000000000000000000000000010000000010000100... 17292360.0 \n", + "... ... ... \n", + "7575 0000000000000000000000000000000000000000000000... NaN \n", + "7576 0000000000000000000000000000000000000001100000... NaN \n", + "7577 0000000000000000000000000100010000000010000100... NaN \n", + "7578 0000000000000000000000000000000000000000000000... NaN \n", + "7579 0000000000000010000000000000000000000000000000... NaN \n", + "\n", + " MW LogP log10_KM checked \\\n", + "0 283.091669 -2.6867 -0.728158 True \n", + "1 457.170981 -0.5219 -0.744727 True \n", + "2 130.135765 2.3392 0.588832 True \n", + "3 506.995745 -1.6290 -0.709965 True \n", + "4 853.115603 -1.8606 -2.246545 True \n", + "... ... ... ... ... \n", + "7575 390.175064 -2.1652 -1.000000 False \n", + "7576 153.993594 -1.5660 -0.853872 False \n", + "7577 522.990660 -2.9161 -3.102373 False \n", + "7578 415.104936 -0.9324 -0.366532 False \n", + "7579 238.033351 2.0974 -0.221849 False \n", + "\n", + " GNN FP Uniprot ID \n", + "0 [13.362184, 70.41528, 2.7784162, 60.74657, 0.0... Enzyme:train:0 \n", + "1 [18.767265, 151.67131, 25.194078, 66.9493, 0.6... Enzyme:train:1 \n", + "2 [0.053105697, 23.302288, 3.7088723, 5.9439626,... Enzyme:train:2 \n", + "3 [15.331518, 103.84776, 6.569991, 63.609444, 0.... Enzyme:train:3 \n", + "4 [19.037132, 187.85568, 16.434797, 90.37692, 1.... Enzyme:train:4 \n", + "... ... ... \n", + "7575 [14.411318, 104.04242, 21.408749, 26.555807, 2... Enzyme:train:7575 \n", + "7576 Enzyme:train:7576 \n", + "7577 [12.673787, 120.78082, 6.8376884, 88.71222, 0.... Enzyme:train:7577 \n", + "7578 [10.382019, 105.68732, 18.721575, 34.91598, 2.... Enzyme:train:7578 \n", + "7579 Enzyme:train:7579 \n", + "\n", + "[7569 rows x 12 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "valid_mols = os.listdir(join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data_KM\", \"GNN_input_matrices\"))\n", + "valid_mols = [mol.split(\"_A\")[0] for mol in valid_mols]\n", + "\n", + "df_train = df_train.loc[df_train[\"molecule ID\"].isin(valid_mols)]\n", + "df_test = df_test.loc[df_test[\"molecule ID\"].isin(valid_mols)]\n", + "\n", + "train_IDs = get_uid_cid_IDs(df_train)\n", + "test_IDs = get_uid_cid_IDs(df_test)\n", + "df_train" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (c) Creating representations for the enzymes:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "uids_list = list(set(df_train[\"Uniprot ID\"])) + list(set(df_test[\"Uniprot ID\"]))\n", + "uids_list = list(set(uids_list))\n", + "uid_to_emb = {}\n", + "embeddings = np.zeros((0,1280))\n", + "for uid in uids_list:\n", + " try:\n", + " emb = np.reshape(np.array(list(df_train[\"ESM1b\"].loc[df_train[\"Uniprot ID\"] == uid])[0]), (1,1280))\n", + " except IndexError:\n", + " try:\n", + " emb = np.reshape(np.array(list(df_test[\"ESM1b\"].loc[df_test[\"Uniprot ID\"] == uid])[0]), (1,1280))\n", + " except IndexError:\n", + " emb = np.reshape(np.array(list(df_validation[\"ESM1b\"].loc[df_validation[\"Uniprot ID\"] == uid])[0]), (1,1280))\n", + " embeddings = np.concatenate([embeddings, emb])\n", + " uid_to_emb[uid] = emb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We perform a PCA an the enzyme representations to get 50-dimensional representations" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "dim = 50\n", + "\n", + "pca = PCA(n_components = dim)\n", + "pca.fit(embeddings)\n", + "emb_pca = pca.transform(embeddings)\n", + "\n", + "#Calculate mean and std to normalize the PCA-transformed vectors\n", + "mean = np.mean(emb_pca, axis = 0)\n", + "std = np.std(emb_pca, axis = 0)\n", + "\n", + "uid_to_pca_emb = {}\n", + "\n", + "for i, uid in enumerate(uids_list):\n", + " uid_to_pca_emb[uid] = (emb_pca[i] - mean) / std" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "uid_to_emb = uid_to_pca_emb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training GNN:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (a) Defining a DataGenerator:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "class CustomDataSet(Dataset):\n", + " def __init__(self, split_IDs, folder):\n", + " self.all_IDs = split_IDs\n", + " self.folder = folder\n", + "\n", + " def __len__(self):\n", + " return len(self.all_IDs)\n", + "\n", + " def __getitem__(self, idx):\n", + " ID = self.all_IDs[idx]\n", + " try:\n", + " [uid,cid1, cid2] = ID.split(\"_\") \n", + " cid = cid1 +\"_\"+cid2\n", + " except ValueError:\n", + " [uid,cid] = ID.split(\"_\")\n", + " \n", + " XE = torch.tensor(np.load(join(self.folder, cid + '_XE.npy')), dtype = torch.float32)\n", + " X = torch.tensor(np.load(join(self.folder, cid + '_X.npy')), dtype = torch.float32)\n", + " A = torch.tensor(np.load(join(self.folder, cid + '_A.npy')), dtype = torch.float32)\n", + " ESM1b = torch.tensor(uid_to_emb[uid], dtype = torch.float32)\n", + " label = torch.tensor(target_variable_dict_KM[ID], dtype= torch.float32)\n", + " return XE,X,A,ESM1b, label" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (b) Splitting the training set in a validation and a training set:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "n = len(train_IDs) \n", + "random.seed(1)\n", + "random.shuffle(train_IDs)\n", + "test_IDs = train_IDs[int(0.8*n):]\n", + "train_IDs = train_IDs[:int(0.8*n)]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 64\n", + "\n", + "train_dataset = CustomDataSet(folder = join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data_KM\",\n", + " \"GNN_input_matrices\"), split_IDs = train_IDs)\n", + "train_loader = DataLoader(train_dataset , batch_size=batch_size, shuffle=True, drop_last=True)\n", + "\n", + "test_dataset = CustomDataSet(folder = join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data_KM\",\n", + " \"GNN_input_matrices\"), split_IDs = test_IDs)\n", + "test_loader = DataLoader(test_dataset , batch_size=batch_size, shuffle=False, drop_last=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "n_train_batches = int(len(train_dataset)/batch_size)\n", + "n_test_batches = int(len(test_dataset)/batch_size)\n", + "train_batches = list(range(n_train_batches))\n", + "test_batches = list(range(n_test_batches))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (c) Training GNN:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 20] loss: 1.585\n", + "[1, 40] loss: 1.272\n", + "[1, 60] loss: 1.098\n", + "Epoch: 0, Val. loss: 1.05, Val. mse: 1.05, Val R2: 0.0\n", + "[2, 20] loss: 1.021\n", + "[2, 40] loss: 0.965\n", + "[2, 60] loss: 0.989\n", + "Epoch: 1, Val. loss: 1.0, Val. mse: 1.0, Val R2: 0.0\n", + "[3, 20] loss: 0.930\n", + "[3, 40] loss: 0.974\n", + "[3, 60] loss: 0.894\n", + "Epoch: 2, Val. loss: 0.93, Val. mse: 0.93, Val R2: 0.0\n", + "[4, 20] loss: 0.857\n", + "[4, 40] loss: 0.882\n", + "[4, 60] loss: 0.933\n", + "Epoch: 3, Val. loss: 0.92, Val. mse: 0.92, Val R2: 0.0\n", + "[5, 20] loss: 0.826\n", + "[5, 40] loss: 0.885\n", + "[5, 60] loss: 0.835\n", + "Epoch: 4, Val. loss: 0.9, Val. mse: 0.9, Val R2: 0.0\n", + "[6, 20] loss: 0.862\n", + "[6, 40] loss: 0.820\n", + "[6, 60] loss: 0.782\n", + "Epoch: 5, Val. loss: 0.89, Val. mse: 0.89, Val R2: 0.0\n", + "[7, 20] loss: 0.815\n", + "[7, 40] loss: 0.849\n", + "[7, 60] loss: 0.808\n", + "Epoch: 6, Val. loss: 0.92, Val. mse: 0.92, Val R2: 0.0\n", + "[8, 20] loss: 0.770\n", + "[8, 40] loss: 0.735\n", + "[8, 60] loss: 0.828\n", + "Epoch: 7, Val. loss: 0.87, Val. mse: 0.87, Val R2: 0.0\n", + "[9, 20] loss: 0.766\n", + "[9, 40] loss: 0.812\n", + "[9, 60] loss: 0.752\n", + "Epoch: 8, Val. loss: 0.88, Val. mse: 0.88, Val R2: 0.0\n", + "[10, 20] loss: 0.714\n", + "[10, 40] loss: 0.753\n", + "[10, 60] loss: 0.766\n", + "Epoch: 9, Val. loss: 0.9, Val. mse: 0.9, Val R2: 0.0\n", + "Finished Training\n" + ] + } + ], + "source": [ + "import torch.optim as optim\n", + "\n", + "model = GNN(D= 100, N = 70, F1 = 32 , F2 = 10, F = F1+F2).to(device)\n", + "criterion = nn.MSELoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay= 0.00001)\n", + "\n", + "for epoch in range(10): # loop over the dataset multiple times\n", + " model.train()\n", + " running_loss = 0.0\n", + " for i, [XE, X, A,ESM1b, labels] in enumerate(train_loader):\n", + " # zero the parameter gradients\n", + " optimizer.zero_grad()\n", + " XE, X, A, ESM1b, labels = XE.to(device), X.to(device), A.to(device),ESM1b.to(device), labels.to(device)\n", + " # forward + backward + optimize\n", + " outputs = model(XE, X, A, ESM1b)\n", + " loss = criterion(outputs, labels.view((batch_size,-1)))\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # print statistics\n", + " running_loss += loss.item()\n", + " if i % 20 == 19: # print every 2000 mini-batches\n", + " print('[%d, %5d] loss: %.3f' %\n", + " (epoch + 1, i + 1, running_loss / 20))\n", + " running_loss = 0.0\n", + " \n", + " #After each epoch, calculate the validation loss:\n", + " running_mse = 0.0\n", + " running_r2 = 0.0\n", + " running_loss = 0.0\n", + " model.eval()\n", + " for i, [XE, X, A,ESM1b, labels] in enumerate(test_loader):\n", + " XE, X, A, ESM1b, labels = XE.to(device), X.to(device), A.to(device),ESM1b.to(device), labels.to(device)\n", + " \n", + " with torch.no_grad():\n", + " outputs = model(XE, X, A, ESM1b)\n", + " loss = criterion(outputs, labels.view((batch_size,-1)))\n", + " running_loss += loss.item()\n", + "\n", + " outputs2 = outputs.view(-1).cpu().detach().numpy()\n", + " labels2 = labels.cpu().detach().numpy()\n", + " mse = np.mean((np.array(outputs2) - np.array(labels2))**2)\n", + " R2 = r2_score(np.array(labels2), np.array(outputs2))\n", + " running_mse += mse\n", + " running_r2 += R2\n", + "\n", + " print(\"Epoch: %s, Val. loss: %s, Val. mse: %s, Val R2: %s\" % (epoch, np.round(running_loss/(i+1),2),\n", + " np.round(running_mse/(i+1), 2), \n", + " np.round(running_r2/(i+1))))\n", + "\n", + "print('Finished Training')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(model.state_dict(),join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data_KM\", \"GNN\", \"Pytorch_GNN_KM\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks_and_code/4_0 - Training Graph Neural Network.ipynb b/notebooks_and_code/4_1 - Training Graph Neural Network.ipynb similarity index 54% rename from notebooks_and_code/4_0 - Training Graph Neural Network.ipynb rename to notebooks_and_code/4_1 - Training Graph Neural Network.ipynb index 931fcea..ccbd1a3 100644 --- a/notebooks_and_code/4_0 - Training Graph Neural Network.ipynb +++ b/notebooks_and_code/4_1 - Training Graph Neural Network.ipynb @@ -9,8 +9,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "cuda:0\n", - "C:\\Users\\alexk\\projects\\SubFinder\\notebooks_and_code\n" + "cpu\n", + "C:\\Users\\alexk\\projects\\ESP\\notebooks_and_code\n" ] } ], @@ -50,10 +50,7 @@ "outputs": [], "source": [ "df_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\",\"splits\", \"df_train_with_ESM1b_ts.pkl\"))\n", - "df_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"splits\", \"df_test_with_ESM1b_ts.pkl\"))\n", - "\n", - "df_train = df_train.loc[df_train[\"evidence\"] == \"exp\"]\n", - "df_test = df_test.loc[df_test[\"evidence\"] == \"exp\"]" + "df_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"splits\", \"df_test_with_ESM1b_ts.pkl\"))" ] }, { @@ -121,15 +118,15 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.5003414600833163 0.5005673758865248\n", - "29286 7050\n" + "0.5 0.5\n", + "29480 7068\n" ] } ], @@ -157,23 +154,111 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_and_save_input_matrixes(molecule_ID, save_folder = join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data\",\n", + " \"GNN_input_matrices\")):\n", + " molecule_ID = molecule_ID.replace(\":\", \"_\")\n", + " molecule_ID = molecule_ID.replace(\"/\", \"Q\")\n", + " [XE, X, A] = create_input_data_for_GNN_for_substrates(substrate_ID = molecule_ID, print_error=True)\n", + " if not A is None:\n", + " np.save(join(save_folder, molecule_ID + '_X.npy'), X) #feature matrix of atoms/nodes\n", + " np.save(join(save_folder, molecule_ID + '_XE.npy'), XE) #feature matrix of atoms/nodes and bonds/edges\n", + " np.save(join(save_folder, molecule_ID + '_A.npy'), A) #adjacency matrix\n", + " \n", + " \n", + "def calculate_atom_and_bond_feature_vectors(mol_files):\n", + " #check if feature vectors have already been calculated:\n", + " try:\n", + " os.mkdir(join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data\", \"mol_feature_vectors\"))\n", + " except FileExistsError:\n", + " None\n", + " \n", + " #existing feature vector files:\n", + " feature_files = os.listdir(join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data\", \"mol_feature_vectors\"))\n", + " for mol_file in mol_files:\n", + " #check if feature vectors were already calculated:\n", + " try:\n", + " if not mol_file + \"-atoms.txt\" in feature_files:\n", + " #load mol_file\n", + " is_CHEBI_ID = (mol_file[0:5] == \"CHEBI\")\n", + " is_inchi = (mol_file[0:5] == \"InChI\")\n", + " if is_CHEBI_ID:\n", + " ID = int(mol_file.split(\" \")[0].split(\":\")[-1])\n", + " Inchi = list(df_chebi_to_inchi[\"Inchi\"].loc[df_chebi_to_inchi[\"ChEBI\"] == float(ID)])[0]\n", + "\n", + " if not pd.isnull(Inchi):\n", + " mol = Chem.inchi.MolFromInchi(Inchi)\n", + " else:\n", + " print(ID, Inchi)\n", + " elif is_inchi:\n", + " mol = Chem.inchi.MolFromInchi(mol_file)\n", + " mol_file = mol_file.replace(\"/\", \"Q\")\n", + " else:\n", + " mol = Chem.MolFromMolFile(mol_folder + \"/mol-files/\" + mol_file + '.mol')\n", + " \n", + " if not mol is None:\n", + " calculate_atom_feature_vector_for_mol_file(mol, mol_file)\n", + " calculate_bond_feature_vector_for_mol_file(mol, mol_file)\n", + " except OSError: pass" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "#calculate_atom_and_bond_feature_vectors(mol_files = mol_files)" + "calculate_atom_and_bond_feature_vectors(mol_files = mol_files)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "More than 70 (91) atoms in molcuele CHEBI_58466\n", + "Could not create input for substrate ID CHEBI_58466\n", + "More than 70 (70) atoms in molcuele CHEBI_83905\n", + "Could not create input for substrate ID CHEBI_83905\n", + "More than 70 (90) atoms in molcuele CHEBI_58502\n", + "Could not create input for substrate ID CHEBI_58502\n", + "More than 70 (76) atoms in molcuele CHEBI_57373\n", + "Could not create input for substrate ID CHEBI_57373\n", + "More than 70 (116) atoms in molcuele CHEBI_60032\n", + "Could not create input for substrate ID CHEBI_60032\n", + "More than 70 (79) atoms in molcuele CHEBI_58677\n", + "Could not create input for substrate ID CHEBI_58677\n", + "More than 70 (166) atoms in molcuele CHEBI_61502\n", + "Could not create input for substrate ID CHEBI_61502\n", + "More than 70 (194) atoms in molcuele CHEBI_61998\n", + "Could not create input for substrate ID CHEBI_61998\n", + "More than 70 (194) atoms in molcuele C00770\n", + "Could not create input for substrate ID C00770\n", + "More than 70 (70) atoms in molcuele CHEBI_18259\n", + "Could not create input for substrate ID CHEBI_18259\n", + "More than 70 (130) atoms in molcuele CHEBI_78435\n", + "Could not create input for substrate ID CHEBI_78435\n", + "More than 70 (91) atoms in molcuele CHEBI_16304\n", + "Could not create input for substrate ID CHEBI_16304\n", + "More than 70 (77) atoms in molcuele CHEBI_70758\n", + "Could not create input for substrate ID CHEBI_70758\n", + "More than 70 (125) atoms in molcuele CHEBI_60365\n", + "Could not create input for substrate ID CHEBI_60365\n" + ] + } + ], "source": [ - "#for mol_ID in mol_files:\n", - "# calculate_and_save_input_matrixes(molecule_ID = mol_ID)" + "for mol_ID in mol_files:\n", + " calculate_and_save_input_matrixes(molecule_ID = mol_ID)" ] }, { @@ -185,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -195,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -228,74 +313,68 @@ " ECFP\n", " ESM1b\n", " ESM1b_ts\n", - " GNN rep\n", " \n", " \n", " \n", " \n", " 0\n", - " Q5B2F7\n", - " CHEBI_57344\n", + " G8BBN0\n", + " CHEBI_35681\n", " exp\n", - " 1\n", + " 1.0\n", " NaN\n", - " CHEBI:57344\n", - " 0100000001000000000000000000000001000000000000...\n", - " [0.09207666, 0.18022089, 0.1191696, -0.0068351...\n", - " [-0.52362674, 0.5027057, -0.40282017, 0.742947...\n", - " [1577.9962, 10.317345, 29.326752, 233.01369, 4...\n", + " CHEBI:35681\n", + " 0100000000000000000000000000000001000000000000...\n", + " [-0.033332635, 0.35044205, -0.07861315, 0.0046...\n", + " [0.5721893, 0.56740093, 0.09789569, 0.8466092,...\n", " \n", " \n", " 1\n", - " Q9SAH9\n", - " CHEBI_58349\n", + " P78937\n", + " CHEBI_30616\n", " exp\n", - " 1\n", + " 1.0\n", " NaN\n", - " CHEBI:58349\n", - " 0000000001000000100000100000000000000000000000...\n", - " [0.022810845, 0.1272514, -0.051154055, -0.0810...\n", - " [0.61918294, 0.121414125, 0.40603346, 1.126637...\n", - " [2261.6094, 0.0, 0.0, 115.09651, 179.84134, 46...\n", + " CHEBI:30616\n", + " 0000000001000000000000000000000000000000000100...\n", + " [0.049317513, 0.11258735, -0.08035447, 0.04825...\n", + " [-0.56589794, -0.5028634, 0.2953197, -0.357490...\n", " \n", " \n", " 2\n", - " Q8IPJ6\n", - " CHEBI_57776\n", + " F4K688\n", + " CHEBI_30616\n", " exp\n", - " 1\n", + " 1.0\n", " NaN\n", - " CHEBI:57776\n", - " 0000000000000000000000000000010001000000000000...\n", - " [0.09814875, 0.22172487, 0.11138555, 0.0365497...\n", - " [0.29864457, 0.22536643, 0.27347004, -0.128196...\n", - " [791.13226, 7.796671, 0.0, 0.0, 4.66982, 10.69...\n", + " CHEBI:30616\n", + " 0000000001000000000000000000000000000000000100...\n", + " [-0.005019231, 0.06971764, -0.022618646, -0.03...\n", + " [0.3031646, 0.69172686, -1.0995013, 0.13241063...\n", " \n", " \n", " 3\n", - " A0A1D5PCZ1\n", - " C00002\n", + " Q9Z0J5\n", + " CHEBI_58349\n", " exp\n", - " 1\n", + " 1.0\n", " NaN\n", - " C00002\n", - " 0000000001000000000000000000000000000000000000...\n", - " [-0.21187752, 0.08564956, 0.055316914, -0.0550...\n", - " [-0.86605054, -0.38922024, -0.539311, 1.373580...\n", - " [1238.0188, 0.0, 0.0, 42.365837, 74.54658, 28....\n", + " CHEBI:58349\n", + " 0000000001000000100000100000000000000000000000...\n", + " [-0.15290919, 0.31520224, 0.025415594, 0.02750...\n", + " [0.118711345, 0.8216332, -0.9046953, 1.179861,...\n", " \n", " \n", " 4\n", - " O22765\n", - " CHEBI_33384\n", + " P49189\n", + " CHEBI_58264\n", " exp\n", - " 1\n", + " 1.0\n", " NaN\n", - " CHEBI:33384\n", - " 0100000000000000000000000000000000000000000000...\n", - " [0.027133903, 0.33383188, -0.0057643764, -0.00...\n", - " [1.1005167, -1.0289398, -0.061415985, 0.988528...\n", - " [72.62339, 18.489643, 0.0, 50.355515, 13.49715...\n", + " CHEBI:58264\n", + " 0000000000000000010000000000000000000000000000...\n", + " [-0.044796597, 0.24305029, 0.10043996, -0.0269...\n", + " [0.8842707, -0.06434063, 0.5387947, 1.6151128,...\n", " \n", " \n", " ...\n", @@ -308,148 +387,129 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", - " 29355\n", - " O54937\n", - " CHEBI_16199\n", - " exp\n", - " 0\n", + " 29475\n", + " C9Y9E7\n", + " CHEBI_16810\n", " NaN\n", - " CHEBI:16199\n", - " 0000000000000000000000000000000000000000000000...\n", - " [0.050787933, 0.20482497, -0.0821579, -0.03619...\n", - " [-0.6821743, -0.25235456, -0.06566423, 0.84851...\n", - " [10.734227, 0.0, 4.6557817, 1.7201436, 0.0, 0....\n", + " 0.0\n", + " engqvist\n", + " CHEBI:16810\n", + " 0000000000000000000000000000000010000000000000...\n", + " [0.1486952, 0.23952422, -0.18132365, 0.0853893...\n", + " [-0.5624707, 0.49068797, -0.78957033, 1.021208...\n", " \n", " \n", - " 29356\n", - " P42980\n", - " CHEBI_43474\n", - " exp\n", - " 0\n", + " 29476\n", + " C9Y9E7\n", + " CHEBI_17544\n", " NaN\n", - " CHEBI:43474\n", + " 0.0\n", + " engqvist\n", + " CHEBI:17544\n", " 0000000000000000000000000000000000000000000000...\n", - " [0.03450865, 0.10044937, -0.081294104, 0.03105...\n", - " [0.7604322, -0.6746883, 0.038595006, 0.1019296...\n", - " [32.170155, 0.0, 0.0, 0.0, 0.7738515, 47.04823...\n", + " [0.1486952, 0.23952422, -0.18132365, 0.0853893...\n", + " [-0.5624707, 0.49068797, -0.78957033, 1.021208...\n", " \n", " \n", - " 29357\n", - " P31254\n", - " CHEBI_30616\n", - " exp\n", - " 0\n", + " 29477\n", + " C9Y9E7\n", + " C00007\n", " NaN\n", - " CHEBI:30616\n", - " 0000000001000000000000000000000000000000000100...\n", - " [-0.10911206, 0.12464452, -0.006680568, 0.1137...\n", - " [0.5049492, 0.23488945, -0.7357721, 0.21344757...\n", - " [1288.9618, 0.0, 0.0, 76.52397, 73.09448, 37.6...\n", + " 0.0\n", + " engqvist\n", + " C00007\n", + " 0000000000000000100000000000000000000000000000...\n", + " [0.1486952, 0.23952422, -0.18132365, 0.0853893...\n", + " [-0.5624707, 0.49068797, -0.78957033, 1.021208...\n", " \n", " \n", - " 29358\n", - " C0HLL2\n", - " CHEBI_30616\n", - " exp\n", - " 0\n", + " 29478\n", + " D4MUV9\n", + " CHEBI_16810\n", " NaN\n", - " CHEBI:30616\n", - " 0000000001000000000000000000000000000000000100...\n", - " [0.087619156, 0.30014926, 0.051759467, 0.07981...\n", - " [1.0081663, -0.47126764, 0.106960185, -0.28055...\n", - " [1288.9618, 0.0, 0.0, 76.52397, 73.09448, 37.6...\n", + " 0.0\n", + " engqvist\n", + " CHEBI:16810\n", + " 0000000000000000000000000000000010000000000000...\n", + " [0.08790772, 0.17450011, -0.014648443, 0.06931...\n", + " [1.0554699, 0.441238, 0.19652943, 1.1101232, -...\n", " \n", " \n", - " 29359\n", - " Q8RVK9\n", - " C00002\n", - " exp\n", - " 0\n", + " 29479\n", + " D4MUV9\n", + " CHEBI_17478\n", " NaN\n", - " C00002\n", - " 0000000001000000000000000000000000000000000000...\n", - " [-0.0870039, 0.34124222, 0.20787948, -0.154150...\n", - " [-0.59816426, 0.41644707, -0.7390129, 1.428337...\n", - " [1238.0188, 0.0, 0.0, 42.365837, 74.54658, 28....\n", + " 0.0\n", + " engqvist\n", + " CHEBI:17478\n", + " 0000000000000000000000000000000000000000000000...\n", + " [0.08790772, 0.17450011, -0.014648443, 0.06931...\n", + " [1.0554699, 0.441238, 0.19652943, 1.1101232, -...\n", " \n", " \n", "\n", - "

29286 rows × 10 columns

\n", + "

29128 rows × 9 columns

\n", "" ], "text/plain": [ - " Uniprot ID molecule ID evidence Binding type substrate ID \\\n", - "0 Q5B2F7 CHEBI_57344 exp 1 NaN CHEBI:57344 \n", - "1 Q9SAH9 CHEBI_58349 exp 1 NaN CHEBI:58349 \n", - "2 Q8IPJ6 CHEBI_57776 exp 1 NaN CHEBI:57776 \n", - "3 A0A1D5PCZ1 C00002 exp 1 NaN C00002 \n", - "4 O22765 CHEBI_33384 exp 1 NaN CHEBI:33384 \n", - "... ... ... ... ... ... ... \n", - "29355 O54937 CHEBI_16199 exp 0 NaN CHEBI:16199 \n", - "29356 P42980 CHEBI_43474 exp 0 NaN CHEBI:43474 \n", - "29357 P31254 CHEBI_30616 exp 0 NaN CHEBI:30616 \n", - "29358 C0HLL2 CHEBI_30616 exp 0 NaN CHEBI:30616 \n", - "29359 Q8RVK9 C00002 exp 0 NaN C00002 \n", + " Uniprot ID molecule ID evidence Binding type substrate ID \\\n", + "0 G8BBN0 CHEBI_35681 exp 1.0 NaN CHEBI:35681 \n", + "1 P78937 CHEBI_30616 exp 1.0 NaN CHEBI:30616 \n", + "2 F4K688 CHEBI_30616 exp 1.0 NaN CHEBI:30616 \n", + "3 Q9Z0J5 CHEBI_58349 exp 1.0 NaN CHEBI:58349 \n", + "4 P49189 CHEBI_58264 exp 1.0 NaN CHEBI:58264 \n", + "... ... ... ... ... ... ... \n", + "29475 C9Y9E7 CHEBI_16810 NaN 0.0 engqvist CHEBI:16810 \n", + "29476 C9Y9E7 CHEBI_17544 NaN 0.0 engqvist CHEBI:17544 \n", + "29477 C9Y9E7 C00007 NaN 0.0 engqvist C00007 \n", + "29478 D4MUV9 CHEBI_16810 NaN 0.0 engqvist CHEBI:16810 \n", + "29479 D4MUV9 CHEBI_17478 NaN 0.0 engqvist CHEBI:17478 \n", "\n", " ECFP \\\n", - 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" ESM1b_ts \\\n", - "0 [-0.52362674, 0.5027057, -0.40282017, 0.742947... \n", - "1 [0.61918294, 0.121414125, 0.40603346, 1.126637... \n", - "2 [0.29864457, 0.22536643, 0.27347004, -0.128196... \n", - "3 [-0.86605054, -0.38922024, -0.539311, 1.373580... \n", - "4 [1.1005167, -1.0289398, -0.061415985, 0.988528... \n", - "... ... \n", - "29355 [-0.6821743, -0.25235456, -0.06566423, 0.84851... \n", - "29356 [0.7604322, -0.6746883, 0.038595006, 0.1019296... \n", - "29357 [0.5049492, 0.23488945, -0.7357721, 0.21344757... \n", - "29358 [1.0081663, -0.47126764, 0.106960185, -0.28055... \n", - "29359 [-0.59816426, 0.41644707, -0.7390129, 1.428337... \n", - "\n", - " GNN rep \n", - "0 [1577.9962, 10.317345, 29.326752, 233.01369, 4... \n", - "1 [2261.6094, 0.0, 0.0, 115.09651, 179.84134, 46... \n", - "2 [791.13226, 7.796671, 0.0, 0.0, 4.66982, 10.69... \n", - "3 [1238.0188, 0.0, 0.0, 42.365837, 74.54658, 28.... \n", - "4 [72.62339, 18.489643, 0.0, 50.355515, 13.49715... \n", + " ESM1b_ts \n", + "0 [0.5721893, 0.56740093, 0.09789569, 0.8466092,... \n", + "1 [-0.56589794, -0.5028634, 0.2953197, -0.357490... \n", + "2 [0.3031646, 0.69172686, -1.0995013, 0.13241063... \n", + "3 [0.118711345, 0.8216332, -0.9046953, 1.179861,... \n", + "4 [0.8842707, -0.06434063, 0.5387947, 1.6151128,... \n", "... ... \n", - 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"execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -550,7 +610,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -587,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -600,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -617,7 +677,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -636,205 +696,224 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "pre_training = True" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\torch\\autograd\\__init__.py:127: UserWarning: Mixed memory format inputs detected while calling the operator. The operator will output contiguous tensor even if some of the inputs are in channels_last format. (Triggered internally at ..\\aten\\src\\ATen\\native\\TensorIterator.cpp:918.)\n", + " allow_unreachable=True) # allow_unreachable flag\n", + "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\torch\\autograd\\__init__.py:127: UserWarning: Mixed memory format inputs detected while calling the operator. The operator will output channels_last tensor even if some of the inputs are not in channels_last format. (Triggered internally at ..\\aten\\src\\ATen\\native\\TensorIterator.cpp:924.)\n", + " allow_unreachable=True) # allow_unreachable flag\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "[1, 20] loss: 0.699\n", + "[1, 20] loss: 0.743\n", "[1, 40] loss: 0.693\n", - "[1, 60] loss: 0.692\n", - "[1, 80] loss: 0.683\n", - "[1, 100] loss: 0.691\n", - "[1, 120] loss: 0.693\n", - "[1, 140] loss: 0.689\n", - "[1, 160] loss: 0.681\n", - "[1, 180] loss: 0.691\n", - "[1, 200] loss: 0.686\n", - "[1, 220] loss: 0.682\n", - "[1, 240] loss: 0.683\n", - "[1, 260] loss: 0.675\n", - "[1, 280] loss: 0.680\n", - "[1, 300] loss: 0.684\n", + "[1, 60] loss: 0.707\n", + "[1, 80] loss: 0.690\n", + "[1, 100] loss: 0.694\n", + "[1, 120] loss: 0.682\n", + "[1, 140] loss: 0.690\n", + "[1, 160] loss: 0.688\n", + "[1, 180] loss: 0.682\n", + "[1, 200] loss: 0.676\n", + "[1, 220] loss: 0.686\n", + "[1, 240] loss: 0.675\n", + "[1, 260] loss: 0.681\n", + "[1, 280] loss: 0.674\n", + "[1, 300] loss: 0.679\n", "[1, 320] loss: 0.676\n", - 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"[7, 20] loss: 0.554\n", - "[7, 40] loss: 0.542\n", - "[7, 60] loss: 0.551\n", - "[7, 80] loss: 0.553\n", - "[7, 100] loss: 0.555\n", - "[7, 120] loss: 0.549\n", - "[7, 140] loss: 0.544\n", - "[7, 160] loss: 0.569\n", - "[7, 180] loss: 0.557\n", - "[7, 200] loss: 0.574\n", - "[7, 220] loss: 0.552\n", - "[7, 240] loss: 0.549\n", - "[7, 260] loss: 0.527\n", - "[7, 280] loss: 0.545\n", - "[7, 300] loss: 0.569\n", - "[7, 320] loss: 0.555\n", - "[7, 340] loss: 0.559\n", - "[7, 360] loss: 0.571\n", - "Epoch: 6, Val. loss: 0.55, Val. acc: 0.68\n", - "[8, 20] loss: 0.541\n", - "[8, 40] loss: 0.562\n", + "[7, 20] loss: 0.542\n", + "[7, 40] loss: 0.564\n", + "[7, 60] loss: 0.538\n", + "[7, 80] loss: 0.548\n", + "[7, 100] loss: 0.552\n", + "[7, 120] loss: 0.557\n", + "[7, 140] loss: 0.547\n", + "[7, 160] loss: 0.580\n", + "[7, 180] loss: 0.565\n", + "[7, 200] loss: 0.580\n", + "[7, 220] loss: 0.558\n", + "[7, 240] loss: 0.537\n", + "[7, 260] loss: 0.564\n", + "[7, 280] loss: 0.569\n", + "[7, 300] loss: 0.547\n", + "[7, 320] loss: 0.559\n", + "[7, 340] loss: 0.560\n", + "[7, 360] loss: 0.578\n", + "Epoch: 6, Val. loss: 0.56, Val. acc: 0.69\n", + "[8, 20] loss: 0.542\n", + "[8, 40] loss: 0.553\n", "[8, 60] loss: 0.540\n", - "[8, 80] loss: 0.536\n", - "[8, 100] loss: 0.537\n", - "[8, 120] loss: 0.527\n", - "[8, 140] loss: 0.547\n", - "[8, 160] loss: 0.551\n", - "[8, 180] loss: 0.557\n", - "[8, 200] loss: 0.525\n", - "[8, 220] loss: 0.539\n", - "[8, 240] loss: 0.561\n", - "[8, 260] loss: 0.563\n", - "[8, 280] loss: 0.546\n", - "[8, 300] loss: 0.549\n", - "[8, 320] loss: 0.531\n", - "[8, 340] loss: 0.537\n", - "[8, 360] loss: 0.543\n", - "Epoch: 7, Val. loss: 0.56, Val. acc: 0.7\n", - "[9, 20] loss: 0.532\n", - "[9, 40] loss: 0.548\n", - "[9, 60] loss: 0.533\n", - "[9, 80] loss: 0.536\n", - "[9, 100] loss: 0.517\n", + "[8, 80] loss: 0.551\n", + "[8, 100] loss: 0.544\n", + "[8, 120] loss: 0.537\n", + "[8, 140] loss: 0.559\n", + "[8, 160] loss: 0.542\n", + "[8, 180] loss: 0.552\n", + "[8, 200] loss: 0.536\n", + "[8, 220] loss: 0.551\n", + "[8, 240] loss: 0.556\n", + "[8, 260] loss: 0.540\n", + "[8, 280] loss: 0.573\n", + "[8, 300] loss: 0.543\n", + "[8, 320] loss: 0.555\n", + "[8, 340] loss: 0.550\n", + "[8, 360] loss: 0.539\n", + "Epoch: 7, Val. loss: 0.55, Val. acc: 0.7\n", + "[9, 20] loss: 0.551\n", + "[9, 40] loss: 0.562\n", + "[9, 60] loss: 0.548\n", + "[9, 80] loss: 0.557\n", + "[9, 100] loss: 0.554\n", "[9, 120] loss: 0.555\n", - "[9, 140] loss: 0.512\n", - "[9, 160] loss: 0.523\n", - "[9, 180] loss: 0.525\n", - "[9, 200] loss: 0.529\n", - "[9, 220] loss: 0.560\n", - "[9, 240] loss: 0.528\n", - "[9, 260] loss: 0.527\n", - "[9, 280] loss: 0.551\n", - "[9, 300] loss: 0.550\n", - "[9, 320] loss: 0.560\n", - "[9, 340] loss: 0.548\n", - "[9, 360] loss: 0.541\n", - "Epoch: 8, Val. loss: 0.54, Val. acc: 0.7\n", - "[10, 20] loss: 0.533\n", - "[10, 40] loss: 0.544\n", - "[10, 60] loss: 0.540\n", - "[10, 80] loss: 0.522\n", - "[10, 100] loss: 0.540\n", - "[10, 120] loss: 0.513\n", - "[10, 140] loss: 0.538\n", - "[10, 160] loss: 0.523\n", - "[10, 180] loss: 0.518\n", - "[10, 200] loss: 0.517\n", - "[10, 220] loss: 0.529\n", - "[10, 240] loss: 0.537\n", - "[10, 260] loss: 0.549\n", - "[10, 280] loss: 0.502\n", - "[10, 300] loss: 0.535\n", - "[10, 320] loss: 0.540\n", - "[10, 340] loss: 0.522\n", - "[10, 360] loss: 0.547\n", - "Epoch: 9, Val. loss: 0.53, Val. acc: 0.71\n", + "[9, 140] loss: 0.550\n", + "[9, 160] loss: 0.543\n", + "[9, 180] loss: 0.539\n", + "[9, 200] loss: 0.545\n", + "[9, 220] loss: 0.544\n", + "[9, 240] loss: 0.538\n", + "[9, 260] loss: 0.546\n", + "[9, 280] loss: 0.548\n", + "[9, 300] loss: 0.536\n", + "[9, 320] loss: 0.538\n", + "[9, 340] loss: 0.542\n", + "[9, 360] loss: 0.545\n", + "Epoch: 8, Val. loss: 0.54, Val. acc: 0.71\n", + "[10, 20] loss: 0.519\n", + "[10, 40] loss: 0.523\n", + "[10, 60] loss: 0.521\n", + "[10, 80] loss: 0.537\n", + "[10, 100] loss: 0.525\n", + "[10, 120] loss: 0.525\n", + "[10, 140] loss: 0.532\n", + "[10, 160] loss: 0.536\n", + "[10, 180] loss: 0.543\n", + "[10, 200] loss: 0.544\n", + "[10, 220] loss: 0.556\n", + "[10, 240] loss: 0.544\n", + "[10, 260] loss: 0.558\n", + "[10, 280] loss: 0.505\n", + "[10, 300] loss: 0.548\n", + "[10, 320] loss: 0.539\n", + "[10, 340] loss: 0.534\n", + "[10, 360] loss: 0.528\n", + "Epoch: 9, Val. loss: 0.54, Val. acc: 0.71\n", "Finished Training\n" ] } @@ -843,9 +922,16 @@ "import torch.optim as optim\n", "\n", "model = GNN(D= 100, N = 70, F1 = 32 , F2 = 10, F = F1+F2).to(device)\n", + "if pre_training:\n", + " model.load_state_dict(torch.load(join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data_KM\", \"GNN\", \"Pytorch_GNN_KM\")))\n", + "\n", "criterion = nn.BCELoss()\n", "optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay= 0.00001)\n", "\n", + "\n", + " \n", + "\n", + "\n", "for epoch in range(10): # loop over the dataset multiple times\n", " model.train()\n", " running_loss = 0.0\n", @@ -891,152 +977,13 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "torch.save(model.state_dict(),join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data\", \"GNN\", \"Pytorch_GNN_V2\"))" + "torch.save(model.state_dict(),join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data\", \"GNN\", \"Pytorch_GNN_with_pretraining\"))" ] }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 20] loss: 0.527\n", - "[1, 40] loss: 0.524\n", - "[1, 60] loss: 0.517\n", - "[1, 80] loss: 0.511\n", - "[1, 100] loss: 0.522\n", - "[1, 120] loss: 0.519\n", - "[1, 140] loss: 0.520\n", - "[1, 160] loss: 0.507\n", - "[1, 180] loss: 0.515\n", - "[1, 200] loss: 0.526\n", - "[1, 220] loss: 0.511\n", - "[1, 240] loss: 0.527\n", - "[1, 260] loss: 0.528\n", - "[1, 280] loss: 0.522\n", - "[1, 300] loss: 0.518\n", - "[1, 320] loss: 0.524\n", - "[1, 340] loss: 0.531\n", - "[1, 360] loss: 0.545\n", - "Epoch: 0, Val. loss: 0.54, Val. acc: 0.71\n", - "[2, 20] loss: 0.536\n", - "[2, 40] loss: 0.537\n", - "[2, 60] loss: 0.502\n", - "[2, 80] loss: 0.527\n", - "[2, 100] loss: 0.544\n", - "[2, 120] loss: 0.522\n", - "[2, 140] loss: 0.519\n", - "[2, 160] loss: 0.536\n", - "[2, 180] loss: 0.522\n", - "[2, 200] loss: 0.519\n", - "[2, 220] loss: 0.519\n", - "[2, 240] loss: 0.526\n", - "[2, 260] loss: 0.500\n", - "[2, 280] loss: 0.536\n", - "[2, 300] loss: 0.507\n", - "[2, 320] loss: 0.514\n", - "[2, 340] loss: 0.527\n", - "[2, 360] loss: 0.512\n", - "Epoch: 1, Val. loss: 0.53, Val. acc: 0.72\n", - "[3, 20] loss: 0.533\n", - "[3, 40] loss: 0.522\n", - "[3, 60] loss: 0.498\n", - "[3, 80] loss: 0.493\n", - "[3, 100] loss: 0.496\n", - "[3, 120] loss: 0.518\n", - "[3, 140] loss: 0.524\n", - "[3, 160] loss: 0.508\n", - "[3, 180] loss: 0.523\n", - "[3, 200] loss: 0.510\n", - "[3, 220] loss: 0.506\n", - "[3, 240] loss: 0.509\n", - "[3, 260] loss: 0.524\n", - "[3, 280] loss: 0.519\n", - "[3, 300] loss: 0.520\n", - "[3, 320] loss: 0.516\n", - "[3, 340] loss: 0.493\n", - "[3, 360] loss: 0.531\n", - "Epoch: 2, Val. loss: 0.52, Val. acc: 0.72\n", - "Finished Training\n" - ] - } - ], - "source": [ - "for epoch in range(3): # loop over the dataset multiple times\n", - " model.train()\n", - " running_loss = 0.0\n", - " for i, [XE, X, A,ESM1b, labels] in enumerate(train_loader):\n", - " # zero the parameter gradients\n", - " optimizer.zero_grad()\n", - " XE, X, A, ESM1b, labels = XE.to(device), X.to(device), A.to(device),ESM1b.to(device), labels.to(device)\n", - " # forward + backward + optimize\n", - " outputs = model(XE, X, A, ESM1b)\n", - " loss = criterion(outputs, labels.view((batch_size,-1)))\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " # print statistics\n", - " running_loss += loss.item()\n", - " if i % 20 == 19: # print every 2000 mini-batches\n", - " print('[%d, %5d] loss: %.3f' %\n", - " (epoch + 1, i + 1, running_loss / 20))\n", - " running_loss = 0.0\n", - " \n", - " #After each epoch, calculate the validation loss:\n", - " running_acc = 0.0\n", - " running_loss = 0.0\n", - " model.eval()\n", - " for i, [XE, X, A,ESM1b, labels] in enumerate(test_loader):\n", - " XE, X, A, ESM1b, labels = XE.to(device), X.to(device), A.to(device),ESM1b.to(device), labels.to(device)\n", - " \n", - " with torch.no_grad():\n", - " outputs = model(XE, X, A, ESM1b)\n", - " loss = criterion(outputs, labels.view((batch_size,-1)))\n", - " running_loss += loss.item()\n", - "\n", - " outputs2 = np.round(outputs.view(-1).cpu().detach().numpy()) \n", - " labels2 = labels.cpu().detach().numpy()\n", - " acc = np.mean([outputs2[i] == labels2[i] for i in range(len(labels))])\n", - " running_acc += acc\n", - "\n", - " print(\"Epoch: %s, Val. loss: %s, Val. acc: %s\" % (epoch, np.round(running_loss/(i+1),2),\n", - " np.round(running_acc/(i+1), 2)))\n", - "\n", - "print('Finished Training')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GNN(\n", - " (BN1): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (BN2): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (BN3): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (linear1): Linear(in_features=100, out_features=32, bias=True)\n", - " (linear2): Linear(in_features=32, out_features=1, bias=True)\n", - " (drop_layer): Dropout(p=0.2, inplace=False)\n", - ")" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -1053,30 +1000,30 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GNN(\n", - " (BN1): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (BN2): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (BN3): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (linear1): Linear(in_features=100, out_features=32, bias=True)\n", + " (BN1): BatchNorm2d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (BN2): BatchNorm2d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (BN3): BatchNorm1d(150, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (linear1): Linear(in_features=150, out_features=32, bias=True)\n", " (linear2): Linear(in_features=32, out_features=1, bias=True)\n", " (drop_layer): Dropout(p=0.2, inplace=False)\n", ")" ] }, - "execution_count": 2, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model = GNN(D= 50, N = 70, F1 = 32 , F2 = 10, F = F1+F2).to(device)\n", - "model.load_state_dict(torch.load(join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data\", \"GNN\",\"Pytorch_GNN\")))\n", + "model = GNN(D= 100, N = 70, F1 = 32 , F2 = 10, F = F1+F2).to(device)\n", + "model.load_state_dict(torch.load(join(CURRENT_DIR, \"..\" ,\"data\", \"substrate_data\", \"GNN\", \"Pytorch_GNN_with_pretraining\")))\n", "model.eval()" ] }, @@ -1089,7 +1036,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1106,7 +1053,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1166,53 +1113,53 @@ " ...\n", " \n", " \n", - " 1347\n", - " CHEBI_85986\n", + " 1355\n", + " CHEBI_88052\n", " P9WIQ3\n", " \n", " \n", - " 1348\n", - " CHEBI_86339\n", + " 1356\n", + " InChI=1SQC18H36O3Qc1-2-3-4-5-6-7-8-9-10-11-12-...\n", " P9WIQ3\n", " \n", " \n", - " 1349\n", - " CHEBI_87136\n", + " 1357\n", + " InChI=1SQC3H6O3Qc1-2(4)3(5)6Qh2,4H,1H3,(H,5,6)...\n", " P9WIQ3\n", " \n", " \n", - " 1350\n", - " CHEBI_87305\n", + " 1358\n", + " InChI=1SQC8H16O3Qc1-2-3-4-5-6-7(9)8(10)11Qh7,9...\n", " P9WIQ3\n", " \n", " \n", - " 1351\n", - " CHEBI_88052\n", + " 1359\n", + " InChI=1SQC8H8O3Qc9-7(8(10)11)6-4-2-1-3-5-6Qh1-...\n", " P9WIQ3\n", " \n", " \n", "\n", - "

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1360 rows × 3 columns

\n", "" ], "text/plain": [ - " molecule ID uid substrate_rep\n", - "0 C00001 P9WIQ3 [0.029613253, 0.0, 0.26818, 0.0, 0.13194986, 0...\n", - "1 C00002 P9WIQ3 [1238.0188, 0.0, 0.0, 42.365837, 74.54658, 28....\n", - "2 C00003 P9WIQ3 [2111.6353, 0.0, 0.0, 139.63763, 183.79233, 33...\n", - "3 C00004 P9WIQ3 [1813.2203, 60.59075, 0.0, 224.62354, 335.9574...\n", - "4 C00005 P9WIQ3 [1859.3567, 120.4939, 0.0, 255.73376, 384.9909...\n", - "... ... ... ...\n", - "1347 CHEBI_85986 P9WIQ3 [673.1395, 31.783257, 0.0, 15.939333, 38.13377...\n", - "1348 CHEBI_86339 P9WIQ3 [6.3847322, 18.652742, 0.0, 76.061226, 61.6412...\n", - "1349 CHEBI_87136 P9WIQ3 [1872.2444, 96.23757, 0.0, 217.94662, 388.0197...\n", - "1350 CHEBI_87305 P9WIQ3 [1924.4116, 97.580894, 0.0, 217.94662, 384.225...\n", - "1351 CHEBI_88052 P9WIQ3 [82.04205, 2.6866338, 36.013027, 88.17541, 116...\n", + " molecule ID uid \\\n", + "0 C00001 P9WIQ3 \n", + "1 C00002 P9WIQ3 \n", + "2 C00003 P9WIQ3 \n", + "3 C00004 P9WIQ3 \n", + "4 C00005 P9WIQ3 \n", + "... ... ... \n", + "1355 CHEBI_88052 P9WIQ3 \n", + "1356 InChI=1SQC18H36O3Qc1-2-3-4-5-6-7-8-9-10-11-12-... P9WIQ3 \n", + "1357 InChI=1SQC3H6O3Qc1-2(4)3(5)6Qh2,4H,1H3,(H,5,6)... P9WIQ3 \n", + "1358 InChI=1SQC8H16O3Qc1-2-3-4-5-6-7(9)8(10)11Qh7,9... P9WIQ3 \n", + "1359 InChI=1SQC8H8O3Qc9-7(8(10)11)6-4-2-1-3-5-6Qh1-... P9WIQ3 \n", "\n", - "[1352 rows x 3 columns]" + " substrate_rep \n", + "0 [0.0, 0.6329006, 0.0, 44.773804, 41.644196, 21... \n", + "1 [0.0, 46.134777, 0.0, 0.0, 1177.1073, 187.5388... \n", + "2 [0.0, 8.150052, 0.0, 8.550125, 40.956882, 66.0... \n", + "3 [0.0, 1.8680593, 0.0, 0.0, 44.121048, 557.9273... \n", + "4 [0.0, 0.0, 0.0, 0.0, 183.79741, 173.6141, 0.0,... \n", + "... ... \n", + "1355 [0.0, 46.631508, 0.0, 0.0, 2.3734078, 1.361197... \n", + "1356 [0.0, 0.2668656, 0.0, 0.0, 0.784772, 10.25194,... \n", + "1357 [0.0, 0.9508717, 0.0, 90.905556, 0.79774696, 0... \n", + "1358 [0.0, 0.2668656, 0.0, 0.0, 0.784772, 10.25194,... \n", + "1359 [0.0, 0.0, 0.0, 32.41623, 66.58023, 126.79576,... \n", + "\n", + "[1360 rows x 3 columns]" ] }, - "execution_count": 7, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1413,77 +1380,69 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "df_train = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"splits\", \"df_train_with_ESM1b_ts_GNN.pkl\"))\n", + "df_test = pd.read_pickle(join(CURRENT_DIR, \"..\" ,\"data\", \"splits\", \"df_test_with_ESM1b_ts_GNN.pkl\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\alexk\\AppData\\Local\\Temp/ipykernel_6620/2047707114.py:4: SettingWithCopyWarning: \n", + "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_train[\"GNN rep\"][ind] = list(df_mols[\"substrate_rep\"].loc[df_mols[\"molecule ID\"] == df_train[\"molecule ID\"][ind].replace(\":\", \"_\")])[0]\n" + " after removing the cwd from sys.path.\n" ] } ], "source": [ - "df_train[\"GNN rep\"] = \"\"\n", + "df_train[\"GNN rep (pretrained)\"] = \"\"\n", "for ind in df_train.index:\n", " try:\n", - " df_train[\"GNN rep\"][ind] = list(df_mols[\"substrate_rep\"].loc[df_mols[\"molecule ID\"] == df_train[\"molecule ID\"][ind].replace(\":\", \"_\")])[0]\n", + " df_train[\"GNN rep (pretrained)\"][ind] = list(df_mols[\"substrate_rep\"].loc[df_mols[\"molecule ID\"] == df_train[\"molecule ID\"][ind].replace(\":\", \"_\").replace(\"Q\", \"/\")])[0]\n", " except IndexError:\n", " pass" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\alexk\\AppData\\Local\\Temp/ipykernel_6620/451796927.py:4: SettingWithCopyWarning: \n", + "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_test[\"GNN rep\"][ind] = list(df_mols[\"substrate_rep\"].loc[df_mols[\"molecule ID\"] == df_test[\"molecule ID\"][ind].replace(\":\", \"_\")])[0]\n" + " after removing the cwd from sys.path.\n" ] } ], "source": [ - "df_test[\"GNN rep\"] = \"\"\n", + "df_test[\"GNN rep (pretrained)\"] = \"\"\n", "for ind in df_test.index:\n", " try:\n", - " df_test[\"GNN rep\"][ind] = list(df_mols[\"substrate_rep\"].loc[df_mols[\"molecule ID\"] == df_test[\"molecule ID\"][ind].replace(\":\", \"_\")])[0]\n", + " df_test[\"GNN rep (pretrained)\"][ind] = list(df_mols[\"substrate_rep\"].loc[df_mols[\"molecule ID\"] == df_test[\"molecule ID\"][ind].replace(\":\", \"_\").replace(\"Q\", \"/\")])[0]\n", " except IndexError:\n", " pass" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "'''df_engqvist[\"molecule ID\"] = df_engqvist[\"substrate\"]\n", - "\n", - "df_engqvist[\"GNN rep\"] = \"\"\n", - "for ind in df_engqvist.index:\n", - " try:\n", - " df_engqvist[\"GNN rep\"][ind] = list(df_mols[\"substrate_rep\"].loc[df_mols[\"molecule ID\"] == df_engqvist[\"molecule ID\"][ind].replace(\":\", \"_\")])[0]\n", - " except IndexError:\n", - " pass\n", - " \n", - "df_engqvist''';" - ] - }, - { - "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [