diff options
| author | Christoph Helma <helma@in-silico.ch> | 2021-01-18 22:08:19 +0100 |
|---|---|---|
| committer | Christoph Helma <helma@in-silico.ch> | 2021-01-18 22:08:19 +0100 |
| commit | 938c40dc83e02ebdd7816fb7a61016fb1db80777 (patch) | |
| tree | 40a0053dbfde8542a595e00631a02e1556736f33 /scripts | |
| parent | ed2625b9b2fde45cfd1739695310d47866b3c0b0 (diff) | |
tensorflow cv with fixed mutagenicity outcome
Diffstat (limited to 'scripts')
| -rwxr-xr-x | scripts/cdk-descriptors.rb (renamed from scripts/padel-descriptors.rb) | 0 | ||||
| -rwxr-xr-x | scripts/cv-tensorflow-confusion-matrix.rb | 27 | ||||
| -rwxr-xr-x | scripts/pa-summary.rb | 12 | ||||
| -rwxr-xr-x | scripts/pa-table.rb | 35 | ||||
| -rwxr-xr-x | scripts/pa-tex-table.rb | 25 | ||||
| -rw-r--r-- | scripts/tensorflow/prediction-v2-ext-Padel-2D.ipynb | 1007 | ||||
| -rw-r--r-- | scripts/tensorflow/prediction-v2-norm.ipynb | 11720 | ||||
| -rw-r--r-- | scripts/tensorflow/prediction-v5-ext.ipynb | 11248 | ||||
| -rw-r--r-- | scripts/tensorflow/prediction-v5.ipynb | 11600 |
9 files changed, 22918 insertions, 12756 deletions
diff --git a/scripts/padel-descriptors.rb b/scripts/cdk-descriptors.rb index 691137f..691137f 100755 --- a/scripts/padel-descriptors.rb +++ b/scripts/cdk-descriptors.rb diff --git a/scripts/cv-tensorflow-confusion-matrix.rb b/scripts/cv-tensorflow-confusion-matrix.rb index 2b0ee58..fba5c49 100755 --- a/scripts/cv-tensorflow-confusion-matrix.rb +++ b/scripts/cv-tensorflow-confusion-matrix.rb @@ -7,17 +7,30 @@ tn = 0 fn = 0 pred = CSV.read(ARGV[0],headers: true,:col_sep => ",") -act = CSV.read(File.join("data","GenoTox-database.csv"),headers: true,:col_sep => ",") +act = CSV.read(File.join("data","mutagenicity.csv"),headers: true,:col_sep => ",") -pred.each_with_index do |row,i| +data = {} +pred.each do |row| row[1].to_f < 0.5 ? p = 0 : p = 1 - a = act[i].to_h["GENO.Outcome"].to_i + data[row[0]] =[p] +end + +act.each do |row| + if row[1] == "mutagenic" + a = 1 + elsif row[1] == "non-mutagenic" + a = 0 + end + data[row[0]] << a if data[row[0]] +end + +data.each do |smi,a| - tp += 1 if a == 1 and p == 1 - tn += 1 if a == 0 and p == 0 - fp += 1 if a == 0 and p == 1 - fn += 1 if a == 1 and p == 0 + tp += 1 if a[0] == 1 and a[1] == 1 + tn += 1 if a[0] == 0 and a[1] == 0 + fp += 1 if a[0] == 0 and a[1] == 1 + fn += 1 if a[0] == 1 and a[1] == 0 end diff --git a/scripts/pa-summary.rb b/scripts/pa-summary.rb index 0715a6c..a8c8f19 100755 --- a/scripts/pa-summary.rb +++ b/scripts/pa-summary.rb @@ -73,7 +73,8 @@ summary = { n = 0 File.read(ARGV[0]).each_line do |l| unless l.match("SMILES") - id,cid,smi,cansmi,exp,lazar_MP2D,lazar_MP2D_high_confidence,lazar_PaDEL,lazar_PaDEL_high_confidence,r_DL,r_RF,r_SVM,tf_lr_sgd,tf_lr_scikit,tf_NN,tf_RF = l.chomp.split(",") + id,r,o,p,n,de,t,ma,mo,di,cid,smi,cansmi,exp,lazar_MP2D,lazar_MP2D_high_confidence,lazar_PaDEL,lazar_PaDEL_high_confidence,r_DL,r_RF,r_SVM,tf_lr_sgd,tf_lr_scikit,tf_NN,tf_RF = l.chomp.split(",") + #id,cid,smi,cansmi,exp,lazar_MP2D,lazar_MP2D_high_confidence,lazar_PaDEL,lazar_PaDEL_high_confidence,r_DL,r_RF,r_SVM,tf_lr_sgd,tf_lr_scikit,tf_NN,tf_RF = l.chomp.split(",") if lazar_MP2D == "1" summary[:lazar][:mp2d][:all][:n] += 1 @@ -161,8 +162,13 @@ summary[:lazar][:mp2d][:all][:n_perc] = (100.0*summary[:lazar][:mp2d][:all][:n]/ summary[:lazar][:mp2d][:all][:mut_perc] = (100.0*summary[:lazar][:mp2d][:all][:mut]/summary[:lazar][:mp2d][:all][:n]).round summary[:lazar][:mp2d][:all][:non_mut_perc] = (100.0*summary[:lazar][:mp2d][:all][:non_mut]/summary[:lazar][:mp2d][:all][:n]).round summary[:lazar][:mp2d][:high_confidence][:n_perc] = (100.0*summary[:lazar][:mp2d][:high_confidence][:n]/summary[:n]).round -summary[:lazar][:mp2d][:high_confidence][:mut_perc] = (100.0*summary[:lazar][:mp2d][:high_confidence][:mut]/summary[:lazar][:mp2d][:high_confidence][:n]).round -summary[:lazar][:mp2d][:high_confidence][:non_mut_perc] = (100.0*summary[:lazar][:mp2d][:high_confidence][:non_mut]/summary[:lazar][:mp2d][:high_confidence][:n]).round +if summary[:lazar][:mp2d][:high_confidence][:n] == 0 + summary[:lazar][:mp2d][:high_confidence][:mut_perc] = 0 + summary[:lazar][:mp2d][:high_confidence][:non_mut_perc] = 0 +else + summary[:lazar][:mp2d][:high_confidence][:mut_perc] = (100.0*summary[:lazar][:mp2d][:high_confidence][:mut]/summary[:lazar][:mp2d][:high_confidence][:n]).round + summary[:lazar][:mp2d][:high_confidence][:non_mut_perc] = (100.0*summary[:lazar][:mp2d][:high_confidence][:non_mut]/summary[:lazar][:mp2d][:high_confidence][:n]).round +end summary[:lazar][:padel][:all][:n_perc] = (100.0*summary[:lazar][:padel][:all][:n]/summary[:n]).round summary[:lazar][:padel][:all][:mut_perc] = (100.0*summary[:lazar][:padel][:all][:mut]/summary[:lazar][:padel][:all][:n]).round summary[:lazar][:padel][:all][:non_mut_perc] = (100.0*summary[:lazar][:padel][:all][:non_mut]/summary[:lazar][:padel][:all][:n]).round diff --git a/scripts/pa-table.rb b/scripts/pa-table.rb index ba7af63..68d7fc6 100755 --- a/scripts/pa-table.rb +++ b/scripts/pa-table.rb @@ -1,25 +1,41 @@ #!/usr/bin/env ruby -header = ["ID","CID","SMILES","Canonical SMILES","Measured","lazar-MP2D","lazar-MP2D-high-confidence","lazar-CDK","lazar-CDK-high-confidence"] +# red groups tab = [] +File.read("pyrrolizidine-alkaloids/R/PA.RF.outcome.csv").each_line do |l| + items = l.chomp.split(';') + if items.first.empty? + items[0] = "ID" + else + id = items.shift + items.collect!{|i| i == "NA" ? 0 : 1} + items = [id]+items + end + tab << items +end + +tab[0] += ["CID","SMILES","Canonical SMILES","Measured","lazar-MP2D","lazar-MP2D-high-confidence","lazar-CDK","lazar-CDK-high-confidence"] + i = 0 File.read("pyrrolizidine-alkaloids/180920_PA_complete_SMILES.csv").each_line do |l| if i > 0 id,cid,name,smi = l.chomp.split(";") - tab << [id,cid,'"'+smi+'"'] + tab[i] += [cid,'"'+smi+'"'] end i += 1 end + i = 0 File.read("pyrrolizidine-alkaloids/lazar/pa-mp2d-predictions.csv").each_line do |l| if i > 0 id,cansmi,exp,mut,p0,p1,max_sim,nn = l.chomp.split(",") max_sim.to_f < 0.5? hc = "F" : hc = "T" hc = "" if mut.empty? - tab[i-1] += ['"'+cansmi+'"',exp,mut,hc] + tab[i] += ['"'+cansmi+'"',exp,mut,hc] end i += 1 end + i=0 File.read("pyrrolizidine-alkaloids/lazar/pa-padel-predictions.csv").each_line do |l| #if i > 0 @@ -30,32 +46,33 @@ File.read("pyrrolizidine-alkaloids/lazar/pa-padel-predictions.csv").each_line do #end i += 1 end + Dir["pyrrolizidine-alkaloids/R/PA.*.outcome.csv"].each do |r| - header << "R-"+r.sub('pyrrolizidine-alkaloids/R/PA.','').sub('.outcome.csv','') + tab[0] << "R-"+r.sub('pyrrolizidine-alkaloids/R/PA.','').sub('.outcome.csv','') i = 0 File.read(r).each_line do |l| if i > 0 items = l.chomp.split(";") items.shift if items.uniq.include? "1" - tab[i-1] << 1 + tab[i] << 1 elsif items.uniq.include? "0" - tab[i-1] << 0 + tab[i] << 0 end end i += 1 end end + Dir["pyrrolizidine-alkaloids/tensorflow/pred.*.v3-ext-Padel-2D.csv"].each do |r| - header << "TF-"+r.sub('pyrrolizidine-alkaloids/tensorflow/pred.','').sub('.v3-ext-Padel-2D.csv','').sub("lr2","LR-scikit").sub("lr","LR-sgd").sub("rf","RF").sub("nn","NN") + tab[0] << "TF-"+r.sub('pyrrolizidine-alkaloids/tensorflow/pred.','').sub('.v3-ext-Padel-2D.csv','').sub("lr2","LR-scikit").sub("lr","LR-sgd").sub("rf","RF").sub("nn","NN") i = 0 File.read(r).each_line do |l| if i > 0 id,pred = l.chomp.split(",") - pred.to_f > 0.5 ? tab[i-1] << 1 : tab[i-1] << 0 + pred.to_f > 0.5 ? tab[i] << 1 : tab[i] << 0 end i += 1 end end -puts header.join(",") puts tab.collect{|r| r.join(",")}.join("\n") diff --git a/scripts/pa-tex-table.rb b/scripts/pa-tex-table.rb index 74410b7..0fe1410 100755 --- a/scripts/pa-tex-table.rb +++ b/scripts/pa-tex-table.rb @@ -6,19 +6,24 @@ puts ' \definecolor{green}{rgb}{0,1,0} \definecolor{darkgreen}{rgb}{0,0.5,0} \definecolor{grey}{rgb}{0.5,0.5,0.5} +\definecolor{black}{rgb}{0,0,0} +\definecolor{white}{rgb}{1,1,1} \tiny -\begin{longtable}{rcccccccccc} -\caption{Summary of pyrrolizidine alkaloid predictions: red: mutagen, green: non-mutagen, grey: no prediction, dark red/green: low confidence} \\\\ +\begin{longtable}{rrrrrrrrrcccccccccc} +\caption{Summary of pyrrolizidine alkaloid predictions: red: mutagen, green: non-mutagen, grey: no prediction, dark red/green: low confidence; 1: Retronecine, 2: Otonecine, 3: Platynecine, 4: N-oxide, 5: Dehydropyrrolizidine, 6:Tertiary PA, 7: Macrocyclic-diester, 8: Monoester, 9: Diester} \\\\ \label{tab:pa} -PubChem & & \multicolumn{2}{c}{lazar} & \multicolumn{3}{c}{R} & \multicolumn{4}{c}{Tensorflow}\\\\ -CID & Measured & MP2D & CDK & DL & RF & SVM & LR-sgd & LR-scikit & NN & RF \\\\ + +1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & Measured & MP2D & CDK & DL & RF & SVM & LR-sgd & LR-scikit & NN & RF \\kill % needed as guide for multicolumn +\multicolumn{9}{c}{PA Group} & & \multicolumn{2}{c}{lazar} & \multicolumn{3}{c}{R} & \multicolumn{4}{c}{Tensorflow}\\\\ + +1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & Measured & MP2D & CDK & DL & RF & SVM & LR-sgd & LR-scikit & NN & RF \\\\ \hline \renewcommand{\arraystretch}{0.075} ' File.read(ARGV[0]).each_line do |l| unless l.match("SMILES") - id,cid,smi,cansmi,exp,lazar_MP2D,lazar_MP2D_high_confidence,lazar_PaDEL,lazar_PaDEL_high_confidence,r_DL,r_RF,r_SVM,tf_lr_sgd,tf_lr_scikit,tf_NN,tf_RF = l.chomp.split(",") - row = cid + id,r,o,p,n,de,t,ma,mo,di,cid,smi,cansmi,exp,lazar_MP2D,lazar_MP2D_high_confidence,lazar_CDK,lazar_CDK_high_confidence,r_DL,r_RF,r_SVM,tf_lr_sgd,tf_lr_scikit,tf_NN,tf_RF = l.chomp.split(",") + row = [r,o,p,n,de,t,ma,mo,di].collect{|group| group == "1" ? '\cellcolor{black}' : '\cellcolor{white}'}.join(' & ') if exp == "1" row += ' & \cellcolor{red}' elsif exp == "0" @@ -37,10 +42,10 @@ File.read(ARGV[0]).each_line do |l| else row += ' & \cellcolor{grey}' end - if lazar_PaDEL == "1" - lazar_PaDEL_high_confidence == "F" ? row += ' & \cellcolor{darkred}' : row += ' & \cellcolor{red}' - elsif lazar_PaDEL == "0" - lazar_PaDEL_high_confidence == "F" ? row += ' & \cellcolor{darkgreen}' : row += ' & \cellcolor{green}' + if lazar_CDK == "1" + lazar_CDK_high_confidence == "F" ? row += ' & \cellcolor{darkred}' : row += ' & \cellcolor{red}' + elsif lazar_CDK == "0" + lazar_CDK_high_confidence == "F" ? row += ' & \cellcolor{darkgreen}' : row += ' & \cellcolor{green}' else row += ' & \cellcolor{grey}' end diff --git a/scripts/tensorflow/prediction-v2-ext-Padel-2D.ipynb b/scripts/tensorflow/prediction-v2-ext-Padel-2D.ipynb deleted file mode 100644 index a66b942..0000000 --- a/scripts/tensorflow/prediction-v2-ext-Padel-2D.ipynb +++ /dev/null @@ -1,1007 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "from keras import optimizers, regularizers\n", - "from keras.layers import Dense, Dropout, Input\n", - "from keras.models import Model, Sequential\n", - "from random import shuffle\n", - "from scipy import interp\n", - "from sklearn.linear_model import LogisticRegression\n", - "from scipy.stats.mstats import gmean\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import roc_curve, auc\n", - "from sklearn.model_selection import StratifiedKFold, train_test_split\n", - "from sklearn.preprocessing import QuantileTransformer\n", - "import contextlib\n", - "import glob\n", - "import gzip\n", - "import h5py\n", - "import keras\n", - "import numpy as np\n", - "import os\n", - "import pandas as pd\n", - "import pylab as plt\n", - "import random\n", - "import scipy\n", - "import sklearn\n", - "import tensorflow as tf\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.datasets import make_classification\n", - "\n", - "\n", - "\n", - "random_state = np.random.RandomState(0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(8080, 1173)\n", - "(8080, 1)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (517,531,534,542,545,547,573,576,599,601,604,607,620,627,629,636,650,653,661,664,666,692,695,718,720,723,726,739,746,748,755,769,772,780,783,785,805,811,814,819,837,845,901,1005) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " interactivity=interactivity, compiler=compiler, result=result)\n" - ] - } - ], - "source": [ - "X_f_ext = '/home/drewe/notebooks/genotox/mutagenicity-mod-2.csv'\n", - "y_f_ext = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\n", - "\n", - "x_pad = pd.read_csv('/home/drewe/notebooks/genotox/PA_Padel_2D_m2.csv', sep=';', decimal=',')\n", - "\n", - "X_ext = pd.read_csv(X_f_ext,sep=';')\n", - "del X_ext['Name']\n", - "selected_cols = list(set(x_pad.columns.tolist()).intersection(set(X_ext.columns.tolist())))\n", - "\n", - "X_ext = np.float64(X_ext[selected_cols].values)\n", - "y_ext = np.float64(pd.read_csv(y_f_ext).values)\n", - "x_pad = np.float64(x_pad[selected_cols].values)\n", - "print(X_ext.shape)\n", - "print(y_ext.shape)\n", - "\n", - "names = pd.read_csv('/home/drewe/notebooks/genotox/PA_Padel_2D_m2.csv', sep=';')['Name'].values\n", - "#X_norm = QuantileTransformer(output_distribution='uniform').fit_transform(X.values.T).T\n", - "\n", - "\n", - "trnsf = sklearn.preprocessing.QuantileTransformer( output_distribution='uniform')\n", - "trnsf.fit(X_ext)\n", - "\n", - "X = trnsf.transform(X_ext)\n", - "x_pad = trnsf.transform(x_pad)\n", - "y = y_ext[: ,0]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - ".\n", - ">>\n", - ".\n", - ">>\n", - ".\n", - ">>\n", - ".\n", - ">>\n", - ".\n" - ] - } - ], - "source": [ - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "\n", - "keras.backend.clear_session()\n", - "prbs=[]\n", - "for mod in range(5):\n", - " print('>>')\n", - " curr_try = 0\n", - " while curr_try <10:\n", - " print('.')\n", - "\n", - " model = Sequential()\n", - " model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(1, activation='sigmoid'))\n", - " # Compile model\n", - " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", - " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", - "\n", - " # Fit the model\n", - " history = model.fit(X, y, epochs=50, batch_size=64, verbose=0)\n", - " if history.history['acc'][-1] > 0.53:\n", - " break\n", - " else:\n", - " curr_try += 1\n", - "\n", - " # Fit the model\n", - " probas_ = model.predict(x_pad)\n", - " prbs.append(probas_)\n", - "# Average the predictions\n", - "probas_ = np.mean(np.hstack(prbs), axis=1)\n", - "results = probas_\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.nn.v3-ext-Padel-2D.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results,100, color='red', alpha=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - ".\n", - "Epoch 1/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6169 - acc: 0.6601\n", - "Epoch 2/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6184 - acc: 0.6558\n", - "Epoch 3/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6161 - acc: 0.6590\n", - "Epoch 4/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6197 - acc: 0.6550\n", - "Epoch 5/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6150 - acc: 0.6579\n", - "Epoch 6/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6167 - acc: 0.6572\n", - "Epoch 7/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6193 - acc: 0.6522\n", - "Epoch 8/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6150 - acc: 0.6620\n", - "Epoch 9/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6164 - acc: 0.6589\n", - "Epoch 10/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6147 - acc: 0.6610\n", - "Epoch 11/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6145 - acc: 0.6562\n", - "Epoch 12/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6162 - acc: 0.6575\n", - "Epoch 13/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6146 - acc: 0.6627\n", - "Epoch 14/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6132 - acc: 0.6621\n", - "Epoch 15/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6138 - acc: 0.6615\n", - "Epoch 16/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6178 - acc: 0.6531\n", - "Epoch 17/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6126 - acc: 0.6645\n", - "Epoch 18/50\n", - "8080/8080 [==============================] - 0s 41us/step - loss: 0.6136 - acc: 0.6605\n", - "Epoch 19/50\n", - "8080/8080 [==============================] - 0s 39us/step - loss: 0.6127 - acc: 0.6631\n", - "Epoch 20/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6126 - acc: 0.6585\n", - "Epoch 21/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6137 - acc: 0.6582\n", - "Epoch 22/50\n", - "8080/8080 [==============================] - 0s 34us/step - loss: 0.6129 - acc: 0.6605\n", - "Epoch 23/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6114 - acc: 0.6626\n", - "Epoch 24/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6144 - acc: 0.6640\n", - "Epoch 25/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6169 - acc: 0.6583\n", - "Epoch 26/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6153 - acc: 0.6574\n", - "Epoch 27/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6113 - acc: 0.6649\n", - "Epoch 28/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6124 - acc: 0.6619\n", - "Epoch 29/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6130 - acc: 0.6608\n", - "Epoch 30/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6150 - acc: 0.6573\n", - "Epoch 31/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6136 - acc: 0.6615\n", - "Epoch 32/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6137 - acc: 0.6585\n", - "Epoch 33/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6128 - acc: 0.6627\n", - "Epoch 34/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6139 - acc: 0.6580\n", - "Epoch 35/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6097 - acc: 0.6652\n", - "Epoch 36/50\n", - "8080/8080 [==============================] - 0s 34us/step - loss: 0.6118 - acc: 0.6603\n", - "Epoch 37/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6111 - acc: 0.6645\n", - "Epoch 38/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6105 - acc: 0.6653\n", - "Epoch 39/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6117 - acc: 0.6624\n", - "Epoch 40/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6138 - acc: 0.6599\n", - "Epoch 41/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6101 - acc: 0.6616\n", - "Epoch 42/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6123 - acc: 0.6620\n", - "Epoch 43/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6116 - acc: 0.6632\n", - "Epoch 44/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6118 - acc: 0.6559\n", - "Epoch 45/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6115 - acc: 0.6618\n", - "Epoch 46/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6130 - acc: 0.6642\n", - "Epoch 47/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6089 - acc: 0.6603\n", - "Epoch 48/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6110 - acc: 0.6634\n", - "Epoch 49/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6107 - acc: 0.6670\n", - "Epoch 50/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6119 - acc: 0.6624\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6197 - acc: 0.6557\n", - "Epoch 2/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6191 - acc: 0.6562\n", - "Epoch 3/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6144 - acc: 0.6599\n", - "Epoch 4/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6151 - acc: 0.6593\n", - "Epoch 5/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6173 - acc: 0.6634\n", - "Epoch 6/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6189 - acc: 0.6546\n", - "Epoch 7/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 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[==============================] - 0s 40us/step - loss: 0.6120 - acc: 0.6632\n", - "Epoch 26/50\n", - "8080/8080 [==============================] - 0s 40us/step - loss: 0.6111 - acc: 0.6625\n", - "Epoch 27/50\n", - "8080/8080 [==============================] - 0s 52us/step - loss: 0.6118 - acc: 0.6611\n", - "Epoch 28/50\n", - "8080/8080 [==============================] - 0s 48us/step - loss: 0.6122 - acc: 0.6624\n", - "Epoch 29/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6134 - acc: 0.6632\n", - "Epoch 30/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6129 - acc: 0.6603\n", - "Epoch 31/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6105 - acc: 0.6636\n", - "Epoch 32/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6123 - acc: 0.6662\n", - "Epoch 33/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6107 - acc: 0.6623\n", - "Epoch 34/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6162 - acc: 0.6599\n", - "Epoch 35/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6123 - acc: 0.6631\n", - "Epoch 36/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6124 - acc: 0.6618\n", - "Epoch 37/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6102 - acc: 0.6609\n", - "Epoch 38/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6104 - acc: 0.6653\n", - "Epoch 39/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6119 - acc: 0.6627\n", - "Epoch 40/50\n", - "8080/8080 [==============================] - 0s 56us/step - loss: 0.6110 - acc: 0.6625\n", - "Epoch 41/50\n", - "8080/8080 [==============================] - 0s 43us/step - loss: 0.6120 - acc: 0.6601\n", - "Epoch 42/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6125 - acc: 0.6598\n", - "Epoch 43/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6115 - acc: 0.6580\n", - "Epoch 44/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6135 - acc: 0.6614\n", - "Epoch 45/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6117 - acc: 0.6656\n", - "Epoch 46/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6133 - acc: 0.6624\n", - "Epoch 47/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6114 - acc: 0.6624\n", - "Epoch 48/50\n", - "8080/8080 [==============================] - 0s 34us/step - loss: 0.6082 - acc: 0.6676\n", - "Epoch 49/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6104 - acc: 0.6646\n", - "Epoch 50/50\n", - "8080/8080 [==============================] - 0s 34us/step - loss: 0.6081 - acc: 0.6642\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6171 - acc: 0.6542\n", - "Epoch 2/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6166 - acc: 0.6593\n", - "Epoch 3/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6170 - acc: 0.6568\n", - "Epoch 4/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6180 - acc: 0.6577\n", - "Epoch 5/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6155 - acc: 0.6580\n", - "Epoch 6/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6177 - acc: 0.6583\n", - "Epoch 7/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6147 - acc: 0.6584\n", - "Epoch 8/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6169 - acc: 0.6561\n", - "Epoch 9/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6142 - acc: 0.6608\n", - "Epoch 10/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6170 - acc: 0.6568\n", - "Epoch 11/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6167 - acc: 0.6606\n", - "Epoch 12/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6190 - acc: 0.6566\n", - "Epoch 13/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6146 - acc: 0.6629\n", - "Epoch 14/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6133 - acc: 0.6543\n", - "Epoch 15/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6139 - acc: 0.6616\n", - "Epoch 16/50\n", - "8080/8080 [==============================] - 0s 39us/step - loss: 0.6137 - acc: 0.6580\n", - "Epoch 17/50\n", - "8080/8080 [==============================] - 0s 39us/step - loss: 0.6140 - acc: 0.6610\n", - "Epoch 18/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6124 - acc: 0.6611\n", - "Epoch 19/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6131 - acc: 0.6616\n", - "Epoch 20/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6142 - acc: 0.6603\n", - "Epoch 21/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6125 - acc: 0.6603\n", - "Epoch 22/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6125 - acc: 0.6558\n", - "Epoch 23/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6116 - acc: 0.6632\n", - "Epoch 24/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6130 - acc: 0.6645\n", - "Epoch 25/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6121 - acc: 0.6579\n", - "Epoch 26/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6113 - acc: 0.6632\n", - "Epoch 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- "8080/8080 [==============================] - 0s 40us/step - loss: 0.6141 - acc: 0.6592\n", - "Epoch 15/50\n", - "8080/8080 [==============================] - 0s 50us/step - loss: 0.6149 - acc: 0.6613\n", - "Epoch 16/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6122 - acc: 0.6613\n", - "Epoch 17/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6137 - acc: 0.6604\n", - "Epoch 18/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6147 - acc: 0.6592\n", - "Epoch 19/50\n", - "8080/8080 [==============================] - 0s 39us/step - loss: 0.6145 - acc: 0.6618\n", - "Epoch 20/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6138 - acc: 0.6594\n", - "Epoch 21/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6126 - acc: 0.6573\n", - "Epoch 22/50\n", - "8080/8080 [==============================] - 0s 35us/step - loss: 0.6120 - acc: 0.6624\n", - "Epoch 23/50\n", - "8080/8080 [==============================] - 0s 61us/step - loss: 0.6112 - acc: 0.6636\n", - "Epoch 24/50\n", - "8080/8080 [==============================] - 0s 44us/step - loss: 0.6115 - acc: 0.6653\n", - "Epoch 25/50\n", - "8080/8080 [==============================] - 0s 36us/step - loss: 0.6153 - acc: 0.6599\n", - "Epoch 26/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6142 - acc: 0.6589\n", - "Epoch 27/50\n", - "8080/8080 [==============================] - 0s 40us/step - loss: 0.6139 - acc: 0.6605\n", - "Epoch 28/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6146 - acc: 0.6573\n", - "Epoch 29/50\n", - "8080/8080 [==============================] - 0s 39us/step - loss: 0.6104 - acc: 0.6656\n", - "Epoch 30/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6112 - acc: 0.6662\n", - "Epoch 31/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6109 - acc: 0.6640\n", - "Epoch 32/50\n", - "8080/8080 [==============================] - 0s 37us/step - loss: 0.6139 - acc: 0.6624\n", - "Epoch 33/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6146 - acc: 0.6631\n", - "Epoch 34/50\n", - "8080/8080 [==============================] - 0s 46us/step - loss: 0.6145 - acc: 0.6625\n", - "Epoch 35/50\n", - "8080/8080 [==============================] - 0s 47us/step - loss: 0.6099 - acc: 0.6631\n", - "Epoch 36/50\n", - "8080/8080 [==============================] - 0s 51us/step - loss: 0.6102 - acc: 0.6640\n", - "Epoch 37/50\n", - "8080/8080 [==============================] - 0s 50us/step - loss: 0.6119 - acc: 0.6592\n", - "Epoch 38/50\n", - "8080/8080 [==============================] - 0s 39us/step - loss: 0.6109 - acc: 0.6623\n", - "Epoch 39/50\n", - "8080/8080 [==============================] - 0s 48us/step - loss: 0.6128 - acc: 0.6577\n", - "Epoch 40/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6111 - acc: 0.6636\n", - "Epoch 41/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6096 - acc: 0.6634\n", - "Epoch 42/50\n", - "8080/8080 [==============================] - 0s 39us/step - loss: 0.6104 - acc: 0.6606\n", - "Epoch 43/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6090 - acc: 0.6645\n", - "Epoch 44/50\n", - "8080/8080 [==============================] - 0s 42us/step - loss: 0.6103 - acc: 0.6641\n", - "Epoch 45/50\n", - "8080/8080 [==============================] - 0s 45us/step - loss: 0.6112 - acc: 0.6630\n", - "Epoch 46/50\n", - "8080/8080 [==============================] - 0s 43us/step - loss: 0.6098 - acc: 0.6649\n", - "Epoch 47/50\n", - "8080/8080 [==============================] - 0s 45us/step - loss: 0.6103 - acc: 0.6624\n", - "Epoch 48/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "8080/8080 [==============================] - 0s 39us/step - loss: 0.6118 - acc: 0.6597\n", - "Epoch 49/50\n", - "8080/8080 [==============================] - 0s 38us/step - loss: 0.6102 - acc: 0.6611\n", - "Epoch 50/50\n", - "8080/8080 [==============================] - 0s 43us/step - loss: 0.6083 - acc: 0.6649\n" - ] - } - ], - "source": [ - "#Logistic regression (SGD)\n", - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "keras.backend.clear_session()\n", - "prbs=[]\n", - "for mod in range(5):\n", - " print('>>')\n", - " curr_try = 0\n", - " while curr_try <10:\n", - " print('.')\n", - "\n", - " model = Sequential()\n", - " model.add(Dense(1, activation='sigmoid'))\n", - " # Compile model\n", - " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", - " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", - "\n", - " # Fit the model\n", - " history = model.fit(X, y, epochs=50, batch_size=64, verbose=0)\n", - " if history.history['acc'][-1] > 0.53:\n", - " break\n", - " else:\n", - " curr_try += 1\n", - "\n", - " # Fit the model\n", - " model.fit(X, y, epochs=50, batch_size=64, verbose=1)\n", - "\n", - " # evaluate the model\n", - " probas_ = model.predict(x_pad)\n", - " prbs.append(probas_)\n", - "# Average the predictions\n", - "probas_ = np.mean(np.hstack(prbs), axis=1)\n", - "results = probas_" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr.v3-ext-Padel-2D.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results,100, color='red', alpha=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - } - ], - "source": [ - "#Logistic regression (scikit)\n", - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "keras.backend.clear_session()\n", - "prbs=[]\n", - "model = LogisticRegression(random_state=0)\n", - "model.fit(X, y)\n", - "probas_ = model.predict_proba(x_pad)[:, 1]\n", - "results = probas_\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr2.v3-ext-Padel-2D.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results,100, color='red', alpha=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "keras.backend.clear_session()\n", - "prbs=[]\n", - "model = RandomForestClassifier(n_estimators=1000, random_state=0, max_leaf_nodes=200)\n", - "# Fit the model\n", - "model.fit(X, y)\n", - "\n", - "\n", - "probas_ = model.predict_proba(x_pad)[:, 1]\n", - "results = probas_" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.rf.v3-ext-Padel-2D.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results,100, color='red', alpha=0.5)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/scripts/tensorflow/prediction-v2-norm.ipynb b/scripts/tensorflow/prediction-v2-norm.ipynb deleted file mode 100644 index a013912..0000000 --- a/scripts/tensorflow/prediction-v2-norm.ipynb +++ /dev/null @@ -1,11720 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "from keras import optimizers, regularizers\n", - "from keras.layers import Dense, Dropout, Input\n", - "from keras.models import Model, Sequential\n", - "from random import shuffle\n", - "from scipy import interp\n", - "from sklearn.linear_model import LogisticRegression\n", - "from scipy.stats.mstats import gmean\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import roc_curve, auc\n", - "from sklearn.model_selection import StratifiedKFold, train_test_split\n", - "from sklearn.preprocessing import QuantileTransformer\n", - "import contextlib\n", - "import glob\n", - "import gzip\n", - "import h5py\n", - "import keras\n", - "import numpy as np\n", - "import os\n", - "import pandas as pd\n", - "import pylab as plt\n", - "import random\n", - "import scipy\n", - "import sklearn\n", - "import tensorflow as tf\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.datasets import make_classification\n", - "\n", - "\n", - "\n", - "random_state = np.random.RandomState(0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "X_f = '/home/drewe/notebooks/genotox/GenoTox-database.csv'\n", - "y_f = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\n", - "\n", - "X = pd.read_csv(X_f).values[:,:-1]\n", - "y = pd.read_csv(y_f).values\n", - "\n", - "\n", - "ix = [i for i in range(y.shape[0])]\n", - "shuffle(ix)\n", - "X = X[ix, :]\n", - "y = y[ix]\n", - "names = pd.read_csv(X_f)['Unnamed: 0'][ix].values\n", - "X = sklearn.preprocessing.quantile_transform(X, axis=0, output_distribution='uniform', copy=True)\n", - "y = y[: ,0]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.4109 - acc: 0.8058\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.3981 - acc: 0.8133\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.4038 - acc: 0.8046\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.3869 - acc: 0.8196\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.3724 - acc: 0.8258\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.3895 - acc: 0.8194\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.3761 - acc: 0.8219\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.3636 - acc: 0.8332\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.3596 - acc: 0.8288\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 33us/step - loss: 0.3411 - acc: 0.8392\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 33us/step - loss: 0.3469 - acc: 0.8391\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.3447 - acc: 0.8441\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.3425 - acc: 0.8409\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.3313 - acc: 0.8436\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.3159 - acc: 0.8545\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.3230 - acc: 0.8584\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.3212 - acc: 0.8553\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 33us/step - loss: 0.2947 - acc: 0.8688\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 33us/step - loss: 0.3099 - acc: 0.8623\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.3064 - acc: 0.8628\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.2890 - acc: 0.8711\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.3001 - acc: 0.8674\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.3063 - acc: 0.8599\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.2808 - acc: 0.8740\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.2679 - acc: 0.8782\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.2628 - acc: 0.8859\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.2655 - acc: 0.8805\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.2608 - acc: 0.8827\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 33us/step - loss: 0.2845 - acc: 0.8700\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.2455 - acc: 0.8903\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.2503 - acc: 0.8893\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.2449 - acc: 0.8860\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2559 - acc: 0.8886\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.2434 - acc: 0.8908\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.2328 - acc: 0.8958\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 33us/step - loss: 0.2326 - acc: 0.8981\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 33us/step - loss: 0.2380 - acc: 0.8944\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.2162 - acc: 0.9064\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.2256 - acc: 0.9006\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.2275 - acc: 0.8959\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.2220 - acc: 0.9025\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.2103 - acc: 0.9076\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2416 - acc: 0.8926\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.2079 - acc: 0.9051\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.2303 - acc: 0.8951\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.2055 - acc: 0.9103\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 33us/step - loss: 0.2086 - acc: 0.9109\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 33us/step - loss: 0.2075 - acc: 0.9102\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 33us/step - loss: 0.1959 - acc: 0.9116\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2170 - acc: 0.9040\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.3747 - acc: 0.8229\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.3632 - acc: 0.8311\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.3711 - acc: 0.8265\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.3593 - acc: 0.8317\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.3483 - acc: 0.8355\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.3388 - acc: 0.8432\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.3455 - acc: 0.8390\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.3354 - acc: 0.8478\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.3179 - acc: 0.8534\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 51us/step - loss: 0.3354 - acc: 0.8439\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.3212 - acc: 0.8537\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.3185 - acc: 0.8551\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 49us/step - loss: 0.3050 - acc: 0.8584\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.3011 - acc: 0.8612\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2976 - acc: 0.8610\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.3180 - acc: 0.8542\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2741 - acc: 0.8768\n", - "Epoch 18/50\n", - "7272/7272 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[==============================] - 0s 38us/step - loss: 0.2671 - acc: 0.8800\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2686 - acc: 0.8797\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2633 - acc: 0.8839\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2598 - acc: 0.8823\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.2733 - acc: 0.8761\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2393 - acc: 0.8914\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2345 - acc: 0.8926\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.2338 - acc: 0.8967\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2364 - acc: 0.8938\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2410 - acc: 0.8940\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2403 - acc: 0.8915\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.2193 - acc: 0.9026\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.2267 - acc: 0.8992\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.2047 - acc: 0.9097\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.2197 - acc: 0.9042\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.2153 - acc: 0.9028\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2000 - acc: 0.9098\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.2106 - acc: 0.9044\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2156 - acc: 0.9039\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2004 - acc: 0.9110\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.1857 - acc: 0.9186\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.1993 - acc: 0.9097\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.2016 - acc: 0.9131\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.2030 - acc: 0.9058\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.1882 - acc: 0.9152\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.1992 - acc: 0.9079\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.1865 - acc: 0.9213\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.1716 - acc: 0.9241\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.1760 - acc: 0.9216\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.1768 - acc: 0.9183\n", - " 0.6616299019607843\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([0.40160879, 0.23865981, 0.0192348 , ..., 0.20569094, 0.98110449,\n", - " 0.58497733])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "for train, test in cv.split(X, y):\n", - " keras.backend.clear_session()\n", - " prbs=[]\n", - " for mod in range(5):\n", - " print('>>')\n", - " curr_try = 0\n", - " while curr_try <10:\n", - " print('.')\n", - "\n", - " model = Sequential()\n", - " model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(1, activation='sigmoid'))\n", - " # Compile model\n", - " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", - " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", - " \n", - " # Fit the model\n", - " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", - " if history.history['acc'][-1] > 0.53:\n", - " break\n", - " else:\n", - " curr_try += 1\n", - "\n", - " # Fit the model\n", - " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", - " \n", - " # evaluate the model\n", - " probas_ = model.predict(X[test,:])\n", - " prbs.append(probas_)\n", - " # Average the predictions\n", - " probas_ = np.mean(np.hstack(prbs), axis=1)\n", - " results[test] = probas_\n", - " \n", - " # Compute ROC curve and area the curve\n", - " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", - " print(' ' + str(auc(fpr, tpr)))\n", - " tprs.append(interp(mean_fpr, fpr, tpr))\n", - " tprs[-1][0] = 0.0\n", - " roc_auc = auc(fpr, tpr)\n", - " aucs.append(roc_auc)\n", - " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", - " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", - "\n", - " i += 1\n", - "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", - " label='Chance', alpha=.8)\n", - "\n", - "mean_tpr = np.mean(tprs, axis=0)\n", - "mean_tpr[-1] = 1.0\n", - "mean_auc = auc(mean_fpr, mean_tpr)\n", - "std_auc = np.std(aucs)\n", - "plt.plot(mean_fpr, mean_tpr, color='b',\n", - " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", - " lw=2, alpha=.8)\n", - "\n", - "std_tpr = np.std(tprs, axis=0)\n", - "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", - "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", - "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", - " label=r'$\\pm$ 1 std. dev.')\n", - "\n", - "plt.xlim([-0.05, 1.05])\n", - "plt.ylim([-0.05, 1.05])\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('Receiver operating characteristic example')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.nn.v3.norm.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", - "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[<matplotlib.lines.Line2D at 0x7fe47ca5b470>]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "results[test] = probas_\n", - "plt.plot(results)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6205 - acc: 0.6522\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6192 - acc: 0.6506\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6182 - acc: 0.6555\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6197 - acc: 0.6536\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6192 - acc: 0.6562\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6194 - acc: 0.6528\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6172 - acc: 0.6550\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6179 - acc: 0.6526\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6167 - acc: 0.6568\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6200 - acc: 0.6535\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6179 - acc: 0.6602\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6176 - acc: 0.6591\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6201 - acc: 0.6528\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6199 - acc: 0.6536\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6160 - acc: 0.6562\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6169 - acc: 0.6566\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6169 - acc: 0.6577\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6164 - acc: 0.6561\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6156 - acc: 0.6555\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6155 - acc: 0.6568\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6168 - acc: 0.6558\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6167 - acc: 0.6587\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6151 - acc: 0.6586\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6167 - acc: 0.6568\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6168 - acc: 0.6581\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6176 - acc: 0.6575\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6145 - acc: 0.6624\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6168 - acc: 0.6536\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6193 - acc: 0.6531\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6149 - acc: 0.6581\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6155 - acc: 0.6594\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6169 - acc: 0.6565\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6153 - acc: 0.6614\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6143 - acc: 0.6612\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6150 - acc: 0.6598\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6142 - acc: 0.6565\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6152 - acc: 0.6631\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6145 - acc: 0.6555\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6144 - acc: 0.6632\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6145 - acc: 0.6588\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6137 - acc: 0.6586\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6130 - acc: 0.6624\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6134 - acc: 0.6630\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6151 - acc: 0.6572\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6139 - acc: 0.6594\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6159 - acc: 0.6612\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6150 - acc: 0.6597\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6130 - acc: 0.6632\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6127 - acc: 0.6581\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6137 - acc: 0.6580\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6192 - acc: 0.6510\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6200 - acc: 0.6528\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6193 - acc: 0.6526\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6184 - acc: 0.6517\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6207 - acc: 0.6522\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6202 - acc: 0.6533\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6184 - acc: 0.6575\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6192 - acc: 0.6546\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6182 - acc: 0.6558\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6189 - acc: 0.6542\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6189 - acc: 0.6590\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6187 - acc: 0.6573\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6188 - acc: 0.6531\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6196 - acc: 0.6507\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6198 - acc: 0.6528\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6190 - acc: 0.6587\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6183 - acc: 0.6575\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6169 - acc: 0.6566\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6176 - acc: 0.6587\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6166 - acc: 0.6564\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6196 - acc: 0.6576\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6182 - acc: 0.6584\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6183 - acc: 0.6518\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6173 - acc: 0.6565\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6189 - acc: 0.6515\n", - "Epoch 26/50\n", - "7272/7272 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"text": [ - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6149 - acc: 0.6579\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6142 - acc: 0.6602\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6148 - acc: 0.6605\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6166 - acc: 0.6553\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6142 - acc: 0.6594\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6153 - acc: 0.6554\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6147 - acc: 0.6575\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6139 - acc: 0.6590\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6140 - acc: 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0.6561\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6136 - acc: 0.6609\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6137 - acc: 0.6620\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6118 - acc: 0.6588\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6114 - acc: 0.6610\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6117 - acc: 0.6614\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6182 - acc: 0.6562\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6173 - acc: 0.6553\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6186 - acc: 0.6575\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6188 - acc: 0.6554\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6166 - acc: 0.6591\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6170 - acc: 0.6566\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6176 - acc: 0.6544\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6193 - acc: 0.6515\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6209 - acc: 0.6459\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6200 - acc: 0.6488\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6149 - acc: 0.6575\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6164 - acc: 0.6570\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6163 - acc: 0.6558\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6153 - acc: 0.6592\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6165 - acc: 0.6554\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6161 - acc: 0.6565\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6143 - acc: 0.6566\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6147 - acc: 0.6616\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6166 - acc: 0.6551\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6139 - acc: 0.6544\n", - "Epoch 21/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6150 - acc: 0.6535\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6143 - acc: 0.6564\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6140 - acc: 0.6587\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6146 - acc: 0.6558\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6150 - acc: 0.6595\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6181 - acc: 0.6553\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6136 - acc: 0.6532\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6172 - acc: 0.6526\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6141 - acc: 0.6597\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6126 - acc: 0.6588\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6135 - acc: 0.6591\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6140 - acc: 0.6586\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6144 - acc: 0.6612\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6127 - acc: 0.6566\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6130 - acc: 0.6612\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6132 - acc: 0.6581\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6175 - acc: 0.6586\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 21us/step - loss: 0.6142 - acc: 0.6577\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6125 - acc: 0.6608\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6148 - acc: 0.6564\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6126 - acc: 0.6588\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6118 - acc: 0.6638\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6130 - acc: 0.6562\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6135 - acc: 0.6612\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6138 - acc: 0.6581\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6117 - acc: 0.6605\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6113 - acc: 0.6609\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6128 - acc: 0.6584\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6122 - acc: 0.6642\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6123 - acc: 0.6586\n", - " 0.6677879901960784\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([0.70463479, 0.57834029, 0.35220766, ..., 0.52625859, 0.25496623,\n", - " 0.43516079])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Logistic regression (SGD)\n", - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "for train, test in cv.split(X, y):\n", - " keras.backend.clear_session()\n", - " prbs=[]\n", - " for mod in range(5):\n", - " print('>>')\n", - " curr_try = 0\n", - " while curr_try <10:\n", - " print('.')\n", - "\n", - " model = Sequential()\n", - " model.add(Dense(1, activation='sigmoid'))\n", - " # Compile model\n", - " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", - " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", - " \n", - " # Fit the model\n", - " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", - " if history.history['acc'][-1] > 0.53:\n", - " break\n", - " else:\n", - " curr_try += 1\n", - "\n", - " # Fit the model\n", - " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", - " \n", - " # evaluate the model\n", - " probas_ = model.predict(X[test,:])\n", - " prbs.append(probas_)\n", - " # Average the predictions\n", - " probas_ = np.mean(np.hstack(prbs), axis=1)\n", - " results[test] = probas_\n", - " \n", - " # Compute ROC curve and area the curve\n", - " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", - " print(' ' + str(auc(fpr, tpr)))\n", - " tprs.append(interp(mean_fpr, fpr, tpr))\n", - " tprs[-1][0] = 0.0\n", - " roc_auc = auc(fpr, tpr)\n", - " aucs.append(roc_auc)\n", - " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", - " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", - "\n", - " i += 1\n", - "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", - " label='Chance', alpha=.8)\n", - "\n", - "mean_tpr = np.mean(tprs, axis=0)\n", - "mean_tpr[-1] = 1.0\n", - "mean_auc = auc(mean_fpr, mean_tpr)\n", - "std_auc = np.std(aucs)\n", - "plt.plot(mean_fpr, mean_tpr, color='b',\n", - " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", - " lw=2, alpha=.8)\n", - "\n", - "std_tpr = np.std(tprs, axis=0)\n", - "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", - "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", - "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", - " label=r'$\\pm$ 1 std. dev.')\n", - "\n", - "plt.xlim([-0.05, 1.05])\n", - "plt.ylim([-0.05, 1.05])\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('Receiver operating characteristic example')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr.v3.norm.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", - "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6685537828100875\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.7202411587383281\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.7168627450980394\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6643872549019608\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.671936274509804\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6846323529411765\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6668872549019609\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6213235294117647\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6297058823529411\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6967401960784314\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6637254901960785\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.713872549019608\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6824999999999999\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6579166666666668\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.640735294117647\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6849264705882354\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6687990196078433\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6941176470588236\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6903676470588236\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([0.73577705, 0.50272183, 0.19845793, ..., 0.3937505 , 0.3072859 ,\n", - " 0.41628284])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Logistic regression (scikit)\n", - "cv = StratifiedKFold(n_splits=20)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "for train, test in cv.split(X, y):\n", - " keras.backend.clear_session()\n", - " prbs=[]\n", - " model = LogisticRegression(random_state=0)\n", - " model.fit(X[train,:], y[train])\n", - " probas_ = model.predict_proba(X[test,:])[:, 1]\n", - " results[test] = probas_\n", - " \n", - " # Compute ROC curve and area the curve\n", - " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", - " print(' ' + str(auc(fpr, tpr)))\n", - " tprs.append(interp(mean_fpr, fpr, tpr))\n", - " tprs[-1][0] = 0.0\n", - " roc_auc = auc(fpr, tpr)\n", - " aucs.append(roc_auc)\n", - " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", - " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", - "\n", - " i += 1\n", - "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", - " label='Chance', alpha=.8)\n", - "\n", - "mean_tpr = np.mean(tprs, axis=0)\n", - "mean_tpr[-1] = 1.0\n", - "mean_auc = auc(mean_fpr, mean_tpr)\n", - "std_auc = np.std(aucs)\n", - "plt.plot(mean_fpr, mean_tpr, color='b',\n", - " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", - " lw=2, alpha=.8)\n", - "\n", - "std_tpr = np.std(tprs, axis=0)\n", - "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", - "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", - "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", - " label=r'$\\pm$ 1 std. dev.')\n", - "\n", - "plt.xlim([-0.05, 1.05])\n", - "plt.ylim([-0.05, 1.05])\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('Receiver operating characteristic example')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr2.v3.norm.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", - "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - " 0.6996023454876323\n", - ">>\n", - " 0.6807397966998965\n", - ">>\n", - " 0.706390931372549\n", - ">>\n", - " 0.6830269607843136\n", - ">>\n", - " 0.6797181372549019\n", - ">>\n", - " 0.6988848039215687\n", - ">>\n", - " 0.7022120098039215\n", - ">>\n", - " 0.6979289215686274\n", - ">>\n", - " 0.7029473039215686\n", - ">>\n", - " 0.7025183823529413\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([0.54703285, 0.55016726, 0.34399391, ..., 0.4632062 , 0.38298533,\n", - " 0.47006439])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "for train, test in cv.split(X, y):\n", - " print('>>')\n", - " keras.backend.clear_session()\n", - " prbs=[]\n", - " model = RandomForestClassifier(n_estimators=1000, random_state=0, max_leaf_nodes=200)\n", - " # Fit the model\n", - " model.fit(X[train,:], y[train])\n", - "\n", - " \n", - " probas_ = model.predict_proba(X[test,:])[:, 1]\n", - " results[test] = probas_\n", - "\n", - " # Compute ROC curve and area the curve\n", - " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", - " print(' ' + str(auc(fpr, tpr)))\n", - " tprs.append(interp(mean_fpr, fpr, tpr))\n", - " tprs[-1][0] = 0.0\n", - " roc_auc = auc(fpr, tpr)\n", - " aucs.append(roc_auc)\n", - " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", - " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", - "\n", - " i += 1\n", - "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", - " label='Chance', alpha=.8)\n", - "\n", - "mean_tpr = np.mean(tprs, axis=0)\n", - "mean_tpr[-1] = 1.0\n", - "mean_auc = auc(mean_fpr, mean_tpr)\n", - "std_auc = np.std(aucs)\n", - "plt.plot(mean_fpr, mean_tpr, color='b',\n", - " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", - " lw=2, alpha=.8)\n", - "\n", - "std_tpr = np.std(tprs, axis=0)\n", - "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", - "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", - "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", - " label=r'$\\pm$ 1 std. dev.')\n", - "\n", - "plt.xlim([-0.05, 1.05])\n", - "plt.ylim([-0.05, 1.05])\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('Receiver operating characteristic example')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.rf.v3.norm.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", - "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/scripts/tensorflow/prediction-v5-ext.ipynb b/scripts/tensorflow/prediction-v5-ext.ipynb new file mode 100644 index 0000000..c1b24b4 --- /dev/null +++ b/scripts/tensorflow/prediction-v5-ext.ipynb @@ -0,0 +1,11248 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras import optimizers, regularizers\n", + "from keras.layers import Dense, Dropout, Input\n", + "from keras.models import Model, Sequential\n", + "from random import shuffle\n", + "from scipy import interp\n", + "from sklearn.linear_model import LogisticRegression\n", + "from scipy.stats.mstats import gmean\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import roc_curve, auc\n", + "from sklearn.model_selection import StratifiedKFold, train_test_split\n", + "from sklearn.preprocessing import QuantileTransformer\n", + "import contextlib\n", + "import glob\n", + "import gzip\n", + "import h5py\n", + "import keras\n", + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "import pylab as plt\n", + "import random\n", + "import scipy\n", + "import sklearn\n", + "import tensorflow as tf\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.datasets import make_classification\n", + "\n", + "\n", + "\n", + "random_state = np.random.RandomState(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "X_f_ext = '/home/drewe/notebooks/genotox/mutagenicity-fingerprints.csv'\n", + "\n", + "X = pd.read_csv(X_f_ext,sep=',')\n", + "X['Mutagenicity_bin'] = np.int32(X['Mutagenicity'] == 'mutagenic')\n", + "del X['Mutagenicity']\n", + "\n", + "X_f_ext = '/home/drewe/notebooks/genotox/mutagenicity-mod-2.csv'\n", + "\n", + "X_ext = pd.read_csv(X_f_ext,sep=';')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#X = pd.merge(X_ext,X[['Canonical SMILES','Mutagenicity_bin']], right_on='Canonical SMILES', left_on='Name')\n", + "y = X['Mutagenicity_bin'].values\n", + "del X['Mutagenicity_bin']\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "names = X['Canonical SMILES'].values\n", + "X = np.float64(X.values[:,1:])\n", + "\n", + "\n", + "ix = [i for i in range(y.shape[0])]\n", + "shuffle(ix)\n", + "X = X[ix, :]\n", + "names = names[ix]\n", + "y = y[ix]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8309, 9638)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#X = sklearn.preprocessing.quantile_transform(X, axis=1, output_distribution='uniform', copy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0163 - acc: 0.9900\n", + "Epoch 2/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0161 - acc: 0.9910\n", + "Epoch 3/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0145 - acc: 0.9909\n", + "Epoch 4/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0138 - acc: 0.9912\n", + "Epoch 5/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0134 - acc: 0.9905\n", + "Epoch 6/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0134 - acc: 0.9906\n", + "Epoch 7/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0132 - acc: 0.9912\n", + "Epoch 8/50\n", + "7478/7478 [==============================] - 1s 78us/step - loss: 0.0132 - acc: 0.9910\n", + "Epoch 9/50\n", + "7478/7478 [==============================] - 1s 78us/step - loss: 0.0132 - acc: 0.9914\n", + "Epoch 10/50\n", + "7478/7478 [==============================] - 1s 78us/step - loss: 0.0131 - acc: 0.9918\n", + "Epoch 11/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0129 - acc: 0.9920\n", + "Epoch 12/50\n", + "7478/7478 [==============================] - 1s 78us/step - loss: 0.0132 - acc: 0.9905\n", + "Epoch 13/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0130 - acc: 0.9912\n", + "Epoch 14/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0129 - acc: 0.9909\n", + "Epoch 15/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0130 - acc: 0.9910\n", + "Epoch 16/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0128 - acc: 0.9918\n", + "Epoch 17/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0129 - acc: 0.9918\n", + "Epoch 18/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0130 - acc: 0.9905\n", + "Epoch 19/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0129 - acc: 0.9920\n", + "Epoch 20/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0128 - acc: 0.9921\n", + "Epoch 21/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0129 - acc: 0.9914\n", + "Epoch 22/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0127 - acc: 0.9912\n", + "Epoch 23/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0126 - acc: 0.9925\n", + "Epoch 24/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0127 - acc: 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loss: 0.0125 - acc: 0.9913\n", + "Epoch 34/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0126 - acc: 0.9914\n", + "Epoch 35/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0126 - acc: 0.9921\n", + "Epoch 36/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0126 - acc: 0.9918\n", + "Epoch 37/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0132 - acc: 0.9914\n", + "Epoch 38/50\n", + "7478/7478 [==============================] - 1s 75us/step - loss: 0.0127 - acc: 0.9922\n", + "Epoch 39/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0127 - acc: 0.9918\n", + "Epoch 40/50\n", + "7478/7478 [==============================] - 1s 75us/step - loss: 0.0124 - acc: 0.9914\n", + "Epoch 41/50\n", + "7478/7478 [==============================] - 1s 78us/step - loss: 0.0124 - acc: 0.9921\n", + "Epoch 42/50\n", + "7478/7478 [==============================] 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0.9928\n", + "Epoch 48/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0104 - acc: 0.9934\n", + "Epoch 49/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0104 - acc: 0.9932\n", + "Epoch 50/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9930\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9924\n", + "Epoch 2/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9928\n", + "Epoch 3/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9926\n", + "Epoch 4/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0109 - acc: 0.9930\n", + "Epoch 5/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9928\n", + "Epoch 6/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9922\n", + "Epoch 7/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0109 - acc: 0.9926\n", + "Epoch 8/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0108 - acc: 0.9928\n", + "Epoch 9/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0108 - acc: 0.9921\n", + "Epoch 10/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9928\n", + "Epoch 11/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9926\n", + "Epoch 12/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9932\n", + "Epoch 13/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0108 - acc: 0.9924\n", + "Epoch 14/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9924\n", + "Epoch 15/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9930\n", + "Epoch 16/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9930\n", + "Epoch 17/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9926\n", + "Epoch 18/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9921\n", + "Epoch 19/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0106 - acc: 0.9925\n", + "Epoch 20/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9932\n", + "Epoch 21/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9924\n", + "Epoch 22/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9930\n", + "Epoch 23/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9933\n", + "Epoch 24/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9921\n", + "Epoch 25/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9926\n", + "Epoch 26/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9932\n", + "Epoch 27/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9925\n", + "Epoch 28/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9924\n", + "Epoch 29/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9921\n", + "Epoch 30/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9930\n", + "Epoch 31/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9924\n", + "Epoch 32/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9934\n", + "Epoch 33/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9922\n", + "Epoch 34/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0104 - acc: 0.9921\n", + "Epoch 35/50\n", + "7478/7478 [==============================] - 1s 74us/step - loss: 0.0103 - acc: 0.9924\n", + "Epoch 36/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9929\n", + "Epoch 37/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9934\n", + "Epoch 38/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9929\n", + "Epoch 39/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9932\n", + "Epoch 40/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9934\n", + "Epoch 41/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9928\n", + "Epoch 42/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9926\n", + "Epoch 43/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9925\n", + "Epoch 44/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9929\n", + "Epoch 45/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9929\n", + "Epoch 46/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0103 - acc: 0.9926\n", + "Epoch 47/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0103 - acc: 0.9934\n", + "Epoch 48/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0103 - acc: 0.9932\n", + "Epoch 49/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9937\n", + "Epoch 50/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0102 - acc: 0.9928\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0115 - acc: 0.9917\n", + "Epoch 2/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9926\n", + "Epoch 3/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9924\n", + "Epoch 4/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9918\n", + "Epoch 5/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0111 - acc: 0.9920\n", + "Epoch 6/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9933\n", + "Epoch 7/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0108 - acc: 0.9932\n", + "Epoch 8/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9932\n", + "Epoch 9/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0111 - acc: 0.9926\n", + "Epoch 10/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9928\n", + "Epoch 11/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9925\n", + "Epoch 12/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9921\n", + "Epoch 13/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0108 - acc: 0.9929\n", + "Epoch 14/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9925\n", + "Epoch 15/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0108 - acc: 0.9930\n", + "Epoch 16/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9929\n", + "Epoch 17/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9933\n", + "Epoch 18/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9926\n", + "Epoch 19/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9933\n", + "Epoch 20/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9930\n", + "Epoch 21/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9928\n", + "Epoch 22/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9926\n", + "Epoch 23/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9928\n", + "Epoch 24/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9928\n", + "Epoch 25/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0107 - acc: 0.9929\n", + "Epoch 26/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0108 - acc: 0.9926\n", + "Epoch 27/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0107 - acc: 0.9930\n", + "Epoch 28/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9924\n", + "Epoch 29/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9926\n", + "Epoch 30/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9929\n", + "Epoch 31/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9934\n", + "Epoch 32/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9930\n", + "Epoch 33/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0106 - acc: 0.9929\n", + "Epoch 34/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9930\n", + "Epoch 35/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9928\n", + "Epoch 36/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9924\n", + "Epoch 37/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0106 - acc: 0.9926\n", + "Epoch 38/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9934\n", + "Epoch 39/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9930\n", + "Epoch 40/50\n", + "7478/7478 [==============================] - 1s 74us/step - loss: 0.0105 - acc: 0.9936\n", + "Epoch 41/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9930\n", + "Epoch 42/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9929\n", + "Epoch 43/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9930\n", + "Epoch 44/50\n", + "7478/7478 [==============================] 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"7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9929\n", + "Epoch 13/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9930\n", + "Epoch 14/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0107 - acc: 0.9933\n", + "Epoch 15/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0106 - acc: 0.9926\n", + "Epoch 16/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0106 - acc: 0.9933\n", + "Epoch 17/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0106 - acc: 0.9932\n", + "Epoch 18/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9933\n", + "Epoch 19/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9933\n", + "Epoch 20/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0106 - acc: 0.9929\n", + "Epoch 21/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9929\n", + "Epoch 22/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0105 - acc: 0.9926\n", + "Epoch 23/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0105 - acc: 0.9932\n", + "Epoch 24/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0105 - acc: 0.9929\n", + "Epoch 25/50\n", + "7478/7478 [==============================] - 1s 74us/step - loss: 0.0104 - acc: 0.9933\n", + "Epoch 26/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0104 - acc: 0.9929\n", + "Epoch 27/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0104 - acc: 0.9924\n", + "Epoch 28/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9936\n", + "Epoch 29/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0104 - acc: 0.9938\n", + "Epoch 30/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0104 - acc: 0.9934\n", + "Epoch 31/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0104 - acc: 0.9925\n", + "Epoch 32/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0104 - acc: 0.9929\n", + "Epoch 33/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9936\n", + "Epoch 34/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9925\n", + "Epoch 35/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0104 - acc: 0.9930\n", + "Epoch 36/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9922\n", + "Epoch 37/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0104 - acc: 0.9930\n", + "Epoch 38/50\n", + "7478/7478 [==============================] - 1s 72us/step 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43/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9930\n", + "Epoch 44/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0103 - acc: 0.9922\n", + "Epoch 45/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0104 - acc: 0.9930\n", + "Epoch 46/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0103 - acc: 0.9933\n", + "Epoch 47/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0103 - acc: 0.9930\n", + "Epoch 48/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0103 - acc: 0.9925\n", + "Epoch 49/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0103 - acc: 0.9928\n", + "Epoch 50/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9929\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 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loss: 0.0104 - acc: 0.9917\n", + "Epoch 46/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9925\n", + "Epoch 47/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9936\n", + "Epoch 48/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0103 - acc: 0.9933\n", + "Epoch 49/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9928\n", + "Epoch 50/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9926\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9925\n", + "Epoch 2/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0111 - acc: 0.9922\n", + "Epoch 3/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9922\n", + "Epoch 4/50\n", + "7478/7478 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44/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9922\n", + "Epoch 45/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9917\n", + "Epoch 46/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0106 - acc: 0.9922\n", + "Epoch 47/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9920\n", + "Epoch 48/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0108 - acc: 0.9928\n", + "Epoch 49/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9918\n", + "Epoch 50/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9932\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0214 - acc: 0.9898\n", + "Epoch 2/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 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29/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0115 - acc: 0.9925\n", + "Epoch 30/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9916\n", + "Epoch 31/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0111 - acc: 0.9921\n", + "Epoch 32/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9929\n", + "Epoch 33/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9925\n", + "Epoch 34/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0109 - acc: 0.9925\n", + "Epoch 35/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9925\n", + "Epoch 36/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9930\n", + "Epoch 37/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0114 - acc: 0.9929\n", + "Epoch 38/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9929\n", + "Epoch 39/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9920\n", + "Epoch 40/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9921\n", + "Epoch 41/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0108 - acc: 0.9914\n", + "Epoch 42/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9926\n", + "Epoch 43/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9922\n", + "Epoch 44/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9928\n", + "Epoch 45/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9932\n", + "Epoch 46/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9918\n", + "Epoch 47/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0108 - acc: 0.9922\n", + "Epoch 48/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9925\n", + "Epoch 49/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0108 - acc: 0.9928\n", + "Epoch 50/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9933\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0119 - acc: 0.9910\n", + "Epoch 2/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0118 - acc: 0.9922\n", + "Epoch 3/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0123 - acc: 0.9916\n", + "Epoch 4/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 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31/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9926\n", + "Epoch 32/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9926\n", + "Epoch 33/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9933\n", + "Epoch 34/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9930\n", + "Epoch 35/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0109 - acc: 0.9928\n", + "Epoch 36/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9925\n", + "Epoch 37/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0108 - acc: 0.9918\n", + "Epoch 38/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9933\n", + "Epoch 39/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 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[==============================] - 1s 73us/step - loss: 0.0113 - acc: 0.9916\n", + "Epoch 13/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0114 - acc: 0.9921\n", + "Epoch 14/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0113 - acc: 0.9925\n", + "Epoch 15/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0113 - acc: 0.9925\n", + "Epoch 16/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0114 - acc: 0.9926\n", + "Epoch 17/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0113 - acc: 0.9918\n", + "Epoch 18/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0112 - acc: 0.9921\n", + "Epoch 19/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9924\n", + "Epoch 20/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9920\n", + "Epoch 21/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0111 - acc: 0.9918\n", + "Epoch 22/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0114 - acc: 0.9913\n", + "Epoch 23/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9920\n", + "Epoch 24/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0113 - acc: 0.9922\n", + "Epoch 25/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9930\n", + "Epoch 26/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9922\n", + "Epoch 27/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9922\n", + "Epoch 28/50\n", + "7479/7479 [==============================] - 1s 74us/step - loss: 0.0111 - acc: 0.9920\n", + "Epoch 29/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9913\n", + "Epoch 30/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9922\n", + "Epoch 31/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9920\n", + "Epoch 32/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9925\n", + "Epoch 33/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9930\n", + "Epoch 34/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9924\n", + "Epoch 35/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9920\n", + "Epoch 36/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9918\n", + "Epoch 37/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9920\n", + "Epoch 38/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9926\n", + "Epoch 39/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0109 - acc: 0.9921\n", + "Epoch 40/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0109 - acc: 0.9917\n", + "Epoch 41/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0109 - acc: 0.9918\n", + "Epoch 42/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0116 - acc: 0.9924\n", + "Epoch 43/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9926\n", + "Epoch 44/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9920\n", + "Epoch 45/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9922\n", + "Epoch 46/50\n", + "7479/7479 [==============================] - 1s 75us/step - loss: 0.0110 - acc: 0.9916\n", + "Epoch 47/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9918\n", + "Epoch 48/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9917\n", + "Epoch 49/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9922\n", + "Epoch 50/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9918\n", + " 0.9073547090696851\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([1.31914479e-08, 2.52192222e-05, 9.99993205e-01, ...,\n", + " 1.42529135e-08, 1.00000000e+00, 1.18783440e-07])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " for mod in range(5):\n", + " print('>>')\n", + " curr_try = 0\n", + " while curr_try <10:\n", + " print('.')\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(1, activation='sigmoid'))\n", + " # Compile model\n", + " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", + " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + " \n", + " # Fit the model\n", + " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", + " if history.history['acc'][-1] > 0.53:\n", + " break\n", + " else:\n", + " curr_try += 1\n", + "\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", + " \n", + " # evaluate the model\n", + " probas_ = model.predict(X[test,:])\n", + " prbs.append(probas_)\n", + " # Average the predictions\n", + " probas_ = np.mean(np.hstack(prbs), axis=1)\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.nn.v4_ext.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mAHCgzf594Mk17HIL8OdrWH8z6m3MvY0XHHMv1jLmH6yqrcs1mvYRwBywfWz+YuDkeIOqOgQcmsTOksxU1a5JbGuz6G3MvY0XHHMvpjHmad8F9BCwM8klSc4D9gFHp9wHSRJTPgKoqteS3ATcA5wDHK6qE9PsgyRpZNqngKiqY8CxKe1uIqeSNpnextzbeMEx92LdxzzVi8CSpLcOPwpCkjq16QNguY+WSPKuJF9vyx9MsmP6vZysAWP+F0keT/JoknuT/OBG9HOShn6ESJJPJKkkm/6OkSFjTvKP23N9IslvTruPkzbgtf13k9yX5Fvt9X3tRvRzUpIcTnIqyWOLLE+SL7bvx6NJLp9oB6pq034xupD8p8DfA84D/gS49Kw2/wz4tTa9D/j6Rvd7CmP+MeBvtumf62HMrd17gG8ADwC7NrrfU3iedwLfAi5o8xdudL+nMOZDwM+16UuBZza632sc8z8ELgceW2T5tcB/BgJcCTw4yf1v9iOA///RElX1f4DTHy0xbi9wpE3/NnBVkkyxj5O27Jir6r6qeqXNPsDo/Rab2ZDnGeDzwL8D/nqanVsnQ8b8T4EvVdVLAFV1asp9nLQhYy7gb7fp7+es9xFtNlX1DeDFJZrsBb5aIw8A5ye5aFL73+wBsNBHS2xbrE1VvQa8DPzAVHq3PoaMedwNjP6C2MyWHXOSDwHbq+r3p9mxdTTkeX4/8P4k/z3JA0n2TK1362PImA8CP51kjtHdhD/P29tKf95XZOq3gU7YQn/Jn31b05A2m8ng8ST5aWAX8I/WtUfrb8kxJ3kHcBvwM9Pq0BQMeZ7PZXQaaDejo7z/luRHquq769y39TJkzNcDX6mqLyT5B8CvtzF/b/27tyHW9ffXZj8CWPajJcbbJDmX0WHjUodcb3VDxkySnwD+FfCxqnp1Sn1bL8uN+T3AjwD3J3mG0bnSo5v8QvDQ1/bdVfV/q+o7jD43a+eU+rcehoz5BuAugKr6H8D3MfrMnLerQT/vq7XZA2DIR0scBfa36U8Af1jt6somteyY2+mQ/8jol/9mPy8My4y5ql6uqi1VtaOqdjC67vGxqprZmO5OxJDX9n9idMGfJFsYnRJ6eqq9nKwhY34WuAogyQcYBcD8VHs5XUeBT7W7ga4EXq6q5ye18U19CqgW+WiJJJ8DZqrqKHAHo8PEWUZ/+e/buB6v3cAx/3vg3cBvtevdz1bVxzas02s0cMxvKwPHfA9wdZLHgdeBf1lVf7FxvV6bgWP+DPDlJP+c0amQn9nMf9Al+RqjU3hb2nWNW4B3AlTVrzG6znEtMAu8Anx6ovvfxN87SdIabPZTQJKkVTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnq1P8D81c+diZz8iAAAAAASUVORK5CYII=\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7fb2b11d67f0>]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results[test] = probas_\n", + "plt.plot(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2395 - acc: 0.9192\n", + "Epoch 2/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2380 - acc: 0.9207\n", + "Epoch 3/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2366 - acc: 0.9206\n", + "Epoch 4/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2352 - acc: 0.9215\n", + "Epoch 5/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2340 - acc: 0.9212\n", + "Epoch 6/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2326 - acc: 0.9222\n", + "Epoch 7/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2314 - acc: 0.9230\n", + "Epoch 8/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2301 - acc: 0.9230\n", + "Epoch 9/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2289 - acc: 0.9239\n", + "Epoch 10/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2277 - acc: 0.9240\n", + "Epoch 11/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2265 - acc: 0.9252\n", + "Epoch 12/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2254 - acc: 0.9244\n", + "Epoch 13/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2243 - acc: 0.9246\n", + "Epoch 14/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2233 - acc: 0.9252\n", + "Epoch 15/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2221 - acc: 0.9260\n", + "Epoch 16/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2210 - acc: 0.9258\n", + "Epoch 17/50\n", + "7478/7478 [==============================] - 0s 45us/step - loss: 0.2201 - acc: 0.9260\n", + "Epoch 18/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2191 - acc: 0.9267\n", + "Epoch 19/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2181 - acc: 0.9274\n", + "Epoch 20/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2171 - acc: 0.9279\n", + "Epoch 21/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2162 - acc: 0.9278\n", + "Epoch 22/50\n", + "7478/7478 [==============================] - 0s 42us/step - loss: 0.2153 - acc: 0.9279\n", + "Epoch 23/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2143 - acc: 0.9282\n", + "Epoch 24/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2134 - acc: 0.9281\n", + "Epoch 25/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2126 - acc: 0.9291\n", + "Epoch 26/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2117 - acc: 0.9298\n", + "Epoch 27/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2109 - acc: 0.9298\n", + "Epoch 28/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2100 - acc: 0.9294\n", + "Epoch 29/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2092 - acc: 0.9302\n", + "Epoch 30/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2084 - acc: 0.9305\n", + "Epoch 31/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2076 - acc: 0.9305\n", + "Epoch 32/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2068 - acc: 0.9306\n", + "Epoch 33/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2061 - acc: 0.9303\n", + "Epoch 34/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2054 - acc: 0.9307\n", + "Epoch 35/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2046 - acc: 0.9310\n", + "Epoch 36/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2039 - acc: 0.9313\n", + "Epoch 37/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2031 - acc: 0.9314\n", + "Epoch 38/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2025 - acc: 0.9310\n", + "Epoch 39/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2018 - acc: 0.9322\n", + "Epoch 40/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2011 - acc: 0.9319\n", + "Epoch 41/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2004 - acc: 0.9315\n", + "Epoch 42/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1998 - acc: 0.9323\n", + "Epoch 43/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1991 - acc: 0.9327\n", + "Epoch 44/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1985 - acc: 0.9323\n", + "Epoch 45/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1979 - acc: 0.9329\n", + "Epoch 46/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.1972 - acc: 0.9331\n", + "Epoch 47/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1966 - acc: 0.9335\n", + "Epoch 48/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1960 - acc: 0.9334\n", + "Epoch 49/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1954 - acc: 0.9343\n", + "Epoch 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- 0s 44us/step - loss: 0.1995 - acc: 0.9315\n", + "Epoch 43/50\n", + "7479/7479 [==============================] - 0s 44us/step - loss: 0.1988 - acc: 0.9317\n", + "Epoch 44/50\n", + "7479/7479 [==============================] - 0s 43us/step - loss: 0.1981 - acc: 0.9318\n", + "Epoch 45/50\n", + "7479/7479 [==============================] - 0s 44us/step - loss: 0.1975 - acc: 0.9322\n", + "Epoch 46/50\n", + "7479/7479 [==============================] - 0s 44us/step - loss: 0.1969 - acc: 0.9325\n", + "Epoch 47/50\n", + "7479/7479 [==============================] - 0s 44us/step - loss: 0.1963 - acc: 0.9327\n", + "Epoch 48/50\n", + "7479/7479 [==============================] - 0s 44us/step - loss: 0.1957 - acc: 0.9325\n", + "Epoch 49/50\n", + "7479/7479 [==============================] - 0s 43us/step - loss: 0.1951 - acc: 0.9329\n", + "Epoch 50/50\n", + "7479/7479 [==============================] - 0s 44us/step - loss: 0.1945 - acc: 0.9337\n", + " 0.9092741115866867\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.191219 , 0.27326334, 0.97852647, ..., 0.04910662, 0.94311446,\n", + " 0.1081358 ])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Logistic regression (SGD)\n", + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " for mod in range(5):\n", + " print('>>')\n", + " curr_try = 0\n", + " while curr_try <10:\n", + " print('.')\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(1, activation='sigmoid'))\n", + " # Compile model\n", + " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", + " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + " \n", + " # Fit the model\n", + " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", + " if history.history['acc'][-1] > 0.53:\n", + " break\n", + " else:\n", + " curr_try += 1\n", + "\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", + " \n", + " # evaluate the model\n", + " probas_ = model.predict(X[test,:])\n", + " prbs.append(probas_)\n", + " # Average the predictions\n", + " probas_ = np.mean(np.hstack(prbs), axis=1)\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr.v4_ext.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.9098222459124671\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.9059896406679103\n", + " 0.9144572938272749\n", + " 0.9007954900983789\n", + " 0.9133072225634132\n", + " 0.895995318601606\n", + " 0.9145124847923064\n", + " 0.8979781009211516\n", + " 0.9030154683969642\n", + " 0.9073663242135096\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.3525606 , 0.2494471 , 0.98007672, ..., 0.06643755, 0.86553187,\n", + " 0.11520544])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Logistic regression (scikit)\n", + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " model = LogisticRegression(random_state=0)\n", + " model.fit(X[train,:], y[train])\n", + " probas_ = model.predict_proba(X[test,:])[:, 1]\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr2.v4_ext.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + " 0.8903898075296354\n", + ">>\n", + " 0.8864558106119422\n", + ">>\n", + " 0.8847234614537828\n", + ">>\n", + " 0.8610557480387954\n", + ">>\n", + " 0.8935995782106398\n", + ">>\n", + " 0.8672348462902236\n", + ">>\n", + " 0.8833903018365101\n", + ">>\n", + " 0.8821331324952205\n", + ">>\n", + " 0.8771073518336134\n", + ">>\n", + " 0.8798936052825674\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.2541524 , 0.23359182, 0.80675604, ..., 0.18775895, 0.49521864,\n", + " 0.27560522])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " print('>>')\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " model = RandomForestClassifier(n_estimators=1000, random_state=0, max_leaf_nodes=200)\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train])\n", + "\n", + " \n", + " probas_ = model.predict_proba(X[test,:])[:, 1]\n", + " results[test] = probas_\n", + "\n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.rf.v4_ext.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/tensorflow/prediction-v5.ipynb b/scripts/tensorflow/prediction-v5.ipynb new file mode 100644 index 0000000..eb89ba3 --- /dev/null +++ b/scripts/tensorflow/prediction-v5.ipynb @@ -0,0 +1,11600 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras import optimizers, regularizers\n", + "from keras.layers import Dense, Dropout, Input\n", + "from keras.models import Model, Sequential\n", + "from random import shuffle\n", + "from scipy import interp\n", + "from sklearn.linear_model import LogisticRegression\n", + "from scipy.stats.mstats import gmean\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import roc_curve, auc\n", + "from sklearn.model_selection import StratifiedKFold, train_test_split\n", + "from sklearn.preprocessing import QuantileTransformer\n", + "import contextlib\n", + "import glob\n", + "import gzip\n", + "import h5py\n", + "import keras\n", + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "import pylab as plt\n", + "import random\n", + "import scipy\n", + "import sklearn\n", + "import tensorflow as tf\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.datasets import make_classification\n", + "\n", + "\n", + "\n", + "random_state = np.random.RandomState(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"X_f = '/home/drewe/notebooks/genotox/GenoTox-database.csv'\\ny_f = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\\n\\nX = pd.read_csv(X_f).values[:,:-1]\\ny = pd.read_csv(y_f).values\\n\\n\\nix = [i for i in range(y.shape[0])]\\nshuffle(ix)\\nX = X[ix, :]\\ny = y[ix]\\nnames = pd.read_csv(X_f)['Unnamed: 0'][ix].values\\nX = sklearn.preprocessing.quantile_transform(X, axis=1, output_distribution='uniform', copy=True)\\ny = y[: ,0]\\n\"" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''X_f = '/home/drewe/notebooks/genotox/GenoTox-database.csv'\n", + "y_f = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\n", + "\n", + "X = pd.read_csv(X_f).values[:,:-1]\n", + "y = pd.read_csv(y_f).values\n", + "\n", + "\n", + "ix = [i for i in range(y.shape[0])]\n", + "shuffle(ix)\n", + "X = X[ix, :]\n", + "y = y[ix]\n", + "names = pd.read_csv(X_f)['Unnamed: 0'][ix].values\n", + "X = sklearn.preprocessing.quantile_transform(X, axis=1, output_distribution='uniform', copy=True)\n", + "y = y[: ,0]\n", + "'''\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "X_f_ext = '/home/drewe/notebooks/genotox/mutagenicity-fingerprints.csv'\n", + "\n", + "X = pd.read_csv(X_f_ext,sep=',')\n", + "X['Mutagenicity_bin'] = np.int32(X['Mutagenicity'] == 'mutagenic')\n", + "del X['Mutagenicity']\n", + "\n", + "X_f_ext = '/home/drewe/notebooks/genotox/mutagenicity-mod-2.csv'\n", + "\n", + "X_ext = pd.read_csv(X_f_ext,sep=';')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "X = pd.merge(X[['Canonical SMILES','Mutagenicity_bin']], X_ext, left_on='Canonical SMILES', right_on='Name')\n", + "y = X['Mutagenicity_bin'].values\n", + "del X['Mutagenicity_bin']\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "names = X['Name'].values\n", + "del X['Name']\n", + "\n", + "X = np.float64(X.values[:,1:])\n", + "\n", + "ix = [i for i in range(y.shape[0])]\n", + "shuffle(ix)\n", + "X = X[ix, :]\n", + "names = names[ix]\n", + "y = y[ix]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "X = sklearn.preprocessing.quantile_transform(X, axis=1, output_distribution='uniform', copy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8083, 1442)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7274/7274 [==============================] - 0s 42us/step - loss: 0.4451 - acc: 0.7986\n", + "Epoch 2/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4496 - acc: 0.7961\n", + "Epoch 3/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4410 - acc: 0.8000\n", + "Epoch 4/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4555 - acc: 0.7903\n", + "Epoch 5/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4735 - acc: 0.7792\n", + "Epoch 6/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4449 - acc: 0.7994\n", + "Epoch 7/50\n", + "7274/7274 [==============================] - 0s 36us/step - loss: 0.4403 - acc: 0.8016\n", + "Epoch 8/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4437 - acc: 0.8008\n", + "Epoch 9/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4498 - acc: 0.7906\n", + "Epoch 10/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4442 - acc: 0.7993\n", + "Epoch 11/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4411 - acc: 0.7996\n", + "Epoch 12/50\n", + "7274/7274 [==============================] - 0s 36us/step - 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44/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7275/7275 [==============================] - 0s 40us/step - loss: 0.3833 - acc: 0.8316\n", + "Epoch 45/50\n", + "7275/7275 [==============================] - 0s 35us/step - loss: 0.3909 - acc: 0.8324\n", + "Epoch 46/50\n", + "7275/7275 [==============================] - 0s 37us/step - loss: 0.3934 - acc: 0.8279\n", + "Epoch 47/50\n", + "7275/7275 [==============================] - 0s 36us/step - loss: 0.3959 - acc: 0.8279\n", + "Epoch 48/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3863 - acc: 0.8304\n", + "Epoch 49/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3929 - acc: 0.8294\n", + "Epoch 50/50\n", + "7275/7275 [==============================] - 0s 35us/step - loss: 0.3817 - acc: 0.8331\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.4483 - acc: 0.8003\n", + "Epoch 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0.8275\n", + "Epoch 36/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3791 - acc: 0.8326\n", + "Epoch 37/50\n", + "7275/7275 [==============================] - 0s 37us/step - loss: 0.3924 - acc: 0.8256\n", + "Epoch 38/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3877 - acc: 0.8297\n", + "Epoch 39/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3971 - acc: 0.8230\n", + "Epoch 40/50\n", + "7275/7275 [==============================] - 0s 37us/step - loss: 0.3947 - acc: 0.8195\n", + "Epoch 41/50\n", + "7275/7275 [==============================] - 0s 36us/step - loss: 0.3782 - acc: 0.8330\n", + "Epoch 42/50\n", + "7275/7275 [==============================] - 0s 36us/step - loss: 0.3968 - acc: 0.8214\n", + "Epoch 43/50\n", + "7275/7275 [==============================] - 0s 40us/step - loss: 0.3823 - acc: 0.8297\n", + "Epoch 44/50\n", + "7275/7275 [==============================] - 0s 36us/step - loss: 0.3902 - acc: 0.8293\n", + "Epoch 45/50\n", + "7275/7275 [==============================] - 0s 41us/step - loss: 0.3761 - acc: 0.8326\n", + "Epoch 46/50\n", + "7275/7275 [==============================] - 0s 37us/step - loss: 0.3752 - acc: 0.8362\n", + "Epoch 47/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3739 - acc: 0.8334\n", + "Epoch 48/50\n", + "7275/7275 [==============================] - 0s 37us/step - loss: 0.3840 - acc: 0.8296\n", + "Epoch 49/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3778 - acc: 0.8351\n", + "Epoch 50/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3810 - acc: 0.8329\n", + " 0.8730056613484303\n" + ] + }, + { + "data": { + "image/png": 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LFlG4LHUtcSM9iygibDqO0BCq3XVNe+q1tkXwt7DbpPI+1ni9cOAAVFQYxyEhRo/B2pJt6/hw3KakKqWuUUolKKXsSqmuSqnXlVL/9ikElME0pVQvpdQQ3ybjJibtFqvVSmpqKoMHD+biiy+mrKzMH7ZlyxbOPvts+vbtS58+ffjrX/9azwyyZMkS0tLSGDBgAP379+eee+7B6XTW+5SXl3P22WeTkpLC22/Nobq6kurKMqpKCqgqLaSksJzKMhfnTfwVq1aspbrKRpUznIqaaEor4vj37M+44w9/YX9RDAeKosgvCqewOIzColDue+ARRp2RytnnjOL7HzZTWW2nttaGy2VF89pAt+Oq9XDTjRNBB7vFisMK8997gbQR4VjVfmKjKukYU8Y3S2fx8j9vo09yOT16Kbomw42/m8SuwgzCutrRoz088Pi9jBqTwllnj+TKyZPYsW0jkeEOIsMdhIcFoxyhODXBZrMSEx5MTFgw0WEO/ycq1IHFYsFisfgVwN13302fPn0YOnQo69evb/I/Sk9PZ8iQIaSkpDBp0iSKiooAmDJlCqmpqaSmppKUlERqaioAP/30EzfeeONxemIw7GXl5bBrl6EQLBbo1AmSkiCo5QHl40W7c51tYnKyEhISQkZGBgA33HADr7zyCn/605+ora3lkksuYdasWZx33nnU1NRw+eWX8+KLL3LbbbexZcsWpk2bxsKFC+nXrx9er5fXX3+doKAgXy9A4XG5Wb92HW6XmxVfrcCreSkrduH1WnF7wnB5g1C6BQTcXisllaEUVoTXk8/jretVGK11sRit8hXfLWFv7g6WLv+JTRvX8H9/vZMl/12Gzar7W/qa183sN1/n15dPpFeXIn+a//tiHqedNoxVa/7H1KlTEREkPATNZqfGEYXTq6MUeAGPApuycM9tt5GUlETWzp1YLBaysrLYtm0bkSFHPtVyyZIlZGZmkpmZyerVq7n99ttZvXp1vTher5e7776brVu3EhcXx4MPPsjLL7/MzJkzmT9/vj/efffdR1RUFABDhgwhNzeXnJyc4zPz8cABKC01foeFQUJCmymDOkw3FyYmx5C6Vn1aWho5OTk4nU7eeustRo0axVlnnklNZSVK03jmb3/j2WeewaJ7eP7ZZ3j4gfvp17snmstDTUUNV06+jrycMnKyK8nOqmHDhnxu+O3v2LjpJ0aNHceajCIWf7GeSReezYUXjeKxR29FtFpCbR6sogixeol0uPjis9e47OL+/P7mcWRu/YbIEBc9EwpJ6lxE945FdI0r5vuVH/Lb6y4lMbKMSWf1paqimKrSTIKCvVjsOsoKtmAHHy76hMlXXU1QVByeiFi2F5RRXVPD/TMe593571OqQYlX4dUVogSLBmEWCwlhDoIsFuKCgyjcl8P6tT/y96efwmIxqp+ePXty4YUXHtV9X7RokV8pjRo1irKyMvbvrz9EWadgq6urUUpRUVFBYmJiozjvv/8+11xzjf/cxRdfzLx5845KvmaJiDBMRImJ0L17mysEMHsKJqcoX23NP+ZpThzYqd6x0+n0VzR1WJXCW1PD8qVLufG666C2ls0ZGQzpP5Ca0lq8KDyalcjIJCoqq9jycxUbMrYx+Yp72bnL4UvF4Z8pU5dyVFQ3/jLzVd566zlenbUQr6eaW26ayLy5/2VA357c9cBtfLbkZabdcTtBwRCXIOi2Al54+Um+++47oqKiOP/88xk6dCiWsBi/yUVEyC8qIbnvQLyhHVBAQtfu7C2rJSw51JgyL+B2u9mVtZuILt0p9hgT+xfOf59fXzmFc84ay72//x2WqnI6duxIlMNOkM3SZMt/y5YtpKamYm2FrXzKlCls37690fl7772XqVOn1juXl5dHt24HJzN27dqVvLw8EhIS/OfsdjuzZs1iyJAhhIWF0adPH1555ZV66Xz77bd06tSJPn36+M+lpaXx9NNP8+CDDx5S5kPickFNDcTEGMfh4dC7d5uMHTSHqRRMTkkaVuBHg/J6cTqd1JSVoTQNl+72KQIdqwWUx46mW6itrSX1tNPJzd3L4MFD6Z8ykb0Fdipr7JTXhHCgLNKXom8QVAkeT7Axxx4LYrH4TDYKq92C1aIIcoA9CGw2RXa2k+AQL4mJNfy0eTM9eyVx+pgBANxw41T+85//cN8DDyAiWK1W1q9fz7hx4/yt4auvvpodO3bgUVaq61ZsoXBpOuVenVogxG4FiyB2C6EOG5G+KZf7yorpEBNN51CH/74sXPABCxcuJCrUweTJk/nggw+YNm1as4O8hzv4G2jSORSNp6k2zs/j8TBr1iw2bNhAz549ueuuu3jqqad49NFH/XHS09Pr9RIAOnbsyL59RzlbXikoLobCQuO3wwG+WU4nk0IAUymYmNSjbkW00jSUrqM0F7rmxu2xoXmD8GhBuDw2lGbHq1nwahZ8U2NwOEJ4//31VFaWM+3Oy3hjztv85jd30bPXENav/5YghxWbzajg8/J2ER4RTnJSKIMHD2BfzvecMzYZiyVg4qaIfxETOr5pmFYcYWHYg0MQEYJ85gavsqApKHJreJSizKtT4dVxKyh0eQGo9GjUajrVSic06GCF36tHd2oK8/0V/oG8PPp27+4PB2O8JHC1+KZNm8jMzOTcc88FjJ5Ez549mTZtGrGxsZTW2cl9lJSUEBcXR3R0NBs3bkTXdb/5qDkOp6fQtWtX9u496CAhNze3kWmobrynV69eAFx11VU8/fTT/nCv18tHH33EunXr6l3ndDoJOYwpoY1wOmHfPuMbICrqpDATNYc5pmDyi6e0qpSSyhKKK4qpqi2nsqyMkpJqCgp08g6Esmd/HPsKO5BfFk5JZQjVtSE4vcF4VRBYbFjtVhzBxrz46BgLScnRPPXk35n79jN0iCrn9zdfyqaN37Ltp//SIaICu8pn5p+nc/dd03DYa/jD9Dt44Z/PkZmbix4chhYUwsuvzUYPDkUPCjM+jjB0ezAeBcUenbiefcjKzmb1lp8pcHp45713OHPcWEKDbH77/aSxY1i98lvstVXE2i18vmghoTYrnUMd9Sr8Sy65hLfffhulFD/88ANRUVH1zC4AMTExaJrmVwzp6enMnDmT7OxssrOz2bdvH3l5eezZs4cRI0awatUqDhw4AMDatWtxuVx069aNXr16kZaWxowZM/yt+8zMTBYtWtTof5k/fz4ZGRmNPg0VQmvL0KVLF7Zu3epf2PXll18yYMAAf/hXX31F//796dq1a73rduzYweDBg1v9PPnRdSgogN27DYVgtxvjBl26gO3kbY+fvJKZmBwGTq+TSndli3GUUnjdRqsZr4bSFW43uJ1WvJ5gPF4rXs1izIc3Jt1jLGeCIJsXR5CG3a5jtymsDuMdV97aeqtfI0MqUShGjRhIytAUPvvfR1x95TW8NXc+jzxyP/c+9CC6rnHV1dfyu7vuQYmF5GEjmPmPZ7n5phtx1dYiIpx3wa8Qe32zgrIKVosYLfpQB2/PmcMdU6/D6/UyYsQI/jBtGo6Ayj4hIYGZM2cyevRoEhISGD58OJrWeEXzBRdcwOLFi+nduzehoaG8+eabTd6/8847j5UrVzJx4kTmzZvHkiVL6oVffvnlzJs3j4ceeogXX3yRCy64AF3XCQ8PJz093d8zmD17Nvfdd58/v9jYWP7xj3+0+N8dipbKkJqaSkZGBomJicyYMYOxY8dit9vp0aMHc+bM8cebN29eI9MRwLJly45sILygAEpKjN8dOkB8/ElnKmoKacoWdzKTlpam1q41lzT8UlmZtxKv7m10PqositTBqf7WZ+AAsKYpnDUaNdXGylvdI7i9xqrbg8//QbuNza7jsGvYLG6sVg9Bdg9iAc3SVJdfEPtBO7uI+KxJUhds2PhtFiJDDl5f4dX8eYtIvZb7ycqGDRt47rnnmDt3bluLcsJwuVyMGzeOlStXYjvc1r3XC3v3GusO6sYPThDbtm2r1wsCEJF1Sqm0Q11r9hRMTnr8ikApgvLLGB2biq5reDUNr8eDy+2mUAmeomJ0HWrdVpwuKy6vFZfXhkcTn4exIBqOddptihCHEBKsERQGopyAwunVAMESFEpEsK3ebJ2mvut+NzWYWqcAyj3eenGj7O3r9Rs2bBgTJkxA07RWzR46FcjJyeHpp59unUKoqoKyMsM8JGKYiJKSaPTQneS0r6fS5JTGub0EpR3suW7L24BeUUUI0E2LwVNdg1ezUsg3KM2DQigsD+bnfbH0nJzA3pIwnB47uq4HmH8UYoUgu8Ju17DbdLDoWKwKq2hYLMrfSXB5jErd6gghKEiICLY3W9E3xKj49SbD2qMCaI6bbrqprUU4ofTp06fe9NQm0TTIzzcUAhiL0OqmnLYzhQCmUjBpAwIrf2U4nkevqEC5nNi6BKFpGhuyv0cVl9I9diDu0FBqD6ynJiKKotoOZO7txbeZPfkpQyivsIO4mXVRCTVuK4JOkEMjPNyCw6Fhtym8BE5ZFAQrmkXAEoTVbsViabrir9SaruSb4lSq+E0Og4oKY1Wy12sogPh4iI5ua6mOCvMpNjnuNFICmgdrSBUA7r25KBFczlrWefZQvd0YCK3MLsNVnsbHq7qwcwvkFvyaWt2Y5+/VDDOMzWahQ5yHfv29hIcL3boJwcEKESuaV0f3KpxehcVmITzYTnWdYhAhSCDacXx3sDI5hWnowC401HBR4XC0fF07wFQKJsechmYgsQrBfaPwlJZStX4DmubFIxb0jvFoUZF4oqLQdcW6ZTXU5Axl/RrYvScaix6wu5Y9BEeoTny8my5dXIweHUyvXqtJSgohNLQPxcWKkGAdTdOpcekIgssuWIKthARZcQI2sbSLAV2TdkBl5UEHdh07GuaidmgqagpTKZgcU5zbjSl4wf2iUV4vbrebmpwcDixcSnZpGXpoCCQkYLXZqC108c2mcnZshcwtnagqGU1kkAO3y0OIQ+jVs5SBpwtDhlTRt28N4eGasekJCt2r43KV4PWkUbSvHBcaVTUaIoLHbiXEYSPc0j5m9Zi0E3TdUAJgmIjcbkMZnMQL0Y4Ec/GayVHh3F5C7dZiarcWU7O5AHduHppnP2vnz+fbue+w4K0X+OS7/7JcP8CmqAiWliXz6oIEHvprb+64sxfz/zOcrd/1xltsIzHCyeiRRdxx+2beeG89//diBtdfv5vUFBcOu8JVCyX7veTvcnMgy8v7WV15N8vFR/kKzWrBFhGENdxOWIid6IAVuyeKY+k6+/7772+UvsvlYuLEiaSmprboAmL8+PE0NW17zpw53HnnnY3O//zzz4wePRqHw8EzzzzTbLpKKc4++2wq6kwmwMKFCxERfv75Z/+55cuXc9FFF9W79sYbb+TDDz8EDHcTDz/8MH369GHw4MGcfvrpjdY8HAlPPfUUvXv3pl+/fnz++edNxlm6dCnDhw8nNTWVMWPGsHPnTgD27NnDOeecQ0pKCuPHjyc3NxeAwsJCJk2aZKw32LnTUARg9Ao6dTrlFAKYPQWTw6Qp01DIwFi2//wzlTt+xllairsyBmt4OF1HjGb1j7txrY5h9cZYducE1dtnK8LmZUi/WoYN2M3o4fl0GhHDln25aMrNx6VdcB1IRKsKQsSCze5ALBYsCPZIO/YQOx0cdu4Zargz3rZtW5sP9B6O6+wrrriCf/3rX0ybNo3Nmzdz55138tlnn9G/f3+8Xi+vvvpqo/Q3bNiAx+Px53Gs6NChAy+99BIff/xxi/EWL17M0KFDiYyM9J9LT09nzJgxzJs3j5kzZ7Yqv8cee4z9+/ezefNmHA4H+fn5fPPNN0dTBLZu3cq8efPYsmUL+/btY+LEiezYsaPR1Nnbb7+dRYsWMWDAAP71r3/xxBNPMGfOHO6//36mTp3KDTfcwNdff80jjzzC3LlziY+MJCEiglWLF3Pm8OGGySgu7qhkPdkxlYLJIQlUBH4lsGkTrv378WoatVuceErLSAwOJnbkSL7N38M3/0tkzRNhOMsHEOQRdFsIIeE6w0+3M3Soh8GDPfTsqbPaqfNzvpMvdB2VU4hXdaVndDRJzmDOiAgjqn8YHXtEYbG0L3vt6NGj2bRpEwDvvfceZ555Jueddx4AoaGAXDb7AAAgAElEQVShvPzyy4wfP55p06bx97//nT/96U/0798fAJvNxh133FEvvYKCAn7zm99QWFhIamoqCxYsIDs7m/vvv9+/onnWrFk4Ggx0vvnmmzz11FMkJCTQt2/fRuFgOHzr2LEjn332WYtlevfdd7n11lv9x1VVVaxatYply5ZxySWXtEop1NTU8Nprr7F7926/LJ06deKqq6465LUtsWjRIq6++mocDgfJycn07t2bNWvWMHr06HrxRMTf0ykvL/f7R9q6dSvPP/88ABMmTOCyyy6DoiIoLOSyceN4d/FizrzkEghQiKcqplIwaZLMzEx0XcedZ7iOCOoS4Q/Tt+VTsXEjsVFR6PHxxNhsbI2uYlllBMtehg0rRxLiAd3lpkdcJamn65x3iYXS7tXUbdt7ANhX42bXgU0MUC5SuvQhIqIb3morWq0FiRASekXhCD3CGULbj94c0Yh+v2pVNE3TWLp0KTfffDNgmI5OO+20enF69epFVVUVFRUVbN68mfvuu6/FNDt27Mjs2bN55pln+PTTT3E6nYwfP56lS5fSt29fpk6dyqxZs7jnnnv81+zfv58ZM2awbt06oqKimDBhAsOGDTvMQh9k1apV/Oc///Eff/zxx0yaNIm+ffvSoUMH1q9fz/Dhw1tMY+fOnXTv3r1eb6M5/vCHP7Bs2bJG56+++moefvjheufy8vIYNWqU/7jOdXZDZs+ezQUXXEBISAiRkZH88MMPAAwdOpQFCxZw9913s3D+fCorKynesYPY6GjSRo/m0f/85xehEMBUCiYNqFMG2r5qkjv1QLrEE9yvA2BUdhUVFRRv3UqMxYpj2DCsVisLP/uMpZ8PJmtLXzwuDzZcDBu0l+6XQ/8x8QAUU41NwHKgHM2zE6UUDksFqd5QhidNwm63463U0Vwa8d0iCI85yql9razAjyW1tbWkpqaSnZ3Naaed5vcgqpQ6Zu6k69i+fTvJycn07dsXOGiuClQKq1evZvz48cTHG//BlClT2LFjxxHlB4an04iIg42D9PR0f35XX3016enpDB8+/JiVta7l3hpa4zq7Ls3FixczcuRI/vGPf3Dvvff6le2dd97JnDlzGHvGGXTp1AlbcDB0705Hh+PoXWe3I0ylYFIPXdfpQTwkGsrA6XRSWlpKdXU1tbW1UFGBvaAAa79+fPDxUj5fMoSsXRcRGhyLM0Sn3+g8zrvBTmxCPDZgZLAHj9eN11PDptxs7LZqhnePJjpqGHZ7EEV7bMR0PFjR2IOsBIW0z8eybkyhvLyciy66iFdeeYXp06czaNAgVqxYUS9uVlYW4eHhREREMGjQINatW8fQoUNbnVdrfZYdyw3sbTab3+V1cXExX3/9NZs3b0ZE0DRj5tff//73Fl1n9+7dm5ycHCorK+spmKY4nJ5Ca1xnFxYWsnHjRkaOHAkYSnLSpEkAJMbG8tGCBSBCVVUVCz7+mKihQ8FqxVlZeXSus9sZ5uwjE8AYN9iydD3e3GrjRI9Q8vLy2Lt3L8XFxei6jiMvj0UZS3niRw8XPxzOsy9fSGbOYOzR0fQbl83v/rSGx67bwqU9axlhzaG/+0fW7FxBRs4Wth/YQ6gKZUTieKT6TFxlUVQVhhAa4SAs6uCnvSqEQKKionjppZd45pln8Hg8XHfddaxcuZKvvvoKMHoU06dP9+/k9cADD/Dkk0/6W/G6rvPcc8+1mEf//v3Jzs72z56ZO3cu48aNqxdn5MiRLF++nOLiYjweDx988MFRlatfv35kZWUB8OGHHzJ16lT27NlDdnY2e/fuJTk5mZUrV9KnTx/27dvHtm3bAGNmz8aNG0lNTSU0NJSbb76Z6dOn4/bN5Nm/fz/vvPNOo/yef/75Jl1nN1QIYLjOnjdvHi6Xi927d5OZmcnpp59eL05MTAzl5eX++/zll18yoH9/OHCAorVr0cvLAWMW00033+z3aHrErrPbKe3/DTQ5YhoOINu6htGjRw9KS0up2luMzWYjLCwMpWkU5zp5cX4c3/4whRCtM4jQq5Pi8struPhiJ6VZu+jcbwjlFasoLtyCUh5iO/Qj2OZgUHQ8IhASHkREbDAWqxAcdmqvJh42bBhDhw5l3rx5XH/99SxatIi77rqLadOmoWka119/vX96aEpKCi+88ALXXHMNNTU1iMghXTUHBwfz5ptvcuWVV/oHmm+77bZ6cVrrOvvAgQOkpaVRUVGBxWLhhRdeYOvWrY3s/hdeeCHLly+nd+/epKenN6qcr7jiCt577z3OOuss3nnnHX7729/idDqx2+3Mnj2bqKgoAJ544gkeffRRBg4cSHBwMGFhYTz++OOHfY8DGTRoEFdddRUDBw7EZrPxyiuv+GceXXDBBcyePZvExERee+01rrjiCiwWCzGRkbwxcyaUlLD8xx955PrrEZuNsWPH1tum84hdZ7dTTNfZv0DqlIFYhb2WYjRNw+PxUF1dTWxsLDabDYfDwdq8VZTleJm/oCdb1nRB060E26F/32guuqiWc891EhxspJmzaR0SIni1fGIizyEiIgKr1crarBLGDO5ETOdQ5DjOIGrKVbDJsWX//v1MnTqVL7/8sq1FOTo0zXBR4esZEBwMiYn4H+YGjB07lkWLFhFT5+SuHWC6zjY5LHblZmPrGoau63xf5cERGopHKayRsex26ajSMtavyWbzt8ns2tgb3esl2iaMnuDgsstqGTy41L+i3+vW2bt5E9VV+4gKDmfg0HOIiU3y5xXqdtEhMaxtCmpyTElISOB3v/sdFRUVrZo9dFLidEJOTn0HdrGxzbqoKCws5N57721XCuFoMZXCKUzDhWZ16OgkJiZSVFSExSuMCbVjc2poHg8rFlXzyfLOZGTGERoUTLhVY9yZBVx5ayzJyS5/GrVVZRzY8RMVxdVYg4vp0qcfnbr2Y2NRKN78fH88m7V9rS8waZmjXU/Q5gQFGa4qWunALj4+3liz8AvCVAqnMEpThAyM9R/ruo7L5aKi1MWBAwdwOBw4HA5UrZclc4r5aGkcecXdELuNoMhqLrksiAsvdNKtWzRetwdnleFK+secA+il63GVlhLRtS/h8Ynsd/Rif1EoNhtMHNiprYpsYlIfpYxVyOHhxsCxxQI9ehgb4JwiDuyONaZSOMVxOp0UFhbidrv5vsaDVylKvUJnZcNVqrPpw2JmL4omt6IrWpCNmC6KsZN2kXZmESMTh+B16xzYdYBdhbvwKg0lYLc5GdizG0GeIfQbdWZbF9HEpGncbti/H6qrDcd1CQnGefupPcnhaDGVwilAc2Yir+6lIC+PNS4drFYcwQ76lBTgLdzBD4utvP2/eJwl8ShbEPE9NaZeJoxIrcIREgfEUVvuQSmwBbvwhIVw1sDBVO/PxSKCRYVgDTZfLpOTEKWgtBQKCgzPplbrCd8juT1jKoVTgIZmIjA8an6SmY1yWajZk8FwdwnbavL5YGMvln01horSCERXpPTz8qtLPaQMrCU00kZotAOroxKljI1sPB43P6xZh8MRiSu/FEdQFMmppzUlholJ2+NyGb2DmhrjODISOnc2zEUmrcJcvHaK8HVxBV8UlfN5YRmLcvNJ356FVFfRf+MShmZuw1nYh7mzLueLBRfiKe1ASjcXL/y9mr88UcMZY7x07h1GTJcQbMFOqqp+oqp6H1VVeRTu3kBoWASXXjyZ3mkjTYXQAu3Vdfa7775LSkoKKSkpnHHGGWzcuLHJdE9619lPPEHvPn3oN2ECn3//PXTrBl271lMIR+w6+xeEqRROAb5x1QJwVlgQA52VDKgpZ4JFo9ePi/FWOVlT8GtueSmFrMIudEgI4g8P1PLkPzV69bPgdkNwhA2r3WKsBt28hLK9pejFwdiro+kcfxqRfS4hKKhDG5fy5KfOzcXmzZvp0KGDfwFUnevshx9+mB07drBx40a+++47/vWvfwH4XWe/8847bNu2jc2bN9OzZ89G6Qe6zp4yZcoxkzs5OZlvvvmGTZs28dhjj9XzhBrIoVxnt5ZA19mbN2/mv//9L5WVlUdVhq1btzLvgw/Ysnw5/0tP544nn0RrwmR0++238+6775KRkcG1117LE088AeB3nb1p0yb+/Oc/88gjjwDG7KOEhARWrVp1VPK1J46rUhCRSSKyXUR2ikijteki0l1ElonIBhHZJCIXHE95TiUCN7fxCKQoN7m5uWiaRphS7MvKYmuFixlf/JoXFybg1WxcfpGNl18s5vTTnLgqNTSvIjw2CGXxkrVhKQU7vycqKpy0CVMo7jiInPBe7Arqak4rPQJGjx7t99LZnOvsp59+GuCwXGdnZGSQmprKrl27WLp0KcOGDWPIkCHcdNNNuFwuGvLmm2/St29fxo0b12zFdsYZZ/jn4Y8aNcrfSm7Iu+++y6WXXuo/rnOd/frrr7daKdS5zv7nP/959K6zdd0YN6itPeg6OzmZ5FGj/K6zG9KS6+xzzjkHMFxnL1q0yH/NZZddxrvvvnv48rVTjpuhTUSswCvAuUAu8KOIfKKU2hoQ7VHgfaXULBEZCCwGko6XTKcCgauRQwbGopTCmbWX0tJSQkNDyc3NZc/qDzlQHMV7n11HracribGKu++uZPRoN5u/Wk9QMASFWNFFQymdkJAQIjpU0nPwBBBhVVY1Io52PbV0+d7lxzzN8d3Gtypee3ad/frrr/OrXzXtYfakcp09fTrs22fMMKqqIi83l1GjR/unmR6V6+yFCw3X2cXFxMbGkpaWxqOPPnpIeU8Vjufoy+nATqVUFoCIzAMuBQKVggLqno4o4Jfjn/YIqNv/uG5QWSlFUVERmw5swhLmQmqEitIKKvMSeO/r31NR6SWpWyF/+P02oiM87FjpRtBITE3A6/UQERHB9lINl5YPqhOFOcbm4zarML5fx7Ys6lHT2gr8WNLeXWcvW7aM119/nZUrVzYZflK4ztY0o3eQnW0cOxzQuTNNOes5YtfZY8fSpUsXbL6xiI4dO5qus48RXYC9Ace5wMgGcWYCX4jIXUAYMLGphETkVuBWgO7dux9zQdsLgbOMlmUvo6SsBJfLhT0ojpGOXtQW1/LSe8LKFfG47B5GnFbKzKccOBwDASjKqYagMnR9L3mueMRTi9UqjOjZm5CQblit5rS9o6E9u87etGkTt9xyC0uWLCE2NrbJOG3uOlvXwePh6kmTePjWW41tMWNjwWI5etfZiYl89NFHgGEWW7Bggd+Bn9Pp/EW5zkYpdVw+wJXA7IDj64F/NohzL3Cf7/dojF6EpaV0TzvtNPVLpWZLkfJ4PCo/P1+9+8O7KisrS+Xl5an3Nv2s3n/qOzVhZKHq32O/GtavQD08/Se1d2+eysszPplbs9XKJRlq754d6uuML9Syn/PbujjHlK1bt7a1CCosLMz/e/369apbt27K7XarmpoalZycrL788kullFI1NTXqwgsvVC+99JJSSqmNGzeqXr16qe3btyullNI0TT377LON0l+2bJm68MILlVJK1dbWqm7duqnMzEyllFI33HCDeuGFF5RSSo0bN079+OOPat++fap79+6qqKhIud1uNWbMGDVt2rRG6e7Zs0f16tVLrVq1qsXyjRw50p/fv//9b3XrrbfWCx87dqxasWKFcjqdKikpyf+fZGdnq+7du6uysjKllFIPPPCAuvHGG5XL5VJKKbVv3z41d+7cFvNWXq9SP/+s1JYtSmVlKVVbWy948+bNKiUlRTmdTpWVlaWSk5OV1+utF8fj8ajY2Fj/fZ49e7aaPHmyUkqpwsJCpWmaUkqpP/7xj+qxxx7zX7d27Vp1/vnntyzfSUZT7wOwVrWi7j6ePYVcoFvAcVcam4duBiYBKKW+F5FgIA4oOI5ytRsaLkpzed3sz8lBKUVYZS2WvA24dPjvZwPYsjyJWt1Dt4RibrulhORkJxaLMWNI1xXlRdXEJUaRWV4AQrs3D53stCfX2Y8//jjFxcX+wW2bzdbklNYT7jq7rjckYixA69zZcGTXoUMjFxVH5Do7JoY33ngDMKbRPvLII4iI6TpbHSfX2SJiA3YA5wB5wI/AtUqpLQFxlgDzlVJzRGQAsBTooloQ6pfkOrt2a7F/MLm0tJSioiI26Ba2V2VhK6mga4XG/PlpbFoXSRhuLrk4h+kP9MDSYE5Z8b4qaisq6NK3jG0F5Zw1aAwOR/sdRG4K03X28eeEus72eAz31qGhhomoDTFdZx8jlFJeEbkT+BywAm8opbaIyOMY3ZhPgPuA10TkDxiDzje2pBB+STi3lyBWQSnFwqwcKqpryHHtxVpdxei81cQXdeMvH55HVlEnOoRW8/dngxgypIf/+upSN7UVXpzVbryal56DQthWUoY1dNQppxBMTgwnxHW2UsY+B/n5xqByba3ht6hhS+cEYbrOPsYopRZjTDMNPPfngN9bAdOjWgPqZhkF9+tAeXk5zpx1XOyoYc2eDIaWhfFj/nCmfTqJkkoLMdFl/PHuLIYM6Q2Ax6VRW+GlqsSNPUwnuquDbj2S+GHXFrBEMqF/l7Ysmkk757i6zg50YAeGZ9OEhDZTCGC6zjY5SaibZeR0Ovliw1KC3R6qVSJSvYc391zP+0s643a76dd7H/f8fi9904xuoq4pKgpcaJqGPVwjoUccm4t1cn7+EqtFZ1TfQ/YcTUxOPEpBSQkUFh50YNe5s+G3yHRvfcIxlcJJiqZp7N+/n+3uAoZXOFntLeetxb9m767OOKsr+dW5mVx7eTmJAwajawpnlZd1O8uprXJhjbXTIb4DhUUKm9VCSrcIYmMnHPGceBOT405lpaEQoqKgUyfTgV0bYt75kxCFIj8/n++q3XiKK3Hu6corC8eSVxRMWGgJd9yyiSuuG0TZ/ngKsqr5uaAGr1cHh864tE50S070L7wBKCpqw8KYmDSFrhufus1uEhIM89Eh1i6YHH9MpXCS4dxeQmVVJasKNhBms2LfIPzts/OolVCSe1Xzt79BTOQA8ndVY7EKWcqLtZOVEQlhxMfHExUV5e8R6LoLlysf1eR6TxOTNqK21nBRYbcbnkxFjJXJh9ga0+TEYHpJPYnIXrKG3Zs3sf1ABkUFpTh/iuCdhVdR67UzdpyHP/5xF3GxXopzathZ6WKHeHArD6OToujevTvR0dF+hVBRsYnikpVUVWcSGpJsmo5OAO3VdfaiRYtISUkhNTWVtLS0Zt1c1NbWMm7cuHprHZ5//nmCg4MpLy9vMZ/x48ezds0ayM+nassWfv/II/QaM4ZBgwczduxYVq9e3Wx5WoNSiunTp9O7d29SUlJYv359k/HS09MZMmQIKSkpTJo0iSJfNzojI4NRo0b570GdM71PP/2UGTNmHJVs7Q1TKbQxzu0lZH+2mt3//R6Px0Nk3B6Kgt2QF8TLswahe4NIO62Aa67dgs2iU5BVw84aN7ZYG8M72Tl3cCJdu3b1e5z0eMqorNyKy11IdNRpxMedQ1hYYzfMJsee9uo6+5xzzmHjxo1kZGTwxhtvcMsttzQZ74033mDy5Mn+RWFgVLIjRoxg4cKFLWeiaZCXB8XF3PLnP9Ohc2cys7LYsmULc+bM8VfOR8qSJUvIzMwkMzOTV199ldtvv71RHK/Xy913382yZcvYtGkTKSkpvPzyywA8+OCDzJgxg4yMDB5//HEefPBBwFiw98knn1BTt2nPLwBTKbQRda6vATwdFRGj+8CAjqzWwyguTWTB+7+ikhj6jK7iuhv2ExPciUhbPJkVTiyRFobEWYmNjSUuLg6LxYKue9C0GlyuAnTlITIiBbs9uo1L+culPbnODg8P9/ckq6urm+1VNnSdvWvXLqqqqnjiiSdIT09v+kYoZSxCc7nA42HXgQOs3rqVJ55/Hotv3Ktnz55HvWJ40aJFTJ06FRFh1KhRlJWVsX///gaiGG4cqqurUUpRUVHh94/UnEttEWH8+PF8+umnRyVfe8IcU2gjAqedFn5diMTEEZ2/hpLCMBa+1JvCqgj69vcy+e7NaAXRWGzC9hIX1igrQzvZiY+P9y+o8XhKKStfjyBYLMGEhibjcMS3cQnblsqvG7tcPloizp7Qqnjt0XX2woULeeSRRygoKOCzzz5rFO52u8nKyiIpKcl/Lj09nWuuuYazzjqL7du3U1BQQMeODdyniBxcZxATw5aKClKHDavX22iOKVOmsH379kbn7733XqZOnVrvXF5eHt26HfSqU+c6OyEhwX/Obrcza9YshgwZQlhYGH369PH35l544QXOP/987r//fnRd57vvvvNfl5aWxrfffnt812icRJhK4QRSz5eRRSgtLWXLdyuw2+3EFK9j0Q7h1dmjcZaEkdxP55xrPsFV4Eb3RuMItaI5YHhnOx07diQ62ugFaJqLqqodOILiiYgYhLGNhUlrK/BjSXt2nX355Zdz+eWXs2LFCh577DG++uqreuFFRUX+Z66OefPmsXDhQiwWC5MnT+aDDz5g2rRpiK4bPorqiIuD4GCkQwdjkLmVtDRu0pCmHCE0vLcej4dZs2axYcMGevbsyV133cVTTz3Fo48+yqxZs3j++ee54ooreP/997n55pv99+CX5jq7VeYjEQkSkd7HW5hTnbregb1vFKWRTqo3LiKsZBv7Q918lBXEv2efg7MikujkMn59/1eUeCoJsg6j2BLD93nl6JqHhISEei+nrteilEZYWD9TIbQxdWMKe/bswe12+1uhgwYNajTw25Tr7MOhtd5gDlfpjB07ll27djWy8YeEhOB0Ov3HmzZtIjMzk3PPPZekpCTmzZtnmJDKy4l1uynNyzPGEQAsFkrKyoiLi2PQoEFs3LgRXdcPKcuUKVNITU1t9Hn77bcbxW2N6+yMjAzA6KWJCFdddZW/R/DWW28xefJkAK688sp6u7b90lxnH1IpiMiFwE/Al77jVBE5xKiSSSB14wdiFbxeL8U/zOfA0rcozC1AixlAYX40i/6ZhrMklOhuRfz+jxs5KzGVeNWDgRHBDO0awshuoVw+qp/fB72ue/F4yqmq2oHFEoTVak7nO1mIioripZde4plnnsHj8XDdddexcuVKf8uztraW6dOn+wczH3jgAZ588kl/K17XdZ577rkW8+jfvz/Z2dn+jefnzp3LuHHj6sUZOXIky5cvp7i4GI/HwwcffNBkWjt37vQrmfXr1+N2uxvtqRATE4OmaX7FkJ6ezsyZM8nOziY7O5t9e/aQl5PDnh9/ZMTAgazasIEDvtb12rVrcblcdOvWjV69epGWlsaMGTP8eWZmZtbb/rKO+fPnk5GR0ejT0HQEcMkll/D222+jlOKHH34gKiqqnukIoEuXLmzdupXCwkIAvvzyS7/TuMTERL755hsAvv76a/r06eO/bseOHQwePLjJe3cq0hrz0eMYm+MsA1BKZZi9hsOjroeglGLd119CbjZ0HcGu0Fhq80uZN7s3e0pD6di9nN//aROnhQ/k+w3F4NTRxYsjIqjeDCMApzOXWmcugoWIiJS2K5xJk7Qn19kLFizg7bffxm63ExISwvz585vsYZx33nmsXLmSiRMnMm/ePJYsWWIMJJeVQX4+l0+YwLwlS3jo0Ud58eWXueDSS9F1nfDwcNLT07H4xhZmz57NfffdR+/evQkNDSU2NpZ//OMfR3qrAcM99uLFi/1pvvnmm/6w1NRUMjIySExMZMaMGYwdOxa73U6PHj2YM2cOAK+99hp33303Xq+X4OBgXn31Vf/1y5Yt46mnnjoq+doTh3SdLSI/KKVGicgGpdQw37lNSqk2qYnao+vsOhfYRUVFZH32Gr369+TriOF4d+9h1fsD+DYjHmtECW++AlHhHkpya9nucpPoKCY5OZmEhATsdnu9NGtqdqOUTlhYrzYq1cmF6Tr7+LNhwwaee+455s6de/Dkvn2GUgBjNXLnzsaitFOE/Px8rr32WpYuXdrWohwWR+M6uzVjCttE5CrAIiLJIvIC8MORifrLo84FdmVlJc7Nn2K32XF1Owv3vv2s/MDC/34MwWOr4HcPbSY+XmdtTiU/Zu+msjyXoKAgunTp0kgheL2VaFrrB+xMTI4Fw4YNY8KECfV7G1FRhquKLl2ga9dTSiEA5OTk8Oyzz7a1GCeU1piP7gT+DOjARxj7IzxyPIU6VahzgW3rFUludjbRdive+EH8WPQjK1ZYWbr6DCJDwvnr4+UM6NOXxV9uRtc1ukbaGHHWUP8ahIaUla3Fbo8iKOiXPe3U5MRz07XXGj2DujGHsDDo3btN3VsfT0aMGNHWIpxwWqMUzldKPQQ8VHdCRCZjKAiTFqgbS9i4Yhnu3aupstv4JEzhXR/ByoVjCLOH89vfVjNsmIcdW3LQRGfM8GQ6JXYkNi6mnl1X02qprNzi82OkiIwcZrquMDlx6DoUFxveFZWCkBBjVzQ4ZRXCL5XWKIVHaawA/tTEORPqr0Wom21UWVFB/95dcCefgy1jFVvfSsOqhzLyTDeTJ9eilMLt9hIUEUfPvj2anP6m6y6U8hIePhARq6kQTE4cdQ7s6lZMx8SYzutOYZpVCiJyPjAJ6CIigfPjIjFMSSZNUNc7qGPTt8sRiwWxWNDz81nzXnf2HoihU1fhvvsqyc3di7vWy6591SQO6NKkQqiu3onLlY/FGozdfpy2QTQxaYiuQ0GBsQEOQFCQ4eI6LKxt5fp/9s48PKrybNz3OzOZmayQhIBsgZCEQHaQRSyboIi4sRWhClKKVkWluHxSt1J/qVqXYrFoFbB8qCS0VIxW1E9ZqmyyBkwQiEASSFgC2ZfJbO/vj5MMWSZhQvbMua9rLjhn3jnnmUDmmXe7H5UWpaGewkUgFTABadXOFwNLWzKozoLNZqPi1B4Ke1awq1TDN996cfiHcQT5ePDss4WkXS4k/0Ixen03fP09ufPGUAoKD2C3m2tcx24z4eMTgV7ftgXMVdyMqoQgBAQEQFCQOlTkBtT7L4DKVekAACAASURBVCylPCSlXANESCnXVHv8U0qplm1xQtVKI4DTKQdI3bGddPM5rAFRDCi+kf9++gsCPX2YO7eMiAgrVrtkcA8vQnUabhkZgkajwWopws83Gj/fWMeja9cRGAw90WjULnt7pqOqs6vYt28fWq2WjRs3Kie6dVN6Bf37Q48elFdUNE2dXRlTSUkJv/3tbwkNDSUqKqpdqLOr757u378/8fHxAPz444/Mnz+/SbF1NFxJ+72FEElCiCNCiBNVjxaPrAMibRJjRAAA5ooKgkQ23v0DGSL78f6GYIpNvgwabOWXvyznm/3HKbuUg9VqxdvLm8DrjOQX7ENiR6v1QafzrvFQ5xDaPx1VnQ1Kr/aZp57i1rFjlYlkUJaa9uunTCrTRHV2NRYuXEhAQADp6entRp1dfff0jBkzHMqLmJgYzp49S1ZWVpPi60i4khTWAv8ABHAb8E8gqQVj6vBIKSkoKEAjbdi6R7Njry/bDvfEwyh46qlitFqw2mzcFBeOv3d3fHx8qKg4j5RW/LuOUBNAJ6AjqbOxWnn7//0/ZowZQ/cuXaC01Gmza1Jn1+LkyZP88MMPJCQkOJZbtwd1dvU2//znP5kzZ47j3J133klSkvt85Lmy+shLSvm1EOINKeVJ4HkhxPctHVhHJj8/n5/yjnKxWwnm8mG8s05xsEyeV0jv3le63gWXSjFovPHv4YPQlGHQd0enU2vUNgenjzT/CGdIbDeX2nUYdbaUUFRE9uHDbPriC7auXcu+hASnE8nXrM6uRVpaGvHx8e1OnV3F999/T48ePWq4j4YNG8arr77qcFV1dlxJChVC+ep6UgjxEJANNPwv78aUl5fz0+4d6MtzuNFvJO9tGU1OkY6+oaX84rYywMCejCJkhQ1rhaR3SFf8ukNBYSZenv3aOvxOg6sf4M1Jh1JnWyxw7hyUlPC7P/2JPz/7LNrwcGWpqZOYGqXObqb32prq7CqqEl113E2d7UpSWAL4AI8DfwK6AAtaMqiOipSSC+fPc7r0FAHB/mR7juOTT7yQWHh+dg6RPt35Zv9xLEVmgnSCwG4B+HT1RMoytBovPD37t/VbUGkCVXMKhYWF3HHHHaxcuZLHH3+cqKgovvvuuxptnamz4+LiXL5Xk9XZJSXKQ6tl/7FjzF6yBJYs4dKlS2zevBmdTsfUqVNrvLf61Nmg9CQGDBjAokWLCAwMJD8/v8bt8vLy6NatG127dnWos53t1q9OY3oKjVVnA8yaNcsxhAfKnMMnn3xSR2OuqrNrIaX8QUpZLKXMklLOlVLeBWS2QmwdDpPJhOHM92iLMhjpO5B167zIN9mJicsjIrQcu11iyqsgtmcwg2IHEDywO0ZvD8zmywihUecSOgkdQp3dtauyxHTAAE5nZjoU2DNnzuSdd96pkRDABXV2Tg7Z2dlkZmYyfPhwdu7cyfnz54GOoc4G+Pbbbxk0aBB9+vSp8Tp3U2c3mBSEEMOFEFOFEN0qj6OEEOtQhXh1KD+Wx57D37D9+H5kYBg/Wcbz7bdGNBo7S8YeY2c6bNtxEWGVeAcJevXt4fimVF6eidGzz1XuoNKRqK7O9vT0JDk5mYSEBCIiIoiJiWH48OFO1dmDBw8mOjq6ziRpbaqrs2NiYtBoNA2qs28eN46hISFXCt8IoSSFRgjsqtTZoAwdTZs2rcbz06ZNIykpiR49evDXv/6VKVOmEB8fz+9+97s66uzz588TFhZGTEwMDzzwQJ1v9Y1lypQpDBgwgLCwMB544AHHyi7Asby0ujo7NjaWlJQUnn32WUe7pKSkOkNHoKizmzoR3pGoV50thHgFmAEcBkKATcBi4M/Au1LKstYKsjrtUZ1tOp6H2Wxh44//y90hvTEF38TCpd4cP2TkxpEZPHfXWQ5pgxg3JJBTp07Rr18/+vbti8mUQ2npcRAaAgPGqT2FJqCqs+vBZFIUFVVDPz16XJHZNRKn6uxOTkXl3owdO3ag03Wc6sVNUWc39C7vBuKklOVCiAAgp/K47iCfG2M6nkfa5TSOXUjBw0PP9zYf9m8RnEgx0sPTytNjjmPtMQgumzhz5gx2u52goCCEEEhpxtMzGC+vUDUhqDQvdrsir7t8WVll5OGhKCp8fK75ktXV2a6sHuoMZGVl8eqrr3aohNBUGnqnJillOYCUMk8IcUxNCHWRNklW8Qn66/uR211gMmnZ9Y+ueJtM3HfLUXSBXbFYBBpPLRUVFURERNSZtFITgkqzYjJBdvYVgV2VoqIZPsgXLHCvNSbh4eE1lqe6Aw0lhQFCiCoTqgD6VztGSjn9ahcXQkwG/gpogdVSyledtJkFLAMkcFhK+SvXw297Lpz+GbOtgh6RkZw7n0ba9rHkZpXT/7pSht/sjcnTwOHzWeAhEQF6unVr/aWSKm6GEGA2KwK7Xr2uKK5VVFygoaQwo9bx3xpzYSGEFlgJ3AKcBfYJIT6TUh6t1iYcpWDPL6SU+UKIDrX/IePLvUgkHvoLdL2wmzMZffn8MyNClPKbRyz0GOzP+Ys54CEYExVMUFCQoxtqt1swmy/j4dH1KndRUXGB8nIwGpWEYDBAcLCip1AFdiqNpN6kIKVsalHSEcDPUspTAEKIJJR5iqPV2jwArJRS5lfe82IT79mqSKuN7sEXkTkSU7+J/OuPBuylZdw9LocBAwx4GIzY7XaMXYMwGAz4+V3RXttsZdjtFRgMPRu4g4rKVbDZ4MIFpRpa795KeUxQ9dYq10xLzp70Bs5UOz4LjKzVZiCAEGInyhDTMinlV7UvJIR4EHgQIDg4uEWCbQynUw4gs80IjWBrzgE0PYfw3obLnD8VzICeMH2hN+cvnaEUD47nluPhY6R79+41NutIaUWjMaDTqb+8KtdIURGcPw9Wq9JDqF47WUXlGmnJvqWz2dPa6191QDgwHpgDrBZC1BlPkVK+L6UcJqUcVrVlvy2R2WZ6hUXg/4tBWOw2LF7xfLfZHx+jnhm/siErLBh9NfTr1w+tTwC3RPfGaDTWuEZR8Y9otepYb2ei1dTZcXFseOcdOHtWSQheXjBggDKhTOPV2du3b6dLly4OdfRLL73k9P1JKZkwYQJFRUWOc5s2bUIIwbFjx2pc74477qjx2vnz5zuU3BaLhaVLlxIeHk50dDQjRozgyy+/dHrPxvDKK68QFhZGREQEX3/9tdM2W7ZsYejQocTHxzN69GjH5r+srCyHGyo2NpbNmzcDqjq7QYQQjZX5nwX6Vjvug7KstXabZCmlRUp5GjiOkiTaNcIOHmFduHz5MjoPHaeO6cnP9MbHVxAbcgkE6PQabDYbQggCq60Ll9JOWdlppLTh4zOoDd+FSnPTKursigpS/vlP7hk/XpkvuO46RW/dxPKYY8aMcewYfvHFF5222bx5M3FxcTWGQRMTExk9enSjLKIvvPAC586dIzU1ldTUVD7//HOKi4ubFP/Ro0dJSkoiLS2Nr776ikceeaRG3YcqHn74YT7++GNSUlL41a9+RUJCAgAJCQnMmjWLQ4cOkZSU5LDUqupsJwghRgghfgTSK4/jhBBvu3DtfUC4ECJECKEHZgOf1WrzKXBT5XW7oQwnnWpE/G1GUVGRQ3S2I9kHKeGmXxTSNVBLhT4PrVbLd+mXCPTv6ljTbbdXkJe/i9KyU3h7DUAIdRKws9Ji6uwjR4ifOpWTly+zJSODIRMnEhMb2zR1tovUVmeXlJSwc+dO1qxZ43JSKCsrY9WqVbz99tsYKhNZjx49mDVrVpNiS05OZvbs2RgMBkJCQggLC2Pv3r112gkhHD2dwsJCx07q+s6Dqs52xgrgDpQPcKSUh4UQN13tRVJKqxDiUeBrlPmCD6SUaUKIl4D9UsrPKp+bJIQ4CtiAp6WUl6/xvbQadrudvLw8jEYjuRe78NNeI37aUibdVIxfdyNFOZIMkzdGo47JcVfmQGw2EwINAf6/QKs1NnAHlaZy8kDTKnk5I/T62lNizmlWdbaUUFBA98DAK+rsTz/FZLEwfuDApqmzq7F7927i4uLo1asXb7zxBlFRUXXa7Ny5k/fee89x/OmnnzJ58mQGDhxIQEAABw8eZOjQoQ3+bH7++WeCg4Nr9DbqY8mSJWzbtq3O+dmzZ7N0ac2KwNnZ2dxwww2O4yp1dm1Wr17NlClT8PT0xM/Pjz17FGPPsmXLmDRpEm+//TalpaUOTxWo6mxnaKSUmbU2WLk0oyWl3AxsrnXuxWp/l8ATlY8Og8ViQSclB/IO8N3WOKSEcfHn8ff3YF92CXkXSvHsYuTukeF1NqZpNHo1IbQCrn6ANyfNrs6uqFD01mVlypLTKnQ6jqelNU2dXY2hQ4eSmZmJj48PmzdvZurUqaSnp9dpl5eXh6/vlXofiYmJjvvNnj2bxMREhg4d2mzq7OXLl7vc1hV1dtU1N2/ezMiRI3n99dd54oknWL16NYmJicyfP58nn3yS3bt3M3fuXFJTU9FoNG6nznZl/OKMEGIEIIUQWiHE7wC3LsdpMpk4UnyE4kIPju6NQdol42Nz0Bk1WO2S8AAPJkb2rDO5rNK5qZpTyMzMxGw2O+YUoqKi6kz8OlNnO5BSUVScOqUkBJ2ujp6iyersavj5+eFTef0pU6ZgsViclsfU6XTY7XYALl++zNatW1m4cCH9+/fn9ddfZ8OGDUgpG1Rnh4WFkZWV5dIcwpIlSxyT39Uf1XXXVbiizs7NzeXw4cOMHKl8YbjnnnvYtWsXAGvWrHEMYY0aNQqTyeT4Gajq7Lo8jPJNPhi4ANxQec4tMZvNZJdmo9Vqyfr+RirMGgZElTKwfwnHyirQapThpcBa0rHy8ixKS+t++1LpfDRJnW0yYT95kr+89pqSHLp2hdBQqDXc0mR1djXOnz/vSDJ79+51+v8XICIiglOnlCm/jRs3Mm/ePDIrtdtnzpwhJCSEHTt2EB4eTk5ODj/99BMAmZmZHD58mPj4eLy8vPjNb37D448/jtlsBpRhro8++qjO/ZYvX+5UnV176AgUdXZSUhIVFRWcPn2a9PR0RowYUaONv78/hYWFjt5SdXV2cHAwW7YoW7N++uknTCaTo4flbupsV4aPrFLK2S0eSQehpKQEu7Qz2DiCVz/3pFiWM/PuArw87RTknmOAvxZfPz88aimJzebL6PWB6PUdatO2yjVSXZ09d+5ckpOTeeyxx1i0aBE2m425c+fWVWfPnk1ZYSECuP2mm5RdyfUI7Kqrs61WK8OHD29Qnd2zZ0+GDh3qdEXOxo0beffdd9HpdHh6epKUlOS0h3H77bezfft2wsLCSExMrPPhPGPGDNavX8+YMWP46KOP+PWvf43JZMLDw4PVq1fTpXJjXUJCAs8//zyRkZEYjUa8vb3rXQbrKlFRUcyaNYvIyEh0Oh0rV650LPCYMmUKq1evplevXqxatYoZM2ag0Wjw9/fngw8+AODNN9/kgQceYPny5QghWLt2reNnoKqzazcQ4iTKUtENwCdSyqatHWsibanOllKSkZHB0W+2cax4OuvWeRHYJ4d3XpZk7drJ2R7dGRcXSr9+/dDpdNhs5dhsylhwaenPeHuHotdfm7ZYpWE6jTo7J0dZatpMArvm5Ny5c8ybN49vvvmmrUNpNdxRne1K5bVQIAG4HvhRCPGpEMItew4mk4m8HT9RYdOx8Z8GrKWlDLrhDIculJJfUUpogJ6AgAB0Oh1m8yXy8ndRXJxGWflpNBodWq37jEuquIDNpuxILqtWmqRnT2XvQTtLCKD0PB544IEam9c6O6o6ux6klLuAXUKIZcBbwMeA+yzcraS4uJjzZefZfSaQyxdL6RpSQo8x/oz2LCXDz4OevXvj5+eHlHYqKnIxGq7Dx2ewuh9BpS4lJcrKIotFSQohIYqqop1r1Ju6n6CjoaqznSCE8EER2c0GBgPJwI0tHFe749TBfeRkZ2NFy8GtwynReTHnfhO/6QaZB45h79aNgMBAtFotFksRZvNFfHwi1YSgUpOq3kFhoXJsNCp663aeDFTcB1d6CqnA58BrUsrvWziedktJcTE9BkWx6/vjnMj1JKB3BfePsiMLy7F7e9M7MtKxIcdmK0Gr9cZgaHtPk0o7orbALihIKY2pJgSVdoQrSWGAlNLe4pG0U06nHMBcUQFp6fhdymPXoQiswsBD00vQay3Yzp3DhrLcrWrcsaTkOF5eIW0buEr7wmZThotsNkVg17Nnk31FKiotQb1JQQjxppTySeDfQog6S5RcqbzWGbBZrfgFh3DixH4OBHiSltMHjU5yy202KCxEarTIgADHcrsqPD3bXvGt0sZUrewTQpk4vu46JSn4+6u9A5V2S0MD3hsq//wbSgW12o9Oz47sHRzMOcChvbsQHlqKjtyI3WIkOLIEX1+JLSMTa9eu+Pj6utXqBBXn1FBn3347BT/+CJcVlVdaWhoTpk1j4A03ED5wYNPU2fHxbNiwoc7zVTRWnQ2K7jo+Pp6oqKg6m+Cq6Mzq7MzMTCZOnEhsbCzjx4/n7NmzgLILevLkyU2OrSNRb1KQUlYpBgdLKbdUf6BMOHdadmTvYPuZ7djtdnprexPtH0VkcFc2fKZF2iTjpyg7MaW0c9ZiruGDUXFfPD09STl0iNTvviPAw4OVa9ZAfj7lpaXNp862WEhJSeGee+5ptrgLCgp45JFH+Oyzz0hLS6t353NnVmc/9dRTzJs3jyNHjvDiiy/y+9//HoCgoCB69uzZZMNsR8KVpTELnJz7TXMH0p6w2q2M7zueOL847HY7Wo2Wbw56cCFLR1BXSeQNFmRxMVaLBYNez6BBg5DShsmUQ3l5NnVrCam4DZmZcP48o+LiyM7Ph5AQ1iclNZ86OyWF+Ph4Tp48yZYtWxgyZAgxMTFNUmevX7+e6dOnO6oadu/ufNd9Z1ZnHz16lIkTJwJw0003kZyc7HjN1KlT+fjjj5sUX0eioTmFe1CWoYYIIT6p9pQvUOD8VR2fHdk70GmUH0thYSE+RZ4QKNixvy9GnYYJE8vR6QW2M2exd+mCj58fFksBFeZcKkzn0Bu6Y/Tsi/PCcyqtRfnR5jewe0bWsxtdSmWYyG6HsjJsQrDlyBF+89vfgk7XNHV2Jd27d7+izv7PfzCZTIwfP75Z1NknTpzAYrEwfvx4iouLWbx4MfPmzavTrjOrs+Pi4vj3v//N4sWL2bRpE8XFxVy+fJnAwECGDRvG888/f9V4OwsNDYTvBS6jVEyrPodQDBxqyaDaih3ZOwAY3Xs0ZrOZiooKtEKDTh7kh5RfADDhpnIuAVarlYtaK34ylYLCi3jouuDtHYbR2KuBO6i0FvV+gLcUJSWUV1QQ/8tfkpGd3Tzq7AY4fvx4s6mzrVYrBw4cYMuWLZSXlzNq1ChuuOEGx7Wr6Mzq7DfeeINHH32UtWvXMnbsWHr37u2YJ3Q3dXa9SaGyPOZp4Nv62nQ2qoaNAEpKSikvtGEut7F9dyB5pf6E9bVw8boSdFnnyMrMokvsAPr27UGXLkPQaDwavrhK58JuVx46nbKSqGdPZU4hLY3CwkLuuOMOVq5cyeOPP05UVBTfffddjZc7U2fHxcW5fPvmVGf36dOHbt264e3tjbe3N2PHjuXw4cN1kkKVOluj0TjU2ampqQghHKVnX3vtNZfV2Vebi2tMT+Fa1dlVk8i9evXik0+UAZGSkhL+/e9/O1YUqursSoQQ/638M18IkVftkS+EyGu9ENuG85mX4bQZUZLD/+1SSk3fFJeNLfNnemedRBscTHC4EY3GQ00I7kZpqVLrIDv7yrLTansOmqTORlGv/+Uvf2kwhOZUZ9999918//33WK1WysrK+OGHH5zKBTuzOvvSpUuOWhGvvPIKCxZcmUp1N3V2QxPNVSU3uwFB1R5Vx52WwsulnD6QgqUkhyJjOQdP90frIeg+Ng9jcB8sISFEjxyBlGZ8fWPaOlyV1qJqA1pmJpjNys5kJytcoKY629PTk+TkZBISEoiIiCAmJobhw4fXVWfPmcPgwYOJjo7m3LlzDYZSXZ0dExODRqNpUJ1988031zveP3jwYCZPnkxsbCwjRoxg4cKFTj8Eq9TZoAwdTZs2rcbzVepsg8HgUGfHx8czc+bMOursoKAgIiMjiY6OZurUqY4hrmulujp78uTJddTZOTk56HQ6hzo7Li6ODz/8kNdffx1QltFGREQwcOBALly4wHPPPee4tqrOrt1AiP5AjpTSLIQYDcQCH0kp20SV2FLq7OrzCT8fyebUkd1E9Y/jky8vsfqzIfj3zeKh3/7M6IgBpJ87x+jRoyks3EFg4FiEaH9GS3ejxdXZ1QV2QkC3boqiQuM+bit3VGcDjB07luTkZPz9/ds6FJdpUXU28ClKKc5QYB3KHoX11xJoe8Zqt3LjdTeSf76UwvwiPH080F5M46PvQyi2SwaPvMTY4fFYjUYMBkOdIjoqnRQplRoHWVlKQvD0VIymQUFulRDAPdXZubm5PPHEEx0qITQVV7bh2qWUFiHEdOAtKeUKIUSnXH1kqbCRf7EEnRfoyz1Yd7QLl/L96e9ZwOT+l4D+ZGZmutV/ELdHCPDwUP7s3h0CAtxaUeFu6uygoCCmTp3a1mG0Kq581bEKIX4JzAX+U3mu035NttktePt7YMg7yp491+GjEcRGnMYWGkpWVhZCCKKionBjR2Dnp6rGQRWBgUqdZNVoquIGuLqj+SYUdfYpIUQIkNiyYbUdJlMFl06eoKTcgwNHrqO0rIKR0WcZ2c+PiooKIiIi0Gq1FBenIjQeqJvUOhFSQkGBsrLo7FllIhmUYSK9vm1jU1FpJVwpx5kKPA7sF0IMAs5IKf/U4pG1AcV5Js6lH0Gr1XIiJw67ycovQi8wemwvhFaL3W6vrKwmkdKKr88gtYhOZ8FsVuYNcnKUFUVutC5dRaU6rlReGwN8CGSjfC2+TggxV0rZ6QxRhZfK8PCU9I6M4YUVZQhg4kxvtCH9sdlsaLVa9HoN+fm7sdnL8fZ2rzJ9nRIpIS8PcnOVzWhVims/P3WoSMUtceVr7nJgipTyF1LKG4Hbgb+2bFity47sHWjRYrVa0XoIzMd289Mxf3ykZOQYpc3p06fx8fHBbjcBkgD/Ueh0qh21w5OTAxcuKAmhSxdl7qBLl2tKCDXU2XfeSUHBFUVYWloaEyZMYODAgYSHh7crdfbrr79OfHy8I3atVkteXt39qZ1ZnZ2VleVwQ8XGxrJ582YAfvzxR+bPn9/k2DoSriQFvZTyaNWBlPInoFMNsFrtVgba4jh/6jBaDx17DgVgwcj1YcX4+iq/uDabjcjISACERo9W69WWIas0F127KquL+vaF3r0VbcU14unpSUpKCqmpqQQEBLBypaIMKy8vb9fq7KefftqxW/iVV15h3LhxBAQE1GnXmdXZCQkJzJo1i0OHDpGUlOSw1MbExHD27FmysrKaFF9HwpWkcFAI8Z4QYnTl4106oRBPSonWYKNPVCw7D/UAJDcMznc8J4RAr042dnzKy+HSpSvH3t5K76CZa2KMGjXKYelcv359u1ZnVycxMZE5c+Y4fa4zq7PrOw9w5513NirpdXRc+Vr0EMpE8/+gzCl8B7zdkkG1CVJisVjRZf7Avh9vQmu2MWqIsizRYrFgMBjQaDRYrJY2DlTFFY4fP17zhN0ORUVQ9Y20e/dG10iOiIhwqZ3NZmPLli385jdK2ZH2rs6uoqysjK+++oq//e1vTp/vzOrsZcuWMWnSJN5++21KS0sdniqAYcOG8eqrrzpcVZ2dBpOCECIGCAU2SSlfa52QWh9rmaTMVAFIDqb6U04XelyXS49JvZXnrVY8PT2x2ysoLvpR1WN3AGp8gJeWKooKX19lAjkgoEV2JJeXlxMfH09GRkaHUmdX8fnnn/OLX/zC6dARdG51dmJiIvPnz+fJJ59k9+7dzJ07l9TUVDQajdupsxuypD6Lori4F/hGCOGsAlunwGYCjV6i9RDsPKRUnQqPqDnR5uHhQXHJMbRaL7y9Bzq7jEp7o7bAzmCA/v2hR48WUVRUzSlkZmZiNpsdcwpRUVF1Jn6dqbMbQ3Oqs6tISkqqd+gIrqizAYc6e+HChfTv35/XX3+dDRs2IKV0WZ19NZYsWeKYAK/+qBp2q861qrN37doFwJo1axxDWKNGjcJkMnGpcphRVWdf4V4gVkr5S2A48HBjLy6EmCyEOC6E+FkIUdd3e6XdTCGEFEJcVdbUUljtFhAadh3qgSwzExqh/Iew2WycP39emU+QEm/v0Gv+dqfSyuTmQn6+spIoKEhxFrXCL3dHU2eDMo7+3//+t8acQW06szo7ODiYLVu2AIpMzmQyOXpYqjr7ChVSylIAKWXuVdrWQSjq0JXAbUAkMEcIEemknS/KnMUPjbl+c1NhquDMkQKKSjwwBpRjiNYhtFrMZjNGo5HwcHVPQoeg+jfobt2UIaMBA1pdYNeR1NmgLC2dNGkS3t7e9bbpzOrsN998k1WrVhEXF8ecOXNYu3at48ufqs6uekKIAmBr1SGK6qLqGCnl9AYvLMQoYJmU8tbK499Xvu6VWu3eQqnu9hTwlJSyQS92S6izvzq0lX5nzrH2X935cvcw4m68wN3zyrgx8jp+/vlnAgMDiYqKorAwBU/PPuj13Zr1/ipN56effmJwr16KpqJvX7czmLYG7qjOrqioYNy4cezYscNRnrMj0BR1dkPvckatY+dLEuqnN3Cm2vFZYGT1BkKIIUBfKeV/hBB1d+tcafcg8CAo3bzmxmqzIqSdY+fDKRVawocbsBslUkrsdnvLevpVms7Fi8qu5KovOEVFyv4DlWalujrbldVDnYGsrCxeffXVDpUQmkpDNZq3NPHazgbeHd0SoUiDlgPzr3YhKeX7wPug9BSaGFcNdmTvADuYLRpOn/FFChjiX4JF41FjKardbsZuNzfnrVWait0On34Kb70Fy5YpiooewNeF1gAAIABJREFUPZQdySotgrups8PDw91u6Lgl099ZoG+14z5A9XVdvkA0sL1y7O464DMhxF1XG0JqTqx2K3HFJRw6F4DFqiGouwmDl53r+/tRVlaG0WjEZjORl78TgUbdydxeOHMGEhKgatWO0ajMHajFj1RUmkRLDrzuA8KFECFCCD0wG/is6kkpZaGUspuUsr+Usj+wB2jVhFCF1WwiNW8oZUJDSL8SCrpf2bms1+ux2yvQab0JCBijJoX2wqFDSkIICIBXXwV/fzUhqKg0Ay73FIQQBill3X309SCltAohHgW+BrTAB1LKNCHES8B+KeVnDV+h9bBarRw+UIK0eXBr7yLAgNVqRafTXfGnCC0ajfuMK7ZLiouv6CjuvFOZVL77bmW4qHL5o4qKStO4ak9BCDFCCPEjkF55HCeEcElzIaXcLKUcKKUMrarBIKV80VlCkFKOb4tegj37IHah5eRpH3RaLVHDlZ6A2Wx2m8m0do/ZDO+9B3fcodQ8AGXvwbx56vyBikoz48rw0QrgDuAygJTyMMry1E6B3WblQFYgxUUavL3t9OxzZX7cy8sLiyWLouIfEWqFtbbhxx/hvvtg1SpFV1HpqmmPdFR1dmFhIXfeeSdxcXFERUXxj3/8w+l1y8vLGTduXA376PLlyzEajRQWFjZ4n+oxlZSU8Nvf/pbQ0FCioqIYO3YsP/zQtG1KUkoef/xxwsLCiI2N5eDBg07bJSYmEhMTQ2xsLJMnT3bsWgZ4++23iYiIICoqyrGxUFVn19NGSplZ61xdJ20HxW6zcyLdB52HgX7hZrQh3tilHSEEBoMBuyzD22sAfn5xbR2qe1FeDn/5CyxYoJTHDA5WEkM7Xv3SUdXZK1euJDIyksOHD7N9+3aefPJJx27j6nzwwQdMnz7dsSkMlA/Z4cOHs2nTJpfvt3DhQgICAkhPTyctLY21a9fW+HC+Fr788kvS09NJT0/n/fff5+GH6woYrFYrixcvZtu2bRw5coTY2FiH/G/btm0kJydz5MgR0tLSHElZVWc754wQYgQghRBaIcTvgPqtWh2J9G8pOdeFn091RSOgX2g5ezKKEHY7Pj4+jh2NGo0ejUadxGw1UlNh9mxYv14ZJrr/fkhMhAYMn+2NjqTOFkJQXFyMlJKSkhICAgKcrsuvrc4+efIkJSUlJCQkkJjoWtn2kydP8sMPP5CQkICmcoPhgAEDmrxjODk5mXnz5iGE4IYbbqCgoKDOrnCljK6ktLQUKSVFRUUOP9K7777L0qVLHTrv7t27O16nqrPr8jDKEFIwcAFl93GjPUjtErsFs3dfzl4KQGjAs3sRoGdoLyM+Pj5tHZ374uureIsGDoQXXoBr2DyYe6mp22zqEtRtokvtOpo6+9FHH+Wuu+6iV69eFBcXs2HDBscHdhVms5lTp07Rv39/x7mq2gtjxozh+PHjXLx4scaHqTPS0tKIj4+v0duoj3vuuaeuAh144oknmDdvXo1z2dnZ9O17ZQV8lTq7Z8+ejnMeHh68++67xMTE4O3tTXh4uKM3d+LECb7//nuee+45jEYjb7zxBsOHDwdUdXYdpJQXUZaTdkqKiwTnL3rhrbfTrWc5N/TvRmlpKQaDgfT0dDRC1SW0CikpEBen9Az69YO//x0iI6+5EpqrH+DNSUdVZ3/99dfEx8ezdetWTp48yS233MKYMWNqLLS4dOkSXWvtEk9KSmLTpk1oNBqmT5/Ov/71LxYtWtRs77WheZPauKLOtlgsvPvuuxw6dIgBAwbw2GOP8corr/D8889jtVrJz89nz5497Nu3j1mzZnHq1CmEEKo6uzZCiFVCiPdrP1ojuBYl/VvQeJBxyhdhtxHRKx+th/Ifx2g0OjTB1b99qLQAeXnw7LOwcCFU1sUFIDa2SaUx24KOqs7+xz/+wfTp0xFCEBYWRkhISI2ay6C8N5PJ5Dg+cuQI6enp3HLLLfTv35+kpCTHEFJD6uyoqCgOHz7sUHA3xD333ONUnb1u3bo6bV1RZ6ekpABKL00IwaxZsxzq7D59+jh+BiNGjECj0ajq7Ab4FthS+dgJdAdc3q/QbrFbOFXchVOHrSAhMkZi1xvIzMwkNzeX48eP1+lCqzQjUipJYOZM+L//U3YkWzpHVbuOps6uro2+cOECx48frzPR7e/vj81mcySGxMREli1bRkZGBhkZGeTk5JCdnU1mZibDhw9n586dnD9/HoD9+/dTUVFB3759CQ0NZdiwYfzhD39wJLb09HSSk5PrxLVhwwan6uzaQ0egqLPXrVuHlJI9e/bQpUuXGkNHAL179+bo0aPk5uYCNdXZU6dOZetWxfd54sQJzGYz3bp1cxy7kzrbleGjGn04IcSHQMfWJFb2EixmM/mmvngYjIT4neOsUMaDo6KiMBgMmEw5FJdk4eXVv60j7lycPw8vvwyV39IYORKeew56dZ6KdtXV2XPnziU5OZnHHnuMRYsWYbPZmDt3rlN1dllZGUKIq068VldnW61Whg8f3qA6u2fPngwdOtRpMfsXXniB+fPnExMTg5SSP//5z44PxOpMmjSJHTt2cPPNN5OUlMSXX35Z4/lp06aRlJTEM888w1//+lemTJmCvXLRRmJiouNL1urVq3nyyScJCwvDy8uLwMBAh8L6WpkyZQqbN292XLP6str4+HhSUlLo1asXf/jDHxg7diweHh7069ePtWvXArBgwQIWLFhAdHQ0er2e//3f/1XV2S6/QIhQ4GspZVjLhNQwzaLOPv4lRNzG0V3fMW9JKCUXvXl07i6yRvhwhx+MGTMGm62MgoIf8PIKwcsrpHmCV1FWFj3yCJSVKRPKTzyhbEprYuEiZ6pglebl0KFD/OUvf+HDDz9s61BaDVWd7QQhRD5X7KYaIA+ot4paR8JksnMpV4/Obqf3YD1dBvQkrKs3QggqKs6h1fmqCaG5GThQMZn27w/PPKMUwlHpEAwZMoSbbroJm83m0uqhzoCqzq6FUPpPcUB25Sm7bGzXoh1z+FgpUgp6+NsY1s+LLTabo/JUefkZfH2j2jjCToDNBhs2KL0BPz/Q62HNGuXvKh2OBQs6bal2p7ijOrvBmdTKBLBJSmmrfHSahACQk2NEq9HQy7cAoVfMqEajESmVlRF6fWBbhtfxOXFC2Xj2l78ojyrUhKCi0m5xpU+0VwgxVErpXCbSgTl3UVlm1qNbBbv9AvEwm9Hr9RQX/4jQeOC8TpDKVTGbYfVq+N//VXoK110Ht97a1lGpqKi4QL1JQQihk1JagdHAA0KIk0ApyiellFLWXwW8A2C327lwUTGiXhdQjslqYGKAL0IIpLTj6zMIoW5cazxHjsBLL0FGhjJ5PGsWPPooeKl1KFRUOgIN9RT2AkOBqa0US6titVrJzVXc/D0Dy7DZfFW1RVM5c0bZhGa3K7uSX3xR2aWsoqLSYWjoq7AAkFKedPZopfhaDKvVxsVLShK4LqAMAIPBgMVSiNVW0pahdVz69oVp0xSzaWKi2yWEjqrOzs/PZ9q0acTGxjJixAhSU1OdXldKyYQJEygqKnKc27RpE0KIGjugt2/fzh133FHjtfPnz2fjxo2AYg1YunQp4eHhREdHM2LEiDp7Hq6FV155hbCwMCIiIvj666+dttmyZQtDhw4lPj6e0aNHOzb/LVmyxLFjeuDAgQ6lR25uLpMnT25ybB2JhpJCkBDiifoerRZhc1O5cS0nx4rZrMXb24KPpwWBQK/XY7Hko9P5otN1vfq13J2iImWo6NChK+eWLlX2Iej19b+uk9JR1dkvv/wy8fHxHDlyhHXr1rF48WKn7TZv3kxcXFwNJ1JiYiKjR49ulEX0hRde4Ny5c6SmppKamsrnn39OcXFxk97D0aNHSUpKIi0tja+++opHHnnE6Ua9hx9+mI8//piUlBR+9atfkZCQACh1Iap2TD/22GNMnz4dgKCgIHr27FmvYbYz0lBS0AI+gG89j46J3QLhN3PyZxtmm5XuQWXY7RKDweDYwajVeqmlN6/G1q3wy1/CZ5/Ba68p2gpo8ia0zkJHUmcfPXqUiRMVgWCVPuPChQt12tVWZ5eUlLBz507WrFnjclIoKytj1apVvP322w5NdY8ePZjVxDoZycnJzJ49G4PBQEhICGFhYezdu7dOOyGEo6dTWFhYx48EV+yvVUydOpWPP/64SfF1JBr65DsnpXyp1SJpZfZuzQTZg9hwbyQSo9GA3V5BRcUFdSlqQ1y+DH/+s5IUAOLjFb11O0sG/3ep8OqNGsmkbq6V/uxo6uy4uDg++eQTRo8ezd69e8nMzOTs2bP06NGjRrudO3fy3nvvOY4//fRTJk+ezMCBAwkICODgwYMMHdrw+pOff/6Z4OBgl0rdLlmyhG3bttU5P3v2bJYurbl/Njs7mxtuuMFxXKXOrs3q1auZMmUKnp6e+Pn5sadWJb/MzExOnz7NhAkTHOeGDRvG888/f9V4OwsNJYX29VvezJzN0SP0OkLDFQmbTqfDZqtAShtGo2pGrYOU8MUXyn6DoiJlNdFjj8GMGdAOxYGufoA3Jx1Vnb106VIWL15MfHw8MTExDBkyxOkO3ry8PHx9rwwSJCYmOu43e/ZsEhMTGTp0aLO91+XLl7vc1hV1dtU1N2/ezMiRI3n99dd54oknWL16teP5pKQkZs6cWWPHtrupsxtKCq0vpG9Fzl80IoBe+hywSrSVvwRC44FWa2jb4NojxcWwfLmSEG68EX7/e6hloXR3quYUCgsLueOOO1i5ciWPP/44UVFRfPfddzXaOlNnxzViYr451dl+fn4OgZyUkpCQEEJC6updqnTyGo2Gy5cvs3XrVlJTUxFCYLPZEELw2muvNajODgsLIysri+Li4hoJxhmN6Sm4os7Ozc3l8OHDjBw5ElCSZO1J5KSkJMdcUBXups52lKjrKI/rr79eNoljm6WUUg6POy/7DciVe97/j/xy3Q75dW6BNJsLZV7+3qZdvzNhs0lptV453rJFyi++kNJub7uY6uHo0aNtHYL09vZ2/P3gwYOyb9++0mw2y7KyMhkSEiK/+eYbKaWUZWVl8vbbb5crVqyQUkp5+PBhGRoaKo8fPy6llNJms8k333yzzvW3bdsmb7/9dimllOXl5bJv374yPT1dSinl/fffL9966y0ppZTjxo2T+/btkzk5OTI4OFheunRJms1mOXr0aLlo0aI6183Pz5cVFRVSSinff/99OXfuXKfvb+TIkY77/f3vf5cPPvhgjefHjh0rv/vuO2kymWT//v0d/yYZGRkyODhYFhQUSCmlfPrpp+X8+fMd98zJyZEffvhhwz/cq5CamipjY2OlyWSSp06dkiEhIdJa/f+ulNJiscjAwEDHz3n16tVy+vTpjuePHTsm+/XrJ+21/n/v379f3nrrrU2Kr7Vx9vsA7JcufMa2v35/K1BSAoXFerQ6O7mmS/zUxQudENjt5W0dWvshIwMeeAAq1cIATJgAU6a0u/mD9kh1dbanpyfJyckkJCQQERFBTEwMw4cPd6rOHjx4MNHR0XXqC9emujo7JiYGjUbToDr75ptvrne8/6effiIqKopBgwbx5Zdf8te//tVpu9tvv53t27cDytDRtGnTajw/Y8YM1q9fj8Fg4KOPPuLXv/418fHxzJw5k9WrV9OlizKkl5CQQFBQEJGRkURHRzN16lTHENe1EhUVxaxZs4iMjGTy5MmsXLnSMQQ0ZcoUcnJy0Ol0rFq1ihkzZhAXF8eHH35YQ9mdmJjI7Nmz6/SuVHV2O6fJ6uzjX/LVj8E89HQ3rutdxrOztlN6fTy3RdmwWAvx9AzGx9u9BFg1sFph3TpYtUopetOrF2zc2O6XmKrq7Jbn3LlzzJs3j2++6djlVBrL2LFjSU5Oxt/fv61DcZkWVWd3RjKzdEiNhtA+OpAghBmN1ht/nwh0uo672rbJHD8Of/yjIrIDuPtuWLy43ScEldahZ8+ePPDAAxQVFbm0eqgzkJubyxNPPNGhEkJTccukcDZH+ZAL72EGKdFodWg0BvdNCFYrvPeeIrCz25XewfPPw4gRbR2ZSjujqfsJOhpBQUFMndopTT/14pZJ4cJFHVYrWGyXMPf1xcYlBG68g1mrVSqiSQlz5sDDD6sCOxUVN8Utk0JJiTIBFRciGNy3iJ9EMJ6e/do4qlamrAxKSyEoSJk4fuEFuHQJYmPbOjIVFZU2xO1WH/33YjpnLyjCO29PMx5FAoNHT/fSWuzerSitn3/+ip6iVy81IaioqLhfT6G0wANh64JO64GXrgyNRou+t5vMJRQWKjuSv/hCOfb3V851deOhMxUVlRq0aE9BCDFZCHFcCPGzEGKpk+efEEIcFUIcEUJsEUK0yhiOyeyBxm5B530MndFAJzd6KL2BLVsUgd0XXyiriR5/XNmDoCaEZuX8+fPMnj2b0NBQIiMjmTJlCu+//34dlbSKSnulxXoKQggtsBK4BTgL7BNCfCalPFqt2SFgmJSyTAjxMPAa0HxOYCcUZ1+ktEwHWPEN6kqAz+hr9s90CKRUhomq/PJDhyrHwcFtG1cnRErJtGnTuP/++x3W0JSUFD7//PM2jkxFxXVasqcwAvhZSnlKSmkGkoC7qzeQUm6TUpZVHu4B+rRgPABUVOjQGozoDSYMOg90OmNL37JtEQIGDFBWE/3+9/D3v7tHQhg2rP7HJ59caffJJw23bQTbtm3Dw8Ojxs7i+Ph4xowZQ0lJCTNnzmTQoEHce++9DnfRSy+9xPDhw4mOjubBBx90nB8/fjzPPPMMI0aMYODAgXz//feAYmB96qmniImJITY2lrfffhuAAwcOMG7cOK6//npuvfXWq+6IVlGpj5ZMCr2BM9WOz1aeq4/fAE7LLwkhHhRC7BdC7M/Nzb3mgEryKygr9gQpMRrNpOj6s6XChK6z9RRycqC6S/7++5Vdye3UaNpZSE1NraPIruLQoUO89dZbHD16lFOnTjlqGzz66KPs27eP1NRUysvL+c9//uN4jdVqZe/evbz11lv88Y9/BOD999/n9OnTHDp0iCNHjnDvvfdisVh47LHH2LhxIwcOHGDBggU899xzLf+GVTolLTnR7OyT1qlTQwhxHzAMGOfseSnl+8D7oGgurjUgm9WGWWqwWM34+JjR6PXcbPDEM7CT7M6022HDBli5EgwG+Ne/ICAAdDro3r2to2tdXFWhTJ+uPFqYESNG0KeP0hGu0muPHj2abdu28dprr1FWVkZeXh5RUVHceeedlaEpcV1//fVkZGQA8O233/LQQw851NYBAQGOCmZVqm6bzUZP1WCrco20ZFI4C1QvTNAHqCMlF0LcDDwHjJNS1i0d1cyUVxgwmW300Zfj0Zn0DadOQUICHDmiHI8dq/YKWpmoqChHHeLaVFUZA6WWs9VqxWQy8cgjj7B//3769u3LsmXLMJlMdV5T1R6c12aQUhIVFcXu3bub+y2puCEt+amxDwgXQoQIIfTAbOCz6g2EEEOA94C7pJQXWzAWB+XlBqS0Q1cjHlYfhLaDDx1ZrbBmDdx7r5IQgoKUZacvv6yuLGplJkyYQEVFBatWrXKc27dvH//973+dtq9KAN26daOkpKTehFKdSZMm8fe//92RJPLy8oiIiCA3N9eRFCwWC2lpaU19OypuSoslBSmlFXgU+Br4CfinlDJNCPGSEOKuymavo9SB/pcQIkUI8Vk9l2s2LhQob1nvaeXW+N4YIwJa+pYty3PPwbvvKkbTadOUIaOxY9s6KrdECMGmTZv45ptvCA0NJSoqimXLljmtAwzQtWtXHnjgAWJiYpg6dSrDhw+/6j0WLlxIcHAwsbGxxMXFsX79evR6PRs3buSZZ54hLi6O+Ph4du3a1dxvT8VNcCt1dmFuGb/7XTK7908i5oYMEtfEOS072KFISVHMps8+Cy58qHRWVHW2isoVmqLOdqtB5x9OXcZUYURKK14GHZqOOOZ+8CC8//6V4/h4ZWWRGycEFRWV5qODf01uHFYb6NADEi+jpmMlhdJSWLEC/v1v5XjYMGUjGiiWUxUVFZVmwK2SAkCxSQA6PL3sbR2K6+zcCX/6E1y8qCwvXbAAYmLaOioVFZVOiNslhfJyTzx0Brw8bW0dytUpKIA334QvK/f0RUXBiy9CaGjbxqWiotJpcb+kYDIAEi+vDjDBvmqVkhAMBnjkEaUATkca8lJRUelwuFdSkBKTyYiU4OXdToePpFR8RQC//S3k5cGiRdCnxbVQKioqKu61+gh5nnKTASEEXl7tbPhISti0SZkvMJuVc35+8MorakJQUVFpNdyqpyClnfJyL4xagZdvOxo+OntWUVRU7b/45hu4/fa2jUlFRcUtcaueQmHGSWw2LXqtxBjU1tGgCOw+/hjuuUdJCP7+ip5iypS2jkzlGhFCMHfuXMex1WolKCioxYvsaLVa4uPjiY6O5s4776SgoMDx3NmzZ7n77rsJDw8nNDSUxYsXY67qjeK8MNCJEyfq3KO8vJxx48Zhs13pZW/atAkhBMeOHXOcy8jIIDo6usZrly1bxhtvvNGo+zWWr776ioiICMLCwnj11Vedtlm+fDlRUVFER0czZ86cGq6php5ryZgaarNgwQK6d+9e4+dpNpsZO3asQ3XS3LhVUigv0yC0GryMNjRtrcs+dQp+/WtYvhwqKuC22xRFxaRJV+YUVDoc3t7eDg02wDfffEPv3g0Z45sHT09PUlJSSE1NJSAggJUrVwKKLG/69OlMnTqV9PR0Tpw4QUlJiUOtXVUYaPz48Zw8eZKjR4/y8ssvc+HChTr3+OCDD5g+fTraavtiEhMTGT16tKOo0NVozP0ag81mY9GiRXz55ZccPXqUxMREjh49WqNNdnY2K1asYP/+/aSmpmKz2RxxN/ScM7Zv3878+fObHNPV2syfP5+vvvqqxmv0ej0TJ05kw4YNrvxoGo1bDR+Vl3sA4KMpRePl1bbBHDsGaWmK0vrZZ2H06LaNpxPRyNo4LuOqXeW2227jiy++YObMmSQmJjJnzhxHkZyPPvqIFStWYDabGTlyJO+88w5arZapU6dy5swZTCYTixcv5sEHHyQjI4PbbruN0aNHs2vXLnr37k1ycjKenp4N3n/UqFEcqbTlbt26FaPRyK9//WtA6VEsX76ckJAQ/vjHP7Jnzx6nhYGc8fHHH7N+/XrHcUlJCTt37mTbtm3cddddLFu27Ko/m/oKETWVvXv3EhYWxoABAwCYPXs2ycnJREZG1mhntVopLy/Hw8ODsrKyGl6qhp5rqZiu1mbs2LEObXp1pk6dyu9//3vuvffeJsXoDPfqKZQrqmwfowWPtvDN5+df+fttt8H//A/8859qQuhkzJ49m6SkJEwmE0eOHGHkyJGA4qPZsGEDO3fuJCUlBa1Wy8cffwwo38IPHDjA/v37WbFiBZcvXwYgPT2dRYsWkZaWRteuXfl31Y72erDZbGzZsoW77lKck2lpaXUK//j5+REcHMzPP//cYGGg6pjNZk6dOkX//v0d5z799FMmT57MwIEDCQgI4ODBg1e9jqv3AxgzZgzx8fF1Ht9++22dttnZ2fTte8XU36dPH7Kzs2u06d27N0899RTBwcH07NmTLl26MGnSpKs+V52RI0cSHx/PwoUL+eyzzxwxfV1V7raRMbnSxhnR0dHs27fvqu2uBbfqKZRV9hS8jdbWVVyYTEoZzI0b4cMPISREGSKaNav1YnAjrtGX2GzExsaSkZFBYmIiU6rND23ZsoUDBw44bKjl5eV0ryx+tGLFCjZt2gTAmTNnSE9P57rrriMkJMTxTbp6sZ3alJeXO4r3XH/99Y6CO87qLzR0vj4uXbpE11oq9sTERH73u98BSiJMTExk6NCh9V63sbXQq3pXruBM7Fn7fvn5+SQnJ3P69Gm6du3KL3/5Sz766CPuu+++Bp+rzg8//AAow0dr165l7dq1TYrJlTbO0Gq16PV6iouL8fX1vWr7xuBWScFkUpJCoa9A31pJYf9+ZWXR2bPKxrNDh5SkoNKpueuuu3jqqafYvn2741u/lJL777+fV155pUbb7du38+2337J79268vLwYP368Y5KzdnGeqrmK2lTNKRQWFnLHHXewcuVKHn/8caKiour0LoqKijhz5gyhoaFcvHjRpToOnp6eNSZeL1++zNatW0lNTUUIgc1mQwjBa6+9RmBgIPnVe8UodR9CQkLo06ePS/cDpadQXFxc5/wbb7zBzTffXONcnz59OHPmSvXfs2fP1hn++fbbbwkJCSEoSFllMn36dHbt2sV9993X4HPXiisxudKmPioqKjAam7/GvNsMH0kpuVBYDBIMXlYmtHQJzpISZSXRQw8pCSEsDNaubZXSjyptz4IFC3jxxReJqeaomjhxIhs3buTiRaWeVF5eHpmZmRQWFuLv74+XlxfHjh1jz54913zfLl26sGLFCt544w0sFgsTJ06krKyMdevWAcrw0pNPPsn8+fPx8vJyuTCQv78/NpvNkRg2btzIvHnzyMzMJCMjgzNnzhASEsKOHTvw8fGhZ8+ebNmyxfE+v/rqK0aPHt2oQkTff/89KSkpdR61EwLA8OHDSU9P5/Tp05jNZpKSkhxDaFUEBwezZ88eysrKkFKyZcsWh166oeecMX78+AZ7Ca7G5EobZ1y+fJmgoCA8PDyu2raxuE1SMJtslBRq0Uvw8hEtq4tISVGGhj75RBHYPfSQMmxUa9JLpfPSp08fFi9eXONcZGQkCQkJTJo0idjYWG655RbOnTvH5MmTsVqtxMbG8sILL3DDDTc06d5DhgwhLi6OpKQkR+Gff/3rX4SHhzNw4ECMRiMvv/wy0LjCQJMmTWLHjh2AMnQ0bdq0Gs/PmDHDMRG9bt06EhISiI+PZ8KECfzhD38gNDS00YWIXEWn0/G3v/2NW2+9lcGDBzNr1iyioqIAmDJlCjk5OYx4hHkgAAAPMElEQVQcOZKZM2cydOhQYmJisNvtPPjggwANPledqjmF2g9ncwquxNRQG4A5c+YwatQojh8/Tp8+fVizZg2gTNhPaaml61LKDvW4/vrr5bVgKjXLu6Z+KUNCz8tH/+fUNV3DZbKypBw1Ssr586U8ebJl76UipZTy6NGjbR1Cp+fgwYPyvvvua+swVKSU06ZNk8eOHav3eWe/D8B+6cJnrJvNKRiQwPXezXxhKeGHH2DkSGUCuW9fpW5yRIQqsFPpNAwZMoSbbroJm81WY6+CSutiNpuZOnUqERERLXJ9t/nEktKO2ayMv/l5N6Pi4sIFWLIEHn0UPv/8yvnBg9WEoNLpWLBggZoQ2hi9Xs+8efNa7Ppu01Ow2UowV2gRUuDn0wxJwW6HTz+Ft96CsjLw8YEWmPRRUVFRaU3cJimAUmAHKfBr6rLerCxlmWnVZp3x4+GZZyCoPQiVVFRUVK4dt0oKFZU7mrsE6K/9IkeOKKuJzGYICFB2JU+cqPqKVFRUOgVukxSk3Y6p3ABaQRf/JgzzDB4MwcHKJPITT0CXLs0XpIqKikob4zZJwVRQhs2qQ6cXeHs2YgLYbIaPPlI2nXXtqswbfPABtLVQT0VFRaUFcJvlMSWlGiTgaZRoPFx82z/+CPfdB++8A2++eeW8mhBUVFQ6KW7TUyguUcb8Pb2seA3q0XDj8nJ4911ITFT2IAQHq3oKFRUVt8B9kkKxBrDh6Wlt2EK4d6+ysignR9lncP/98OCDoG/C5LRKq3PmzBkqKiqa7XoGg6GG4rg5WLBgAf/5z3/o3r07qampLr+uoKCA9evX88gjjzh9ftmyZfj4+PDUU0+5dL3Gtlfp3LjV8JFWCjy9bfU3ysqCRYuUhDBwIKxbp2xKUxNCh6OiogIvL69mezQ2wbhSmctZVS1XKCgo4J133mn061RUXMFtkkJxsdI7MPZuYLVQcDDMmQOPPKIkhEGDWik6FXdk7NixBAQENNimtLSU22+/nbi4OKKjo9mwYQNLly7l5MmTxMfH8/TTTwPwpz/9if/f3r0HSVmdeRz//sSRkQSMwWQLnDCQEtgVNLPjLJBQmKgEgbiyJlPOUCKyhavRNRvCjmGNqQom+UNFK+WFSAyx1JUEcCpsSERNKoFIGTGCICCGDSHETBGX6yIjl3B58sc5tM0wl3dm+jLd/Xyqpqr77dPv+5zu6X76Paf7PMOHD2f8+PFs3bq1w2O31/6ZZ55h1KhRVFVVceutt3LixAnmzJlzWiKaO3cuD6bPs7miUTrDR80h//VJ/+Havn0wbx584Qvv13CcPTv3wbmiMXr0aI4ePUpzczP79u1LFci57777uPrqqzu9vxdeeIGBAwfy3HPPAXDgwAFGjx7N5s2b2bBhAwDr1q1j8eLFrF+/nuPHj1NdXd1udbP22qdXhysrK+P2229n0aJF1NfXM2vWrNSQ1dKlS7t0luN6vpJKCkd6ldG3n4XJ4+efhwcegHffhT/9CRYt8h+guW7rTGWuJC655BIaGhqYM2cO11xzDePGjTujgM3q1au57rrr6BO/FdfRevzttW+rOtz06dPZtWsXO3fuZPfu3Zx//vkMGjSoW31zPVNWk4KkicBDQC9goZnd2+L23sDTwGXAXqDOzHZkI5bmZmGI6t5H4Mtfht/8JtwwZgx87WueEFyPNGzYMNatW8eKFSu46667mDBhQquLoXW21GVb7a2N6nAAtbW1NDY28s4771BfX9+p47nCkbU5BUm9gPnAJOBiYKqkllVmZgL7zewi4DvAfdmKp7lZnHvoEB954qGQEPr1g7lz4ZFHoJsFPpxrKUllriR27txJnz59mDZtGg0NDbz++uv07dv3tDKVl19+OcuWLePw4cMcPHiQn6av1tuK9tq3VR0OQh3mxYsX09jYSG1tbbf75nqmbJ4pjAK2mdl2AEmLgSnAlrQ2U4C58XIj8KgkxYIQGfXuvhN8oPk9+n5oP1x5ZVjArn//TB/G9RC9e/fm0KFDGd1fEqfmFFpqbU5h6tSprFq1ij179lBRUcE999zDzJkzT2uzadMm7rzzTs466yzKysp47LHH6N+/P2PHjmXkyJFMmjSJefPmUVdXR1VVFZWVlYwbNy51/8mTJ7Nw4cLTKptVV1e32T69OtzJkycpKytj/vz5VFZWMmLECA4ePMiFF17IgAED2j2GK1zKwvtv2LFUC0w0s5vj9RuB0WZ2R1qbzbFNU7z+h9hmT4t93QLcAjBo0KDLTn1y6YyvNhzlJ0ve5qmv7mHMlz7Z1W65Huqtt95qt6auc6WktdeDpHVmVtPRfbP5ldTWBi1bZqAkbTCzx82sxsxqPtLF5anvf6A3W/881BOCc861I5tJoQlI/wloBbCzrTaSzgbOA/ZlMSbnnHPtyGZSeA0YKmmIpHOAemB5izbLgZvi5VrgV9mYT3Clwf91nOv+6yBrScHMjgN3AC8CbwFLzexNSd+UdOqL0T8A+kvaBswG/itb8bjiVl5ezt69ez0xuJJmZuzdu5fy8vIu7yNrE83ZUlNTY2vXrs13GK6HOXbsGE1NTRw5ciTfoTiXV+Xl5VRUVFDWomZ80onmkvlFsytuZWVlDBkyJN9hOFfwSmZBPOeccx3zpOCccy7Fk4JzzrmUgptolrQb6PxPmoMLgD0dtiou3ufS4H0uDd3pc6WZdfjr34JLCt0haW2S2fdi4n0uDd7n0pCLPvvwkXPOuRRPCs4551JKLSk8nu8A8sD7XBq8z6Uh630uqTkF55xz7Su1MwXnnHPt8KTgnHMupSiTgqSJkrZK2ibpjJVXJfWWtCTe/qqkwbmPMrMS9Hm2pC2SNkr6paTKfMSZSR31Oa1drSSTVPBfX0zSZ0nXx+f6TUk/zHWMmZbgf3uQpJWS1sf/78n5iDNTJD0haVesTNna7ZL0cHw8NkqqzmgAZlZUf0Av4A/Ax4FzgDeAi1u0uR1YEC/XA0vyHXcO+nwF0Cdevq0U+hzb9QVeAtYANfmOOwfP81BgPXB+vP7RfMedgz4/DtwWL18M7Mh33N3s8+VANbC5jdsnA88TKleOAV7N5PGL8UxhFLDNzLab2V+BxcCUFm2mAE/Fy43AVZJaKw1aKDrss5mtNLNTlezXECrhFbIkzzPAt4D7gWJYUztJn/8NmG9m+wHMbFeOY8y0JH02oF+8fB5nVngsKGb2Eu1XoJwCPG3BGuBDkgZk6vjFmBQuBP6cdr0pbmu1jYViQAeA/jmJLjuS9DndTMInjULWYZ8l/SPwMTP7WS4Dy6Ikz/MwYJiklyWtkTQxZ9FlR5I+zwWmSWoCVgBfyk1oedPZ13unFGM9hdY+8bf83m2SNoUkcX8kTQNqgE9nNaLsa7fPks4CvgPMyFVAOZDkeT6bMIT0GcLZ4GpJI83s/7McW7Yk6fNU4Ekze1DSJ4H/jn0+mf3w8iKr71/FeKbQBHws7XoFZ55OptpIOptwytne6VpPl6TPSBoP3A1ca2ZHcxRbtnTU577ASGCVpB2EsdflBT7ZnPR/+ydmdszM/ghsJSSJQpWkzzOBpQBm9gpQTlg4rlgler13VTEmhdeAoZKGSDqHMJG8vEWb5cBN8XIt8CuLMzgFqsM+x6GU7xESQqGPM0MHfTazA2Z2gZkNNrPBhHmUa82skGu5Jvnf/h/ClwqQdAFhOGl7TqPMrCR9fhu4CkDSPxCSwu6cRplby4Hp8VtIY4ADZvaXTO286IaPzOy4pDuAFwnfXHjCzN6U9E1grZktB35AOMXcRjhDqM9fxN2XsM/zgA8Cz8Y59bfN7Nq8Bd1NCftcVBL2+UVggqQtwAngTjPbm7+ouydhn/8T+L6krxCGUWYU8oc8ST8iDP9dEOdJvgGUAZjZAsK8yWRgG3AI+NeMHr+AHzvnnHMZVozDR84557rIk4JzzrkUTwrOOedSPCk455xL8aTgnHMuxZOC63EknZC0Ie1vcDttB7e1mmQnj7kqrsT5RlwiYngX9vFFSdPj5RmSBqbdtlDSxRmO8zVJVQnuM0tSn+4e25UGTwquJzpsZlVpfztydNwbzOwThMUS53X2zma2wMyejldnAAPTbrvZzLZkJMr34/wuyeKcBXhScIl4UnAFIZ4RrJb0evz7VCttRkj6bTy72ChpaNw+LW379yT16uBwLwEXxfteFdfp3xTXue8dt9+r9+tTPBC3zZXUIKmWsL7UonjMc+Mn/BpJt0m6Py3mGZIe6WKcr5C2EJqkxyStVaijcE/c9h+E5LRS0sq4bYKkV+Lj+KykD3ZwHFdCPCm4nujctKGjZXHbLuCzZlYN1AEPt3K/LwIPmVkV4U25KS57UAeMjdtPADd0cPx/BjZJKgeeBOrM7BLCCgC3SfowcB0wwswuBb6dfmczawTWEj7RV5nZ4bSbG4HPp12vA5Z0Mc6JhGUtTrnbzGqAS4FPS7rUzB4mrItzhZldEZe++DowPj6Wa4HZHRzHlZCiW+bCFYXD8Y0xXRnwaBxDP0FY06elV4C7JVUAPzaz30u6CrgMeC0u73EuIcG0ZpGkw8AOwvLLw4E/mtn/xtufAv4deJRQn2GhpOeAxEtzm9luSdvjmjW/j8d4Oe63M3F+gLDsQ3rVresl3UJ4XQ8gFJzZ2OK+Y+L2l+NxziE8bs4BnhRc4fgK8H/AJwhnuGcUzTGzH0p6Ffgc8KKkmwnLDD9lZnclOMYN6QvmSWq1xkZcj2cUYRG2euAO4MpO9GUJcD3wO2CZmZnCO3TiOAkVyO4F5gOflzQEaAD+ycz2S3qSsDBcSwJ+YWZTOxGvKyE+fOQKxXnAX+Ia+TcSPiWfRtLHge1xyGQ5YRjll0CtpI/GNh9W8vrUvwMGS7ooXr8R+HUcgz/PzFYQJnFb+wbQQcLy3a35MfAvhDoAS+K2TsVpZscIw0Bj4tBTP+A94ICkvwMmtRHLGmDsqT5J6iOptbMuV6I8KbhC8V3gJklrCENH77XSpg7YLGkD8PeEkoVbCG+eP5e0EfgFYWilQ2Z2hLAC5bOSNgEngQWEN9ifxf39mnAW09KTwIJTE80t9rsf2AJUmtlv47ZOxxnnKh4EGszsDUJt5jeBJwhDUqc8DjwvaaWZ7SZ8M+pH8ThrCI+Vc4Cvkuqccy6Nnyk455xL8aTgnHMuxZOCc865FE8KzjnnUjwpOOecS/Gk4JxzLsWTgnPOuZS/AZqRo4LZLjsdAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.52050287, 0.69786465, 0.89777595, ..., 0.16324709, 0.93710172,\n", + " 0.68392801])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " for mod in range(5):\n", + " print('>>')\n", + " curr_try = 0\n", + " while curr_try <10:\n", + " print('.')\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(1, activation='sigmoid'))\n", + " # Compile model\n", + " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", + " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + " \n", + " # Fit the model\n", + " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", + " if history.history['acc'][-1] > 0.53:\n", + " break\n", + " else:\n", + " curr_try += 1\n", + "\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", + " \n", + " # evaluate the model\n", + " probas_ = model.predict(X[test,:])\n", + " prbs.append(probas_)\n", + " # Average the predictions\n", + " probas_ = np.mean(np.hstack(prbs), axis=1)\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.nn.v4.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7f21bb64eba8>]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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7gc6nNvRd9vNTy3YZbFVJVE9bKkbSjbnHc2WJSEUo62mYaJcqHj5W/vMh1cWBJBjb/iZLunAXqSplyfa8gsKwD8Okopw9FX9SUE6cwl2kAvxrVk7U6WW5akZSV6zvb0okhbtInL5pQ4LZO2N/JYCUTyKu0vKjcBeJUykfpkxJX6wr7Zu+pbpTuItIVOX9xLZUraQL929a70mqj1Q/QSqpJenCXURE/CncRURSUNKFexX91KKISFJJunB/drL/NyiKiHzTJV24z8ou+YPHIiISLunCPdr3dYiISLikC3fTtZAiIr6SL9yrugARkSSQfOGudBcR8ZWE4a50FxHxk3zhXtUFiIgkgeQLd6W7iIivpAt3ERHxp3AXEUlBCncRkRSkcBcRSUEKdxGRFKRwFxFJQQp3EZEUFFe4m9lgM1ttZtlmNjLK/LZmNtXMFprZEjMbkvhSA5yrqCWLiKQO33A3s5rA88AVQFfgBjPrGtHsQeBd51wvYDjwt0QXWkTZLiLiL56ee18g2zm3zjmXB7wNXB3RxgEnebcbA1sTV2I4fUBVRMRfWhxtWgGbQu5vBvpFtHkE+NTMfg40AC5JSHUiInJC4um5R+ssR46O3AC86pxrDQwB3jCzEss2s9vMLMvMsnbt2lX2aqOsWERESoon3DcDbULut6bksMsI4F0A59wXQF2gWeSCnHMvOucynXOZ6enpJ1axiIj4iifc5wGdzKy9mdUmcMJ0bESbjcDFAGb2LQLhfmJdcxERKTffcHfO5QN3AhOBlQSuilluZqPNbJjX7F7gJ2a2GHgLuMm5irloUSdURUT8xXNCFefcBGBCxLRRIbdXAAMSW5qIiJwofUJVRCQFKdxFRFKQwl1EJAUp3EVEUpDCXUQkBSncRURSkMJdRCQFJV2467tlRET8JV+4K91FRHwlXbiLiIi/pAt305fLiIj4Srpw17CMiIi/pAt3ERHxl3ThrmEZERF/SRfuIiLiT+EuIpKCFO4iIilI4S4ikoIU7iIiKSjpwl3XuYtIsht0RnqFryPpwl1ERPwp3EVEUpDCXUQkBSVhuGvQXUTET9KFu06oioj4S7pwFxERf0kX7vriMBERf0kX7iIi4k/hLiKSghTuIiIpSOEuIlLJKuPUYVzhbmaDzWy1mWWb2cgYbb5nZivMbLmZvZnYMovpUkgREX9pfg3MrCbwPHApsBmYZ2ZjnXMrQtp0Au4HBjjn9plZ84oqWERE/MXTc+8LZDvn1jnn8oC3gasj2vwEeN45tw/AObczsWWKiEhZxBPurYBNIfc3e9NCdQY6m9ksM5tjZoMTVaCIiJSd77AM0cf+I0e+04BOwCCgNTDDzLo55/aHLcjsNuA2gLZt25a52GgrFhGRkuLpuW8G2oTcbw1sjdLmI+fccefcemA1gbAP45x70TmX6ZzLTE+v+C+rFxH5poon3OcBncysvZnVBoYDYyPafAhcCGBmzQgM06xLZKEiIhI/33B3zuUDdwITgZXAu8655WY22syGec0mAnvMbAUwFfi1c25PRRSsr5YREfEXz5g7zrkJwISIaaNCbjvgHu9fhdKYu4iIv6T7hKrTp5hERHwlXbiLiIi/pAt30xe6i4j4Srpw17CMiIi/pAt3ERHxp3AXEalklTG8rHAXEUlBSRfuGnEXEfGXdOEuIiL+ki7cdSGkiIi/pAt3ERHxl3Thfv3ZJ/Y98CIi3yRJF+6N69Wq6hJERKq9pAt3ERHxp3AXEUlBCncRkRSkcBcRSUEKdxGRFKRwFxFJQQp3EZEUlHThrh9iEpFkVxkxlnThLiIi/hTuIiIpSOEuIpKCFO4iIilI4S4ikoIU7iIiKUjhLiKSghTuIiIpSOEuIpKCki7c8wsKq7oEEZFqL65wN7PBZrbazLLNbGQp7a41M2dmmYkrMdyUVTsratEiIinDN9zNrCbwPHAF0BW4wcy6RmnXCPgFMDfRRYqISNnE03PvC2Q759Y55/KAt4Gro7R7FHgCOJrA+kRE5ATEE+6tgE0h9zd704LMrBfQxjk3rrQFmdltZpZlZlm7du0qc7EiIhKfeMI92rdTuuBMsxrA08C9fgtyzr3onMt0zmWmp6fHX2VoMfrOXxERX/GE+2agTcj91sDWkPuNgG7ANDPLAc4BxlbUSVXnnH8jEZFqrDL6qPGE+zygk5m1N7PawHBgbNFM59wB51wz51yGcy4DmAMMc85lVUjFIiLiyzfcnXP5wJ3ARGAl8K5zbrmZjTazYRVdYCQNy4iI+EuLp5FzbgIwIWLaqBhtB5W/LBERKY+k+4SqiIj4S7pw16CMiIi/pAt3ERHxp3AXEUlBSRfuulhGRMRf8oW7Rt1FRHwlXbiLiIi/pAt3h75+QETET9KFu4iI+Eu6cJ+Xs6+qSxARqfaSLtxFRMSfwl1EpNJV/FV/CncRkRSkcBcRSUEKdxGRFKRwFxFJQQp3EZEUpHAXEUlBCncRkRSkcBcRSUEKdxGRFKRwFxFJQQp3EZEUpHAXEUlBSRfuNWvoZ/ZERPwkXbiLiIg/hbuISCWzShiASLpwd06/oSoi4ifpwl1ERPwlXbhbZRzPiIgkubjC3cwGm9lqM8s2s5FR5t9jZivMbImZTTazdokvNUDDMiIi/nzD3cxqAs8DVwBdgRvMrGtEs4VApnOuB/A+8ESiCy2iaBcR8RdPz70vkO2cW+ecywPeBq4ObeCcm+qcy/XuzgFaJ7ZMEREpi3jCvRWwKeT+Zm9aLCOAj8tTVGk0KiMi4i8tjjbRzmBGjVgz+yGQCVwQY/5twG0Abdu2jbNEEREpq3h67puBNiH3WwNbIxuZ2SXAA8Aw59yxaAtyzr3onMt0zmWmp6efSL0iIhKHeMJ9HtDJzNqbWW1gODA2tIGZ9QL+QSDYdya+TBERKQvfcHfO5QN3AhOBlcC7zrnlZjbazIZ5zf4ENATeM7NFZjY2xuJERKQSxDPmjnNuAjAhYtqokNuXJLguEREph6T7hKqIiPhTuIuIpKCkC/faNZOuZBGRSpd0SXlljxZVXYKISLlUxtcfJl2462f2RET8JV2469sHRCTZ6ZeYRERSkFXCwEzShbu+OExExF/ShbuG3EVE/CVduJ9xWqOqLkFEpNpLunC/ZUD7qi5BRKRcXCVcGpJ04V5D4zIikuQq49xh0oW7iEiyq4zrQhTuIiKVTD13EZGUpDF3EZGUo567iEgK0pi7iEgKcpXQdVe4i4hUMvXcRaRa6KJPhifUyfVrV/g6FO4i4qtry5OquoSU8ui3u1X4OhTuIiKVrEGdtApfh8JdRCQFJWW4j7qya1WXIPKNUhk/LpFKTm4Qe0y9Sf1alVJDUoa7VB992jWt6hKkkoy++syqLiEljKikb7ZVuFewx7/bvapLSKgerRuH3e/eqnGMltH9e0S/RJYjlcQMBnVuXtVlJL3v92vLzy/uVCnrUrjHoVur2FcK+IX3oDNS6w3RN+Pkcj1+YKdmfPnAxQmqpnro1bZJVZcQl5wxQzmnQ/lev0RoULtm3G1/cVHHCqyk2JDupyV0eT8bdHrU6XXT4v/by0vhHodGdWrR5uR6JabXMKidFv0pvKZXK3LGDOXUk+rGXO6a31+RsBpPRKsmJf+mEzHmO2U7OmneKPZzkozanlw/7rbTfjWo4grx/Pl7PRO2rLq1Atv3d3q1itmm/+mnlGmZr9x0dlzt3hjRl1/E0cv99lkty7T+aH7Yr125l1GkYZ00bj2vQ9R5LZtU3raflOH+rRbRe9K/vyb6taPl7VnVqAGf3nUB9w3uQsOQS5jObNmYq3q05BcXdWTpI5cFp0/71SDGxDEcU6tm+NNfr1bF7tUjn59aNct/kqx9swYM79s2bFrXGK/PN9UV3Yp7hVaOp3zWyItKnV90Ei/aCbuXfpQZWH8cJ0a/0zsQ5H+6tgerHr2CnDFD6d+xWcza68To4BSpnVaD527oFbzfonF8nYrzOqWTFvEe+fKBi1n/xyHU93r/Q3u04LFryj/0md6oTpnah753OqQ3CN5e/8chLPvd5eWuJxGSMtzPDekpPHldcS/l0q6nRm3//u39y7W+GmbUq12Tnw46nYl3nx+c3rppPdJq1uCey86gUd1anHFqI+648HQymjWgTsjh14iBxSdQinr6oW/48kqL89epWkb01C/sUvqQUYPaNcM+bPHmT0qOl387okeXM2Zomf620J1idfDLChgPff77vUudv+ChS4O3r89sE7PdaaUcBbY9uT49W0c///HxL88Lvjf8OjqN6xXvGCwizRuW8drs3w7pwsNXdWXJw5dxZsiHoNqeUr/EtjTpnvOZeNf5kYsA4K5Lil+T5o3qYmYseOhSVoy+nOe/3/uEOik925S9w3ff4C4APHr1mUy654Lg9Bdv7APARV2ahz1nNw/IKPM6Eikpwx3guRt6MfneCzivUzMAmjeqQ/NGdRnavUWJtjXL+dN8oY9v1aRe8DD83ss6h7WbePf5/PryLiUe/1DIpZtFS3r6+rOirivaePRDPpd+vnXbOaXOL9L/9FM4K2SjPqdD6YfThQ5uPKf4cLX/6c34fxeczgWd04PTovXm7riwI+N+PjCstwaBXlaotBpGo7qVc1lYvO6+tLN/ozisGH05r9/Slxd+2DvspyGj9ZxDL5t7/NoeYfOm3HsBb97aj5wxQ6Nux0XDFkW97WhCj3QHdmxWYn6T+rWCVz0VOsd3erUGIDPiSqimpVzeV/R3TP/1hcH7w3q24uYB7albqyanpzfklZsyGXvnACCwLeWMGcofrunOWz85h47NG3FGyFcchNZ81yWdueuSTvwlZHuqW6sm9WsHdjZ10moye+RFrH5sMDljhrJi9OUMPzt8J/nts1qG7WBGD4t+5c/p6Q3CeuKfhnTmbr+gA2v/MIQbz82g3SnFbTo2b8Tkey8osRN/+KozyRkzlDdG9A3uNDs2bxjr6Uu4uMLdzAab2WozyzazkVHm1zGzd7z5c80sI9GFRhrWsyWnpxc/UXW8scFnhp/FolHFPaGiDeLMlifR6AQ/FVYzIsFeuelsbuqfEbb+eP368jOA4iGZf9zYh3e8cL770k40b1SXN2/tx2VdT6V7q8aMurIrIwa25183nR31x8Fzxgzl7IyTWfxwcQ84tFcSeq6gTlrNsPHOQWek893erZk98iJ+0C98aAWgnnfoO/3XFzL3t4GdTnqjOrx2S98Sz+XVZ7UMniirUcPo1qoxg88M78H/MWRs/r3bz+Xz3wSC4KM7BvDkdT2ZdE/gjdQhvQFpNYx7L+1MxinRx7PjHWq7+5LO3DqwPYPOKN4h+Y3jTrzrfG7qnxE2rbQhs2jLq187jfM7pzO4W2CHdm2f1vz8oo4ldoaf3HVeiceGHo12SG9I/yiBHM1DV3bl/M7pnNuhGWv/MIT/3TmQF37YJ7yRt/7QE6sLH7qUV28ObBfOBU5654wZSkazBsTy9PU9g8M/XVqcxPRfX8iUey+g7Sn1yRkzlJwxQzmtcfiRxkVdTqVH6/DX7fv92oYdiRf5+Jfhz8tdl3Tmqp6xx9ZbNqkXPFquXzuNMd/twdzfXsxfv9+LP3+vJ88M7xX2OoUexQ7oeAoZzRpwfud0nryuJ6/d3Dc4r/OpxTscM4vZUTw9vWHw/RLpvE7pLBp1KZ/dfX6lXmDhm3ZmVhN4HrgU2AzMM7OxzrkVIc1GAPuccx3NbDjwOHB9RRQcqXmjOtx9SWeGeSdVatWsQZP6tcl68BLyC1xwAxv/i/PY8fVRrvrLTFo1rcfCjfu5oHM6n3+1K2x5T1/fk7vfWRw276Jvhb8gHZs35JEYe/5YerZpwuJN+7n1vA5hJ1su9wIwZ8zQ4LT+HZuVeENf2KU5F3ZpzsTl29my/0iJ5TeuV4vMdk3J2rCPB4Z8C+ccNWoY32pxEt0enhhsV9RLHNq9BXXSavKUd/Lt99d05z9zNzL87DbcMrA945dsC76Z2kYJ2JsHZPDclOzg2f9nh/cq0aZ2Wg3GfKc7jevVYvbaPWE7hLNDrrrp2aYJPds0IXvnQSCQP9l/GAJAgXM8M2lNiWW/8MM+vDtvE0999hUQ+GDb6HHFm+QjV3XlcF4Bd1xYfLVFxsjxQGDopXWTepwgT4cmAAAJIElEQVRUrxZ1a9Xgpn/NA+C6PoEe6xmnNeKRYWfy6uyc4GMzM5oyY81ubuqfETYdAm/sR68+k4c+Wg5E70UXBfamvblh07ucFuhN/uvmszmSVwAEdgQN69SM+hH1f4/ox9YDR/h46TZGXvEtpqzaCQR6sh3SG/L6LcXB1L11Y7pHDNV0ah4Iqxv6tuV7mW3o3bYpZkbDOmlc26c1N/SNPSwU6pperbmmV2sWbNxHj1aNS4yNn6jzOjVjxprdCVnWqSfV5coexTuEy888jTW/v4LcYwU0Djkv8Z9bA52r0OcOireHoT1aMH7JthLLH9ixGQeP5cdVi5nR6dRK/vI151yp/4BzgYkh9+8H7o9oMxE417udBuwGrLTl9unTx1WlwsJCV1BQ6B4bt9y1u2+cm7Bkq1ux9YBzzrntB464I3n5rt1949zp9493hYWF5V7fgSN5bvmWA+Vezv97Pcu1u2+ce2n6WvfuvI1h837y2jzX7r5xJdZTUFCYkL+hoh3Jy3f9/zjZTVm1Izgt91i+G/2/5e7DhZvdtNU73dhFW8Ies3Tzfrc/Ny94f9HGfe6JT1ZGXX67+8a5Ea9+WWL610fy3JtzN5R4jv5v/ibX7r5xrt1949yho8eD0//86erg9Hb3jQtO//34Fa7dfePc67PXx/wbDx497trdN84Nfma6m7lmV8x28Tp6PN8989lX7ujx/HIvKx6fr97pVm//usKWfyQv323cc7jClh/qjS9y3DsR76FojucXhL3+VQ3Icj657ZwLBHBpzOxaYLBz7lbv/o1AP+fcnSFtlnltNnv313ptYu6CMzMzXVZW1gntkCrL1v1HaFAnLewkU1XLzctn1faD9G5b8pOhB3KP8+mK7VxXykm5b7Kvjx6nXq2aJa5SKs3KbV/TsnG9sJ5eYaFjb24ezRqGX2Fx6Fg+f5m8hnsu6xx2Qj3S8YJC0mpYiROWIvEws/nOuUy/dvEMQkfbAiP3CPG0wcxuA24DaNu25BhvdRN5dUl1UL92WtRgB2hcv5aCvRQnncDJ22iX3daoYSWCHQJXk9w/5Fu+yyzLzkXkRMWzlW0GQhOjNbA1VhszSwMaA3sjF+Sce9E5l+mcy0xPT4+cLSIiCRJPuM8DOplZezOrDQwHxka0GQv82Lt9LTDF+Y33iIhIhfEdlnHO5ZvZnQROmtYEXnHOLTez0QQG9scC/wTeMLNsAj324RVZtIiIlC6uC7+dcxOACRHTRoXcPgpcl9jSRETkROnMjohIClK4i4ikIIW7iEgKUriLiKQg30+oVtiKzXYBG07w4c0IfMVBdaO64lcdawLVVRbVsSZI/braOed8PyhUZeFeHmaWFc/Hbyub6opfdawJVFdZVMeaQHUV0bCMiEgKUriLiKSgZA33F6u6gBhUV/yqY02gusqiOtYEqgtI0jF3EREpXbL23EVEpBRJF+5+v+daAet7xcx2ej9IUjTtZDP7zMzWeP839aabmT3n1bbEzHqHPObHXvs1ZvbjaOsqQ01tzGyqma00s+Vm9stqUlddM/vSzBZ7df3Om97e+23dNd5v7db2psf87V0zu9+bvtrMLi9PXd7yaprZQjMbV41qyjGzpWa2yMyyvGlV/Ro2MbP3zWyVt32dWw1qOsN7jor+fW1md1V1Xd7y7va29WVm9pb3HqjybQvw/5m96vSPwLdSrgU6ALWBxUDXCl7n+UBvYFnItCeAkd7tkcDj3u0hwMcEfrzkHGCuN/1kYJ33f1PvdtNy1NQC6O3dbgR8BXStBnUZ0NC7XQuY663vXWC4N/0F4Kfe7Z8BL3i3hwPveLe7eq9tHaC995rXLOfreA/wJjDOu18dasoBmkVMq+rX8DXgVu92baBJVdcUUV9NYDvQrqrrAloB64F6IdvUTdVh23LOJV24+/6eawWtN4PwcF8NtPButwBWe7f/AdwQ2Q64AfhHyPSwdgmo7yMCP2BebeoC6gMLgH4EPriRFvkaEuO3dyNf19B2J1hLa2AycBEwzltHldbkLSOHkuFeZa8hcBKBsLLqUlOUGi8DZlWHugiE+yYCO4s0b9u6vDpsW865pBuWKXoyi2z2plW2U51z2wC8/5t702PVV2F1e4d2vQj0kqu8Lm/4YxGwE/iMQC9kv3Ou6GfiQ9cRXL83/wBwSgXU9QzwG6DQu39KNagJAj9F+amZzbfAT1BC1b6GHYBdwL+8IayXzaxBFdcUaTjwlne7Sutyzm0BngQ2AtsIbCvzqR7bVtKFe1y/1VqFYtVXIXWbWUPg/4C7nHNfV4e6nHMFzrmzCPSW+wLRflS0aB0VXpeZXQnsdM7ND51clTWFGOCc6w1cAdxhZueX0rYy6kojMAT5d+dcL+AwgeGOqqypeGWBsethwHt+TSujLm+M/2oCQyktgQYEXstY66jU5yvZwj2e33OtDDvMrAWA9/9Ob3qs+hJet5nVIhDs/3HOfVBd6irinNsPTCMw5tnEAr+tG7mOWL+9m8i6BgDDzCwHeJvA0MwzVVwTAM65rd7/O4H/EtgZVuVruBnY7Jyb691/n0DYV5ft6gpggXNuh3e/quu6BFjvnNvlnDsOfAD0pxpsW5B84R7P77lWhtDfjP0xgTHvouk/8s7WnwMc8A4XJwKXmVlTb29/mTfthJiZEfhpw5XOuT9Xo7rSzayJd7segY1/JTCVwG/rRqurqN7Q394dCwz3ri5oD3QCvjyRmpxz9zvnWjvnMghsL1Occz+oypoAzKyBmTUquk3guV9GFb6GzrntwCYzO8ObdDGwoiprinADxUMyReuvyro2AueYWX3vPVn0fFXpthWUiJMclfmPwJnwrwiM5T5QCet7i8B42nECe9gRBMbJJgNrvP9P9toa8LxX21IgM2Q5twDZ3r+by1nTQAKHbUuARd6/IdWgrh7AQq+uZcAob3oHb2PNJnBIXcebXte7n+3N7xCyrAe8elcDVyTotRxE8dUyVVqTt/7F3r/lRdtyNXgNzwKyvNfwQwJXlVRpTd7y6gN7gMYh06pDXb8DVnnb+xsErnipFtu7PqEqIpKCkm1YRkRE4qBwFxFJQQp3EZEUpHAXEUlBCncRkRSkcBcRSUEKdxGRFKRwFxFJQf8fEfIeAj30SM4AAAAASUVORK5CYII=\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results[test] = probas_\n", + "plt.plot(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4980 - acc: 0.7615\n", + "Epoch 2/50\n", + "7274/7274 [==============================] - 0s 13us/step - loss: 0.5028 - acc: 0.7563\n", + "Epoch 3/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4927 - acc: 0.7708\n", + "Epoch 4/50\n", + "7274/7274 [==============================] - 0s 13us/step - loss: 0.4966 - acc: 0.7615\n", + "Epoch 5/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4958 - acc: 0.7656\n", + "Epoch 6/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4982 - acc: 0.7641\n", + "Epoch 7/50\n", + "7274/7274 [==============================] - 0s 13us/step - loss: 0.4993 - acc: 0.7633\n", + "Epoch 8/50\n", + "7274/7274 [==============================] - 0s 13us/step - loss: 0.4955 - acc: 0.7649\n", + "Epoch 9/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4935 - acc: 0.7677\n", + "Epoch 10/50\n", + "7274/7274 [==============================] - 0s 15us/step - loss: 0.4981 - acc: 0.7612\n", + "Epoch 11/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4948 - acc: 0.7612\n", + "Epoch 12/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4928 - acc: 0.7692\n", + "Epoch 13/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4886 - acc: 0.7699\n", + "Epoch 14/50\n", + "7274/7274 [==============================] - 0s 10us/step - loss: 0.4902 - acc: 0.7706\n", + "Epoch 15/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4902 - acc: 0.7695\n", + "Epoch 16/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4957 - acc: 0.7684\n", + "Epoch 17/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4889 - acc: 0.7707\n", + "Epoch 18/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4906 - acc: 0.7685\n", + "Epoch 19/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4858 - acc: 0.7739\n", + "Epoch 20/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4889 - acc: 0.7707\n", + "Epoch 21/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4870 - acc: 0.7715\n", + "Epoch 22/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4907 - acc: 0.7681\n", + "Epoch 23/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4879 - 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"text": [ + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4832 - acc: 0.7783\n", + "Epoch 42/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4842 - acc: 0.7744\n", + "Epoch 43/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4913 - acc: 0.7637\n", + "Epoch 44/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4837 - acc: 0.7753\n", + "Epoch 45/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4817 - acc: 0.7770\n", + "Epoch 46/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4803 - acc: 0.7772\n", + "Epoch 47/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4826 - acc: 0.7788\n", + "Epoch 48/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4832 - acc: 0.7759\n", + "Epoch 49/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4802 - acc: 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0.7748\n", + "Epoch 39/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4906 - acc: 0.7684\n", + "Epoch 40/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4837 - acc: 0.7742\n", + "Epoch 41/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4824 - acc: 0.7764\n", + "Epoch 42/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4857 - acc: 0.7711\n", + "Epoch 43/50\n", + "7275/7275 [==============================] - 0s 10us/step - loss: 0.4851 - acc: 0.7743\n", + "Epoch 44/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4820 - acc: 0.7748\n", + "Epoch 45/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4840 - acc: 0.7769\n", + "Epoch 46/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4797 - acc: 0.7786\n", + "Epoch 47/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4829 - acc: 0.7759\n", + "Epoch 48/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4823 - acc: 0.7802\n", + "Epoch 49/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4825 - acc: 0.7761\n", + "Epoch 50/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4786 - acc: 0.7802\n", + " 0.82888513099527\n" + ] + }, + { + "data": { + "image/png": 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1jcutcLlNXITRNKuhR7Mze81rYBoGIdxWRUGamw9TrMWwBSEBhSvOjwgkxrmjmewRLXToOuzYAdXV1r7fHzXCPzRHbEiqUuoypVSmUsqjlOqqlHpZKfW8bRBQFjcrpXorpQbZi4w7OByzuFwuhg4dysCBA5k8eTKVlZXRc2vWrGH8+PFkZ2fTt29f/vjHPzbKsOfOnUtOTg4nnngi/fr14ze/+U0z/0OhEBMmTGDo0KHMfvNNzEgEo64Oo6YWo3bvNvb00/lm0SLM+nors7G31159lVtvvRVxu9H8vugmvjh+fd+9nDB8OMNOO43cvDzE5Wq0oQn1wTrGjBlDOFyProeIRII89thjxMcnUFi4k7LdQUp3hHjiyVe5+pqpbNxksm6jm81b4zn77PNZ9PkGqioT2Vmmc99vb2XCWSdy7uShXHnleHJXf4vmAXecSVyCjj85QlJ6hPSOOhmdInToFKFX5wjZWWH6donQq1OYnh3D9EqvomdaFVmJVfz5wRsZd9pAzj5zJJvXLiPOY+B2mWiGhls8pPgTWPzJXCaNPZ1J407nusun4DMjdE1P4W8PP8SE0SP5ydjTuPaX16DC9ST7vaxZs4Zf/OIXR84gKAVVVbBli2UQNA06doQePcC7/w7lI4Uzo9nB4TDh9/vJzc0lLy+P9PR0nnnmGQACgQDnn38+9957Lxs3bmTlypV8+eWXPPvsswDk5eVxyy238MYbb7Bu3Try8vLo1atX1F9lmpjhMMu+/ZZIOMyyxUv42eTJoOuIaHbm7o9uomlocXFoiYmNjmter20Q/I0y/P9+8gmbN29m06ZNvPDCC9x4442YpoGuB4lEggQClVRV1TD97y8x/szzKCoStmyBjRtdzJz5Dv375zBz5sfs3OmjotxHMODG0DUM3YNSGh4XuDRI8Jl0SDV57OHr6dY5hbwVeaz6bhkzXngeL6V0aR+iY3qElMQ6kvz1xHsDeKUOD/W4JUBQBagzQgRDVQRDlQRDlQSMMLXiYs78z9icX8CWrVt5+ZWXufv/7iY9PdXeUkhPSyHeI9x2220sWLCAVatWMXjwYJ5++mkAzjrrLPLy8li1ahXZ2dk88sgjAAwaNIiioiK2b99+ZF6aHTuguNiqFSQkWE1GGRnfq7/mcOEYBQeHI8Do0aMpLi4G4K233uLUU09l4sSJAMTHx/P000/z6KOPAvDYY4/x29/+ln79+gHgdru56aabADAjEVQwyM7iYq665hpyV65kxCmj2bZjBwu+/JIRp57C4GHDuPb66wnrulWqF7EyfE3j1VdfJTs7m7Fjx7J48WKUUoQiIYLhIIFwgLpgLe/+820uvuR89tTupPcJfdhdXs6y5ZvZVqCzaYuweUsChUUJvD37XUaOvIi6Og/hsIuCgq3U19dx26+nMffj2cQlKBLSFJ4EhcdnktIhTPvMEF06hfB4TJKTDIrLN7A891vunfY7dK8i6Dbp3q8X5519Gl5qiaOWJLdOuwSN9oku2jVsCRrtEjQy4oWUpHhS2mfaWyfS01KYP/9Trr32l4gIo0aNorKyktLSxl2USimUUtTV1aGUorq6ms6dOwMwceJE3G6rNX3UqFEUFRVFr5s8eTKzZ88+Mi9KUpLVRNS5M3Tr1ma1g1iOuUV2HBxaw6drdx52Pyf079gqd4ZhMH/+fK699lrAajoaMWJEIze9unentraWyt27yVu9mtunTsUMhxu5UeEwmCbi8dApK4sZL7/M448/zn/+8x+CwSBnnHkm8+bNIzs7m6uvvppnnnmG2267DaUUYT3Mlvwt/O53v+OzLz4jMTmRCyb9lIEDB7O7IgCmZo23111s21rG+PEnUrq9PSC0b9edrVuqGDiwL0opNBSmGaK4eBt9+3UlLs7A7VG89fZbXDzlYiacO5L779+IVl9AekI7krV64rUIGa6qaPu9hoFX6slfu40hg/qTLIFGHQWiacSlNl4UbMqUKWzYsKFZ+t5xxx1cddVVjY4VFxeTlbV3MGPXrl0pLi4mMzMzeszj8fDcc88xaNAgEhIS6Nu3b7Q2F8srr7zClClTovs5OTk8+uij3H333ft44gdBKAT19ZCWZu0nJkKfPm3Sd7AvHKPgcFzS2gz8cBIIBBg6dCj5+fmMGD6c8aNGY9TUYIRCqFAIo6Zmr2O7P0H29sg2G78vLhcSH49o1ogb0zRRSqHrOnl5eWR1z6J9VnsqA5VcdMlFzHjxFS698jrCEZPd5QarVi1jxIgx6IHuVNQKZ467lPyCTezZldBwB8BquTB1QZkKj9tEE5NEf4T0xFp0r8KtGZTsLCYlNYkMX4U1VtOA9999mzdem0Ga1HHheT/hw7nvMvWWG/EneHD7/cRndIrGRXN78aW0w1sbwuWJw5fa4YDp+fbbb7c67VvqUG/aDxCJRHjuuedYsWIFvXr14tZbb+WRRx7h/vvvj7r505/+hNvt5oorroge69ChAyUlhzhaXikoL4eyMut/XBzEx1vnjiKDAI5RcHA4bDT0KVRVVXHeuefy7EsvcduddzBwyBC++OILtISEqNutW7eSmJhISvv2DBg4kOWrVzMkJ6dR5iYimEoRCQeJGBHqQ/WEdZ0dZbXs2hVCD2mUFyWi6xrlOxIIBdxU7IhHj2gEajyEAhrK1DBNDbCGY7pdJskJOi6X9d/tVvTu2REjvIlenQeh6xF2lRWS2SsFMz6EaB6SPS7cnTqhRyKkdOiEiLB69Wq2btvGxZf+HIBwOEyvXr349V330a5TFnv2LG6UNhUVFbRr147U1FRWrlyJaZpo2v5brw+mptC1a1cKC/cKJBQVFUWbhhrIzc0FoHfv3gBccskl0SY8gH/84x/85z//Yf78+Y0MSjAYxH8QQ0KbEQxCSYn1C5CSclQ0E+0Lp0/BweEQaOgENkMhAMxQiCSfjycfeYQnpv+NiK5z+RVXsHjJEj78+CMCkSB7dldwyy238uupt1FfXcstv7qJh//0J1atyCVYW09tZS2PPPwXyncFKS4KUrzdpKTAS9kOH+GAm8qyBDp1GExh0XY2b96CMuE//3mDkSefSrwvbGX8iUHGnz6Q5csXkuDZTqe0XSxY8A5+v0FqapDE+CC+uDAmYSacNZE3Zs2mNmKyMHc1Samp9O7Vl8yMDLqkp5CQnEL7Du0xDINwOIyIMGvWLKZNm0Z+fj75+fmUlJRQXFxMQUEBJ510EkuWLGHHjh0ALF26lFAoRFZWFr179yYnJ4cHH3wwagA3bdrEnDlzmqXt22+/TW5ubrOtqUEAOP/883n99ddRSvH111+TkpLSqOkIoEuXLqxduzY6sWvevHmceOKJAPz3v//lz3/+Mx988AHxDSV4m40bNzJw4MCDfzlME3btgm3bLIPg8Vj9Bl26WFOij1KO3pA5OBylmOEwKhKx2l3A6ti1dWYM08AwdQYMGsLAAQN5/dXXmDJlCm/MnMk999zDHVNvxzAMplx+KdffdAMBXejWdxj3/+6v/Pzq6wgE6gGN08f8hB1lVmlSGgQWFJbsQbxBSqrG36e/wL33XY5h6IwYMYK77roBXQ+iuRRur06nru256667OfMnZ9GxYycGDh6GYRpEtDhwCeISdOAn/3MxCxYvYuSpI4n3x/OP114lJb656NrEiRNZvHgxEyZMYPbs2cydO7fR+QsvvJDZs2dzzz338Le//Y1JkyZhmiaJiYnMmjUrWjOYMWMGd955J3369CE+Pp6MjAz+8pe/HNIzmTRpEh999FHUz1dffTV6bujQoeTm5tK5c2cefPBBxowZg8fjoXv37rz22msA3HLLLYRCIc466yzA6mx+/vnnAViwYAHnnnvuwQdq1y6oqLD+p6dD+/ZHXVNRS8jhUPr7IcnJyVFLlzpTGhwas27dumip71BQpomKRKxO3n18G0oXxO0GlwsloJuW3o5hGuimjoaGJhrKa82s0iOCHnahQhEMQzBMIRzRCIZcllyDPdnLmqul0ATcLgNNM/B4DDweHc2r4fEImgsQN4gLMxy0ZgTHhE2ApKQENE2zJRX2TryqMUyaIiIku1uXUa1YsYInnniCmTNntj5Bj3FCoVB05Jb7YEv3ug6Fhda8gya1jyNNS9+DiCxTSuUc6FqnpuDwo0RZugkovbGAmgqFrJK/12uV/u1MVQUNGrJf8YDhUUSMCCE9BAIe8YAL0H0Egx5CIQiFhHBYME1QpgHENZpJrJTC7TbweiO4RMcTZ+LxNG5r93k0UNrejF8pUBEggrg1vH4/ItLICIgI1bqtGxQ1bOqgDEBLDBs2jDPOOAPDMHAdAyXew8H27dt59NFHW2cQamuhstJqHhKxmoh69GjTOQffB8coOPzoUEph1tZaGabLZZX6bcTnQ/N6MYM6KmRG3SsUutskYkQwlIERNHBpLuJccXg0D7W1QmWlRl0dWFoKezeXZuL1RvB4rP+ay8TlMonz6piioXAhaCQlJUbH0gPRzD4atpjMv2EfsAyA2Vg6VERI8Rz+z/uXv/zlYffzaKZv37707dt3/44MA3butAwCWJPQGoacHmMGARyj4HCcY4ZCLTcDKYUrObm5+4COXh9GN3V0t4mpLMNgKhMzYuLW3Hg0Dx6smbuVVTqBoKXSqZQlnpbgDxHnMfB6DOI8JprXZ9UmfI1HsNSFDUQpEjxWxt8wIsflcjVr/omW/PfRBHQkDIBDK6iutmYl67plANq3h9TUtg7VIeG8SQ7HB0pZAnBNmoPMcBiJ89pOFBG7/R+PIHrQymhDZrSEbiqTsNvS9vcqLy5xobD6BcJBF1V1inBQETFcWLOvNATwuHWSEsOkt/ejaS0PX2yoAdSG9i74kuRz42pQHo1t+mmS+TsZ/1FGUwG7+HjIzLTmHxzjOG+ZwzGL0nWrXwCrRmDW14PbTUSMaOVAdxmIvfCKEoWmabjFjalMjIjVT6AZYHqxdITQiBcvRsRFXbVQV6cRDJj2CluWxKYIiCb44gy8Xh2/T8cbp9BcHgK6Ya3o1QINmb7b5SLR527UFNQoXko5BuBop6Zmr4Bdhw5Wc9Ex2FTUEs6b53BMogyD2sWLEZf9Cicl4kpOtmoDkVriXFaJzaXiECXRGcGAZQgi4MLuLHUBLo1QSKjco1NXp6HrDR+4DkpweXTivBF8PoU3LoLX47IlHNzE+ayRJbUhS6o62e9pNvIndr8p0aYhG2ddiKMU07SMAFhNROGwZQyO4olo3wdn8prDMYdRWUnt54sQlwv/qaORUcPA6yFshAnaTUJiCkpXmPUGZkBHhQy0CNFND0eoDkTYXWlQvAu2boGCfKiqcqMbLlwuIcEfISM1SJeuIbKyDLpm+WjX3k9ycjI+fwI+XwJxvjhqQzrVQZ32qUmcOeYUhg0bxoUXXkhNTU20f2Dt2rWMHz+ePtnZ9O7bl99O+z2V4QhVEasW8+Wn8zhz9ChGDR7EyQMHHFA6e38SEOPGjaOlYduvvfYat9xyS7Pj69evZ/To0cTFxfH444/v01+lFOPHj6e6ockEeO+99xAR1q9fHz22cOFCzjvvvEbXXnPNNbz77ruAJTdx77330rdvXwYOHMjJJ5/cbM7D9+GRRx6hT58+nHDCCXz88cctupk/fz7Dhw9n6NChnHbaaWzevLnR+XfffRcRiabf6tWrueaaa6z5Bps3W4YArFpBx47HnUEAxyg4HCMo08QMBDAqKwlu2Ii7fXsSTjmFwppCVuxcQdgIEwgHiEQiuEMuzICOhBWaCBHRCERcVAU0duyB/FJha4mP0p0+yiu91Na5iOiC5lIkJel07BCka1aQDu3qyOjoJyExDq93b7+EUoqaoGUIaoI6mqaRnujbr3T2eeefz62/uYvla9exeuVKln/zNW++9CIpHjfb16/br3R2AytWrCASiZCbm9tIsO1QSU9PZ/r06S0aolg++ugjhgwZQnJMB/2sWbM47bTTDkpF9IEHHqC0tJS8vDzy8vL497//TU2sLtT3YO3atcyePZs1a9bw3//+l5tuugnDMJq5u/HGG3nzzTfJzc3l8ssv56GHHoqeq6mpYfr06YwcOTJ6bFB2NkWbN7N9+XKrHyHGIB6vOEbB4ahHr6qibME8yr74jB3fLWZL9Va2pIfI3bV4GiLiAAAgAElEQVSKjaUbCRcqNMOFN+jBE/YgpkZ9xMWuShfbSjzkF3goLnWzq8xNVaWbcEhDEHw+IS1N6NRJ6NHDEqvM7OwiLT6MX0J43a5os1ONbQBqwyZ1EYXb5SItIY70JD+pCb7o0NGqiE5VRGfoySezrbCQqojOjJkzGTX6FC6c9BOS3a6Dks5uYNeuXfz85z8nNzeXoUOHsmXLFubPn8+wYcMYNGgQv/zlLwnZUhuxxEpnL1mypMX07dChAyeddBIej6fF8w28+eabXHDBBdH92tpalixZwssvv9xqo1BfX89LL73E3//+d+LsTtmOHTtyySWXtOr6fTFnzhwuvfRS4uLi6NmzJ3369OHbb79t5k5EojWdqqqqRvpIDzzwAHfffTc+n88asbZ7N2zdyuTTT2f2xx9D167Qrt0hhfNYwOlTcDiqUUqx++tFbDPL8PTvh6sYND0D90bBCAXpqFLQ6uNQPSFiaNQE3dTUavgLreYIn1L4ULhc1vwAt8vE5xfi4lxoYQ1i1Ko1PQBASDfRNR+wtz/Arwn+gee12N4f2yeQ4nFjGAZfLlzItddeS4rHzbb16xl1UuOJpL1796a2tpbq6mry8vK4884795sOHTp0YMaMGY2ks8eNG8f8+fPJzs7mqquu4rnnnuPXv/519JrS0lIefPBBli1bRkpKCmeccQbDhg07+Idgs2TJEl544YXo/vvvv88555xDdnY26enpLF++nOHDh+/Xj82bN9OtW7dGtY19cfvtt7NgwYJmxy+99FLuvffeRseKi4sZNWpUdL9BOrspM2bMYNKkSfj9VjPg119/DVi1sMLCQs477zwef+wxa+EbW8AwZ9QoHn3pJe5uRZiPBxyj4HDUoldUULt9G7uKq4jvcyIdijsQCAcxunjxeDxEduhszxeWrkpkXLvduHbubeIJZJ1NfLyOz1tDvN+Fx+dr5LeIGwVo4VoaJn2ZQLVprc2b7PeiadoBlTyrdauJIsXjbiydPWJEVEdHKbXPzuPv26m8YcMGevbsSXZ2NkB0PYVYo/DNN98wbtw42re31imYMmUKGzdu/F73A0vpNCkpKbo/a9as6P0uvfRSZs2axfDhww9bXJ988slWu22NdHaDnx999BEjR47kL3/5C3fccQcvvvgit99+e1QHCYBIxBKwy8ykg8dDSZMFe45nHKPgcFQQLiwksqeCunAdoCisLcIdiBCscFGX5CMtJZ5qr8LrSaS6ws3nX/j49/sRSssSEYFTx+8GZRDvj5DgryXBFwJMECHOlQCRQKP7KQClMAHTk0hd2HLrdmsk+70x8wX05oGNIVY6opF09nnn8cwzzzB16lQGDBjAokWLGl3XIJ2dlJTEgAEDWLZsGUOGDGl1erVWs+xwjmRyu93RCXbl5eV89tln5OXlISIYhoGI8Nhjj5GRkcGePXsaXdsgnd2nTx+2b99OTU1NIwPTEgdTU2iNdHZZWRkrV66M9hlMmTKFc845h5qaGvLy8hg3bhwAO3bs4PypU/ng/ffJ6dv30KWzjzGcPgWHNqM+Uk9FsILNezazesMXrApsYb2njEJfAOpS8Os9SUjvgaR3YMOKKl5/JcTNN3m47Ioknn9aUVLkJlH2cPqJK0hOqKN393I6d6wiJVnHFZ+CO6EDnoROGJ5EdHdCo830JIIvBVd8GmFTw+Nxk5HkJyU+LmoQwKoB7G9rSUsoJSWF6dOn8/jjjxOJRLjiiitYvHgxn376KWB1PE+dOjW6ktddd93Fww8/HC3Fm6bJE088sd+069evH/n5+dHRMzNnzmTs2LGN3IwcOZKFCxdSXl5OJBLhnXfeOaTndcIJJ7B161bAGqVz1VVXUVBQQH5+PoWFhfTs2ZPFixfTt29fSkpKWLduHQAFBQWsXLmSoUOHEh8fz7XXXsvUqVMJ2yN5SktLeeONN5rd78knn2xROrupQQBLOnv27NmEQiG2bdvGpk2bOPnkkxu5SUtLo6qqKprO8+bN48R+/UgJBNi9aBH5q1aRn5/PqFGj+ODf/ybHNh7fWzr7GMWpKTj8oNRH6qnX6wHYVrUNbU8N7pIykkIu4lw9idS5ECWUBHdSliDk5qax+PNk8gvSMY0QSoHPYzC83yZOPr2eQYM306FnOqFQT8SbsvdGSqgNmYCOaIImWnRpSAREib0cpIloQpJvbydrg0E4VPG4IUOGMHv2bK688krmzJnDrbfeys0334xhGFx55ZXR4aGDBw/mqaee4rLLLqO+vh4ROaBUs8/n49VXX+VnP/sZuq5z0kkn8atf/aqRm8zMTKZNm8bo0aPJzMxk+PDhLY7I2bFjBzk5OVRXV6NpGk899RRr165t1u5/7rnnsnDhQvr06cOsWbOaZc4XX3wxb731FqeffjpvvPEGv/jFLwgGg3g8HmbMmEFKivV8HnroIe6//3769++Pz+cjISGBP/zhDwedxrEMGDCASy65hP79++N2u3nmmWeion2TJk1ixowZdO7cmZdeeomLL74YTdNIS07mlWnTrOGmIlaTUQt8b+nsYxRHOtvhiGHU1lrrDsSQtzsPU5l4qgNQVU16SSKGx0ckPoENewLk16VTUJDAtm3xbNqYTiQMhhHB5aplUP+dTJhkcGr3RXh9lejtB+NypeLxdKSqyiQ7OxulFPURExENl6aRZE8kaw0NHcaHqiZ6vFJaWspVV13FvHnz2jooh4ZhWBIVVVXWvs8HnTtbv004JOnsNsSRznY4ajCqqlD2zOFAbi6ulL3iYBXBCrZv30CGtx2hiKIejS+3JbGmLJO1a5PZXuDfOywyUo9QQ99umxk1fAWnDCkmu3c365yWTKjL2XglCV3XCYUiKBWwjIGm4XZ7SPY3H17ZdOZwUxx9of2TmZnJ9ddfT3V1datGDx2VBIOwfXtjAbuMjH1KVByUdPZxwo8npg5HnNCmTejl5Yg9/rwuyYuc0Jnt27ZTF6yjOLiDhA6dMT2DWPi+nwVfJbOr3E1ID1qLxnvC9Om2h57dauiV9h29c2rJyBA69x2D252BgdXeHgqFUCHFt4XluDwe4v3xdHGHcLvd0WaglgyAk+kfOoc6n6DN8XotqYpWCti1Sjr7OMP5QhwOGWWahLdsIVxURPywYWzYWUJ1sJqC6iL8S8sJ62H8yUns2HwCuV90I/fbeExDodxCSsJuTh+8kzMmJXFy5mI0rRJd1WOKjtZlEh5PF3RdJxAIoJRiWXEAr89PXFwcmZlJqPZ+dKVgRx2mS6KyEY4BcACsSWjV1ZCYaC2FqWnQvbu1AI6jMdUizlfj8L0xw2FC69ahV1SAgnBWe5ZVr2BH6U4SMhJIjm9P5eZsFn1cz/LVqVTVmIhh4tZqGTakgHFjCjjxhAqykmtRRgRDCTVpA9EkCU1Lw6XHYxhB4uLiSExMJCEhgW3hSs7q3ykahk92VzGxXQrrykocI+DQmHAYSkuhrs4SrsvMtI4fYOb2jx3nK3L43kSKi1mzZQM16fHspoZIYSmiC+laB1Z8NIj3/pVCfR2E6+txeb1061DOxJGKk84I08O3HV9cPYbuQUXaE+k+Fl9cHKk+H16vF5fLhdu9d62BBgThs/Jqq3YAuJ3SnkNTlII9e2DXLkvZ1OX6wddIPpZxjIJDq9FNnZ31O1mxZgX6lnxcEYNwagIZnTuQGU4l3ZPOzpIUpk9PY+tWN4au061zLf37ruKMUfX0zlLEdRtqTRYtreYFGYWW4MXj9iB7BEtzIrzfMLg0YRh+JrZL2a87hx8poZBVO6i3hj2TnAydOlnNRQ6twpm85rBP9gT3UFhdSGF1IR988wHvf/U+X638ikRvIqdk9OLkMy5gwAmn0FXLoq64Ly89n8Udd7Rj5UYD3beDCZfP45zrF9Br4h52Z3Xiq/TefFO0gk/yV/Ge0ZHkpCTuP7kv9wzvwd3Durdqu3NIN8ZnHJ0jX1wuF0OHDmXgwIFMnjyZyoY1e4E1a9Ywfvx4srOz6du3L3/84x8bdYTPnTuXnJwcTjzxRPr16/eDSme/+eabDB48mMGDB3PKKaewcuXKFv096qWzH3qIPn37csIZZ/DxV19BVpYlYhdjEPYlnf3aa6/Rvn17hg4dytChQ5kxYwZgzYI+55xzDjlsxxKO+XTYJwXVBXg0D+WF5aR6UxkxbARiCHW1dVSum4cZCLN0aXtmz06kcLPVjKNH6hl82lamnrSJrBP6QY8ERIT08uUkiJf1ZoQ9Pccy7oQObRy7w0+DzAXs1SL67W9/SyAQ4Pzzz+e5555j4sSJ1NfXc/HFF/Pss89y8803k5eXxy233MKHH35Iv3790HWdF198sZn/sdLZh5OePXvy+eefk5aWxty5c7nhhhv45ptvmrk7kHT2tGnTWnW/WOnsuLg4du7cyeeff35IcVi7di2z33mHNQsXUrJrFxOuuIKN11xD09kmN954I3PmzOHEE0/k2Wef5aGHHopqHk2ZMoWnn366kfv27duTmZnJkiVLOPXUUw8pjMcKR7SmICLniMgGEdksIs3mpotINxFZICIrRGSViEw6kuFxaB0RM8KKXSuoDFWSlZxFx/iOjOw/kvId5ZQUl1C1ajU79qRxy4Od+N2jyazN19B99fQ7q4BLpi3n/MtW0b6jSbxrBVn1edTlf0tBrfCFezR7Oh+fBqEpo0ePjqp0vvXWW5x66qlMnDgR4KiTzj7llFNIS0sDYNSoURQVFbXo7qiTzjZNq98gENgrnd2zJz1Hjfpe0tn74qc//SlvvvnmwYfvGOWI1RRExAU8A5wFFAHficgHSqm1Mc7uB/6fUuo5EekPfAT0OFJhcmgdS4qXoInG8I7DSfIksbluM9u3b8ddXc3HGwy++LgzS5d3Q+GicyrcMPk7hiasp8sIP9+uL0eVV7K6w4Wkt0/H7XLjzpIf3BAsLFx42P0clzWuVe4Mw2D+/Plce+21gNV0NGLEiEZujlbp7Jdffpmf/OQnLZ47qqSzp06FkhJrhFFtLcVFRYwaPTo6zPRgpbMB/vnPf7Jo0SKys7N58sknycrKAiAnJ4f777//gOE9XjiSzUcnA5uVUlsBRGQ2cAEQaxQU0PB2pAAlRzA8DgfAVCZl9WUAZAYyKdpcRFVVFaFQiM/zE5n3jzQ2bOiAL6QTJxEmDFrL2Tm5JLgiFGhdWV6QiS/xO04ZeT5duvSPas+0Ba3NwA8nx7p09oIFC3j55ZdZvHhxi+ePCulsw7BqB/n51n5cHHTqREvz1FsrnT1jxgwmT57MZZddRlxcHM8//zxXX301n332GWAZ5JKSH0/WdCSNQhegMGa/CBjZxM004BMRuRVIACa05JGI3ADcANCtW7fDHlAHqA5Xs2LnChSKLoldCOwJkJiYiNvt4/d/q2XJ3HTidSExTue0k8r55aXrqGM3RWYOaUku0tQuOiVsomO3HmRlDflRLj5/LEtnr1q1iuuuu465c+eSkZHRops2l842TYhEuPScc7j3hhusVdAyMkDTDkk6G2gU5+uvv5577rknuv9jk86Orjl7uDfgZ8CMmP0rgb83cXMHcKf9fzRWLULbn78jRoxQDoePikCF2lixUS3YvkDl7c5TpmmqpUuXqoULF6rFi4vUpAlFKrP7bpXdvUpdd2WxevnD9ertL9aqz999Qb353gr15fyFasumz1VxyX9Vbe0WZZp6m8Rj7dq1bXLfWBISEqL/ly9frrKyslQ4HFb19fWqZ8+eat68eUopperr69W5556rpk+frpRSauXKlap3795qw4YNSimlDMNQf/3rX5v5v2DBAnXuuecqpZQKBAIqKytLbdq0SSml1NVXX62eeuoppZRSY8eOVd99950qKSlR3bp1U7t371bhcFiddtpp6uabb27mb0FBgerdu7dasmTJfuM3cuTI6P2ef/55dcMNNzQ6P2bMGLVo0SIVDAZVjx49os8kPz9fdevWTVVWViqllLrrrrvUNddco0KhkFJKqZKSEjVz5sz93lvpulLr1yu1Zo1SW7cqFQg0Op2Xl6cGDx6sgsGg2rp1q+rZs6fS9cbvYiQSURkZGdF0njFjhrrooouiYWjgX//6lxo5cmR0f+nSperss8/ef/iOMlr6HoClqhV595GsKRQBWTH7XWnePHQtcA6AUuorEfEB7YBdRzBcP3qUUkTMCLWRWlaVrSI1LpV+6f3oGN+RsrIyKiurWb9+CE/+3UUkIqQlB3nsxj2MGr4MTB2UojJZw+1aSdfe3Ulr1xm3KwGf78Cddj8WjiXp7D/84Q+Ul5dHO7fdbneLQ1p/cOnshtqQiDUBrVMnS8guPb2ZRMX3ks5OS+OVV14BYPr06XzwwQe43W7S09MbrcLmSGcfLo9F3MBG4EygGPgOuFwptSbGzVzgbaXUayJyIjAf6KL2EyhHOvvQ2Fq1lR11OwgbYbyal3R/Ov3S+2GaJrt27WLLlnoe/j+TNUUdQSkuHLuHU07O5dQLTiIu/zMqO44mGNyAGarF782kz8DT2zpKQMtSwQ6Hlx9UOjsSseSt4+OtJqI2ZMyYMcyZMyc6QutY4KiUzlZK6SJyC/Ax4AJeUUqtEZE/YFVjPgDuBF4SkduxOp2v2Z9BcPj+mMqksKaQ7dXbyU7LJjUulXiPNfU/FAqxe1khf/ywnLn/7EU46CE+Q3HduKUMGBGgndqOe0slAUMhag9mXRC/9yQ6dc1s41g5/JD8INLZSlnrHOzcaXUqBwKWbtEB1so+UpSVlXHHHXccUwbhUHEW2TmOMZVJUA9SUF1ARbCCiBmhZ0pPuid3RylFbW0te/bsIbSpmnfmpvO3f/lJj/Nw6qnwm7GzSaUcd/dOhBGq0kYT70kiWF+E1++mW/ZgXK6jZ0K8U1M4DogVsANL2TQz0xGw+x4clTUFh7YlqAcpri2mtK4UU5nR2oHP7cMwDHbt2sVn68uorXPz3mudWZ2r4TLD/Oqn67lk3FqMbSVUZJ9FwO0jPrEOFVmD7nGT2M5Levv+R5VBcDjGUcpaErOsbK+AXadOlm7Rj3AUW1vjGIXjkLAR5tsd3+Jz+eiT2odOCXulpkOhEKWlpXy5rYairUm8PaM9FWUmiVLLry7N5dSTMyjcmoHu74pm6qQmFhCfmIgvrj9pHbrj8ex/URIHh+9FTY1lEFJSoGNHR8CuDXFS/jhkfcV64t3xjOg4IjpO3TAMKisrqaiooObbrXy0uDtffpyCRGro03kn1436hOFdBbOuG9oJ7UkZ0A2XqwS/fxAJCb3bOEYOxx2maW0Ni91kZlrNRweYu+Bw5HGMwnFEyAixsWIj1eFqBmQMQERQSrEnt5iqyiqUqfh8Tzkff9KLb7/uiktMLhu7ntNP3UxW+omoodkkJSaSmuqmPpCHx5OB39+9raPlcLwRCFgSFR6PpWQqYs1MPsDSmA4/DE7D8HFCUA+yqmwVQSNI/4z+pMRZY8J3LS2goqKCMr2Qj41dzF+UyfKvuuJB8Ys7dnPrxUvxd0wiLqc/nTpl0LFjOoHAWnxxnUlJGYqmOeWG1nKsSmfPmTOHwYMHM3ToUHJycvYpcxEIBBg7dmyjuQ5PPvkkPp+Pqqqq/d5n3LhxLP32W9i5k9o1a/jf++6j92mnMWDgQMaMGdOiKuvBoJRi6tSp9OnTh8GDB7N8+fIW3c2aNYtBgwYxePBgzjnnHHbv3g3AtGnT6NKlS1Q6+6OPPgJg9erVXHPNNYcUtmMNxygcB+wO7Obr0q8J6AEGZAwg3ZdOeGMl5csKKVq/lgqjiGXKx/KvBrN6fncwTf547df8pP2/2VJcDcokJaWCcGQl1dXLcbniSUzs19bROuZokLnIy8sjPT2dZ555BiAqnX3vvfeyceNGVq5cyZdffsmzzz4LEJXOfuONN1i3bh15eXn06tWrmf+x0tlTpkw5bOE+88wzWblyJbm5ubzyyitcd911Lbp75ZVXuOiiixppWs2aNYuTTjqJ9957b/83MQwoLobycq773e9I79SJTVu3smbNGl577bVo5vx9mTt3Lps2bWLTpk28+OKL3Hjjjc3c6LrObbfdxoIFC1i1ahWDBw9uJJV9++23k5ubS25uLpMmWYLNgwYNoqioiO3btx9S+I4lHKNwjBPUg2yu3ExmQiandTlt79yDQIj1O1bwTUc/s9YNZsbDI/jq7SSUYXL+pRvomfI1gdp42nUZwKDTTkRRTWpKDunpp5KWNvJHqV10ODmWpLMTExOjz7uurm6fz76pdPaWLVuora3loYceYtasWS0nhFLWJLRQCCIRtuzYwTdr1/LQk0+i2Z3JvXr1OuQZw3PmzOGqq65CRBg1ahSVlZWUlpY2CYol41BXV4dSiurq6lZJZ0+ePLnV0uDHA07bwDHMtqptlNWX4RY3vVJ7oYll46tX72TLmpV8GZfGnOeHUbHRjaHCtMuo5mdn5dErdQlZoqgbPIRO2Z2JRDaR4O+Dx3P8LHFZ81lzyeVDJWn8Ga1ydyxKZ7/33nvcd9997Nq1iw8//LDZ+XA4zNatW+nRo0f02KxZs7jssss4/fTT2bBhA7t27aJDhyYS6SJ7J56lpbGmupqhw4a1SkF3ypQpbNiwodnxO+64g6uuuqrRseLi4qjUNeyVzs7M3DvB0uPx8NxzzzFo0CASEhLo27dvtDYH8PTTT/P666+Tk5PDX//61+iEtZycHB599FHuvvvuA4b5eMAxCscw1eFqOid2JqXIg15RTdHWTdTX1VEfDPBVu3g+fLY/1ZtNEr3l/HTsei49O4zLCFFU6af2hFPo1LsXbnclkIrf37Wto3NYaW0Gfjg5lqWzL7zwQi688EIWLVrEAw88wKefftro/O7du0lNTW10bPbs2bz33ntomsZFF13EO++8w80334yYpqVR1EC7duDzIenpVidzK9lfv0lTWpqE2zRtI5EIzz33HCtWrKBXr17ceuutPPLII9x///3ceOONPPDAA4gIDzzwAHfeeWdUF+nHJp3dquYjEfGKSJ8jHRiH1hMyQoSNMBXrNrN781byK9eyw12OL6cHmWcOY8Gs3lSsF+L9ddx501rGnL6JYr2Q7WoX4aRE0vv0IcUuCblcCVhrIjkcCg19CgUFBYTD4WgpdMCAAc06fluSzj4YWqtEcLBGZ8yYMWzZsqVZG7/f7ycYDEb3V61axaZNmzjrrLPo0aMHs2fPtpqQqqrICIfZU1xs9SMAaBoVlZW0a9eOAQMGsHLlSkzTPGBYpkyZEu34jd1ef/31Zm5bI53dsIxp7969EREuueQSvvzyS8Ba/c3lcqFpGtdff32jVdt+bNLZBzQKInIusBqYZ+8PFZED9Co5HEmKa4v5quQrtK1BfMUa8Z0ycbXrSJf+g/D7/TzyYpiVX6eCT+O6a7cjvTPR4lz0HnkeXYZMoO/Ic8nIyKCmZg3BYCng9B8cTlJSUpg+fTqPP/44kUiEK664gsWLF0dL34FAgKlTp0abI+666y4efvjhaCneNE2eeOKJ/d6jX79+5OfnRxeenzlzJmPHjm3kZuTIkSxcuJDy8nIikQjvvPNOi35t3rw5amSWL19OOBxutqZCWloahmFEDcOsWbOYNm0a+fn55OfnU1JQQPH27RR89x0n9e/PkhUr2GGXrpcuXUooFCIrK4vevXuTk5PDgw8+GL3npk2bmDNnTrNwvf3229GO39itadMRwPnnn8/rr7+OUoqvv/6alJSURk1HAF26dGHt2rWUlVkLSc2bNy8qBRHb//Dee+8xcODA6P7GjRsb7R/vtKb56A9Yi+MsAFBK5Tq1hrZl857NdEvqhllWyu74KrQ0nQR/AiLCB+97mTs7kUSvi6uv3sLpYzuwsjpIdoKPqkAAEaFjx44Eg9sJhnaQnDwEj9uZMHS4OZaks//5z3/y+uuv4/F48Pv9vP322y3WMCZOnMjixYuZMGECs2fPZu7cuVZHcmUl7NzJhWecwey5c7nn/vv529NPM+mCCzBNk8TERGbNmoVm9y3MmDGDO++8kz59+hAfH09GRgZ/+ctfvm9SA5Y89kcffRT189VXX42eGzp0KLm5uXTu3JkHH3yQMWPG4PF46N69e1Qi++677yY3NxcRoUePHo2WHXWks5s6EPlaKTVKRFYopYbZx1YppQb/ICFswo9dEG/Tnk2U1pXSJddDdVUVaaf2w+v1ouvw7DMJzP6XC13XuWxyMRPG76YyLh3DZzJOlhE36AJSUlJwu91UVS3H6+2I39+lraN0WHAE8Y48K1as4IknnmDmzJl7D5aUWEYBrNnInTodVwJ2oVCIsWPHsnjxYtzHkPTGkRbEWycilwCaiPQEbgO+PsA1DkeA2nAte4J76JfWj9LKFaSeegJer5eaGuFPf0pmxXIXinqmXr6Jn/0ik7jiNawtWc3JfdqTkNwF1/9n77zDoyrTh32/M5OZSSchAUJISCAF00WauzQ7UhTQVdRPZBV1FfuuZW3r+sOy6oriYkXXtZCwyy6CfZGyKkgnQBJKgCSkQRJCyiTT5/3+OMmQkBAC6cm5r2suOGfeOeeZBM5zzlvup65LwGorw+Goxssrsou/kUpP4sILL+SSSy7B6XSemj3k7w8mk+Ir6oUCu6NHj/Lyyy/3qITQVlrzTe8DngVcwH9Q6iP8sSODUmnMgfIDlJpLEQiCPIPI/WUHHlKw3QEnCm2895Q3ZcUuvHwsXH/LHm65ZThCpyO7tIyqiMvxHRXbqDvA6ahGrw9Gp+vXwllVVJpy+803K08G9WMO3t4QFdVl9Q46mujoaKKjo7s6jE6lNUnhKinl44C7krUQYjZKglDpYI7VHONYzTESghLw1flycOVP6CxwaFgERbuqSHshCHOlIHloLU/fdQCjnxGh06E9sh6XxoNrx0Q3TghOMxZLEUbjYHWBmkrrcbngxAkoK1PGETw9lapo0GsTQl+lNUnhaZomgKea2afSAVgPVhDm6A/Ha9ZD4EUAACAASURBVMnct43tnnp8xsZydJ+B5X8OxGwzMHKchhfv+i96jZWqSh0eR7KxOSXWiFgqK7c0mm4qpROh0WM0hrVwVhWVBtQL7OpXTAcEqPK6XswZk4IQ4ipgChAqhGg4P84PpStJpYOxHCgHKSkNqMEpDWwe6EFQZCSunb58ttCA3WLnkkl2Hn/Whm+xheoBE6nVFWD3L6TYCgZNKUbjKPT6wEbH1Wj0quhO5ey4XFBSohTAAdDrFcW1t3fXxqXSobR0ZSgBMgALkNlgfzXwREcGpaLgcrrYV5tHdUk1680lAJSl2Vm2zAhScv1Fu7n/N9noir1Bo8NqLcNqP07YoOEcP+7PpLhQNBr1jk7lPKlPCEJAYCAEB6tdRX2AM/6GpZS7pJQfArFSyg8bvP4ppWyb0lDlrFgOlFNdY+KnghNkO3X4+/hiz5jIiuXheGg03D15E/devQXHiCuwDr2E6oFJ7M7dxjGrlt3HPdF5+KgJoZPpqerserZt24ZWq2XFihXKjqAg5akgIgIGDsRcNz3zvNXZdTGZTCbuvvtuhg8fTnx8fLdQZ9fz2muvIYRw7//qq6/405/+1KbYehqtSfuhQog0IcQeIcTB+leHR9aHyVy/k837t/O/sq3YNZIJEYPJWn0RK/5pRGO38NCNB5gzOQPXuGsRBgPVJ6vYvH0LeucgLht1OZcnjGBy7ICzn0ilXemp6mxQJH6P/+EPXDVxojKQDEpVtKFDlUFl2qjObsD8+fMJDAwkOzu7W6mz8/PzWbNmDeHh4e5906ZNY/Xq1dTW1rYpvp5Ea5LCx8DfUVwIVwP/BPqOR7YTyVq/kz1rtlFTU4tpiI3DLi1+ugCWvDCCHzd4YHRW8ez8vVwxbh+24HAs0ovC/SXs2rce70AjU6+8Ar+gvuNo6c70JHU2Dgdv/d//cd2ECQzw94eammabnZc6+zQOHz7Mli1bWLhwoXuFc3dRZz/88MO88sorjWblCSGYPHkyX331VZvi60m0ZrTRS0r5vRDiNSnlYeBpIcRPHR1YX8Rud2DXVlDjrGJnoQNPAtmZOp6DB3UE6Ct45ncH6X/BQE4WHccjaiTS7MDpkYXXoBCmXTwDTSt0xH2FnD3t38MZmRTUqnY9Rp0tJVRVUbh7Nyu//pp1H3/MtoULmx1IPm919mlkZmaSkpLS7dTZq1evJjQ0lOTk5CbnGzVqFD/99BM33HDDWWPuDbQmKViFkjoPCyF+BxQCat9EO3N45zaKcg5hCqpCGzUAV20Qu94excGDOgYOdPHCjdsIGmzCUlpMwGAX1v5aqqv3MSDAi2rzKDUhnEZrL+DtSY9SZ9vtUFwMJhMPvfACf3nySbTR0cpU02ZiOid1djt9185SZz/yyCO88MIL/Pe//2322Ko6uykPAz7AA8CvgTuB2zsyqL5ETvoODm3fwpH0XCy+GrRRA6jUxLLjnRQyNrnwFyd5/jfrGRBgA+GCyIuoHjgSu/04AQGSPEscHjp9V38NFXqYOttkUl5aLdv372fOww8TERPDihUruPfee/niiy+afLdWqbOB/v37c/LkyUafLy8v77bq7MOHD5OTk0NycjIREREUFBQwcuRIjh07Bqjq7CZIKbdIKaullEellLdKKa8B8johtl5PTvoORLETX+cAPHz98UsJZoT/CNZ8EkLeZj2+/oIXFzsZMqyCCpuD6moDVqsFvf4wfn5VBASMwIm/OqjczegR6ux+/ZQppsOGkZOX51ZgX3/99bz99tvMnDmz0bHOqs4uKqKwsJC8vDxGjx7Nxo0b3RfV7q7OTkxMpKSkxP1dhgwZws6dOxk0aBDQ99TZLSYFIcRoIcRMIURQ3Xa8EOITVCFeu1CQVUgZdjJrcnCF6Kh11rJjmyebvzKixc5zz1USHu6iqlKHLnokhqQEwqMH4e2tJyhokiq068Y0VGd7enqyatUqFi5cSGxsLImJiYwePbpZdfYFF1xAQkJCk0HS02mozk5MTESj0bSozr580iRGRkaeKnwjhJIUzsFoWq/OBqXraNasWY3enzVrFmlpaQwcOJA333yTqVOnkpKSwkMPPdREnX3s2DGioqJITEzkzjvvbFWt5JaYOnUqw4YNIyoqijvvvNM9swsUdTbQSJ2dlJREeno6Tz755FmPraqz698Q4iXgOmA3EAmsRDGk/gV4R0rZJXO0eoM6Ozs7G5fLRfHmfYT+Kh6j0Ui+OZ+yEy5ee/RC8o85+N11Zcz8f1rI3YGUIGISOXCyBE/DcUVboR8HQqDTCvVJAVWdfUYsFkVRUd/1M3DgKZndOdKsOruXc/z4cW6++WbWrl3b1aGcEx2lzr4WSJZSmoUQgUBR3XbT6QAqrSYnfQeFhYUMDg7CX+uJXq/nQPUBKm1VfLF0AoXlGiKHHmPGuN3oC11g0GEdkoyPrwFPs5lfxSTj5TVcldmptIzLpcjrTpxQZhl5eCiKCh+f8z5ks+rsXs7Ro0f561//2tVhdCotJQWLlNIMIKUsF0LsVxNC28hJVwYTBw2Pwbtch3aIH/mWfKrsVRRvGcee7f5oPaz8cfIveMRMxhCgx2KxkFFkwc9WgofGisEwUE0IKi1jsUBh4SmBXb2ioh0u5Lff3rfmmIwePbqrQ+h0WkoKw4QQ9SZUAUQ02EZKOftsBxdCTAHeBLTAUinly820uQF4DpDAbinlza0Pv2fhcjqwVnlQW1OGh3c/ysPMnKg9gbcplk8/6o/JKZl+UwnxocUQoMdqtbKjyExgYCDJA47j6zsWnVo6U+VsCAE2myKwGzz4lOJaRaUVtJQUrjtt+2/NtjoDQvE1LwGuAAqAbUKI1VLKrAZtolEK9vxaSnlSCNErO8dz0nfgcjrQaHVYLFb8YgZg9PaGWjPB+oEseTuC0mpJ7IUmbhtVDcWK52ZrvomgoCAuiwuhrGw/ev359QWr9AHMZjAalYRgMEB4uKKnUAV2KufIGZOClLKtIytjgENSyiMAQog0lHGKrAZt7gSWSClP1p2zpI3n7HbUdxkNv2gsDoeD/Oz1BBlDMTlMWFwW1n01iL17PfD0tfHy7J8YLEtwaJQ6y/4BgUyM9sZkOoBUbeUqzeF0wvHjSjW00FClPCaoemuV86YjbyNCgfwG2wV1+xoSA8QIITYKITbXdTc1QQhxlxBiuxBie/0c455AfUKITLkIKSXHt+aCRlBsLSazIpO8PA0rU0Optru4ZuZe+tfkUGsZiGlQCvsqBEa9B3b7CZzOWnx94hBCvetTaUBVFRw+rCQEIU5NN1VRaQMdeZVpbjT09PmvOiAamAzcBCwVQjQpHCylfF9KOUpKOap+yX5PwOV0EJmiOG/KduZjsVrwGOyNyW4izDCc/7ybgsuuI2FcFfdGpeP0jUQbF8c2jwF4e3u7p5rqdH4YjSEtnUqlG9Bp6uzkZJa//TYUFIDDoYwZDBumDChz7ursDRs24O/v714x/Pzzzzf7/aSUXHrppVRVVbn3rVy5EiEE+/fvb3S86dOnN/rsvHnz3Epuu93OE088QXR0NAkJCYwZM4Zvv/222XOeCy+99BJRUVHExsby/fffN9tm7dq1jBw5kpSUFMaPH+9e/FfPihUrEEK4f3579+5l3rx5bY6tJ9HqpCCEOFc5fwHQsObjEJRprae3WSWltEspc4ADKEmix5OTvgONVumds1gs7DtygGP6SjQaDfZq+PRvQezP1KLxtTJ9ajoeHhJNSAhWrRajlzeXxalJoKfRKepsq5X0f/6TGydPVsYLBg1S9NZtLI85YcIE94rhZ599ttk233zzDcnJyfj5+bn3paamMn78eNLSWi9OfuaZZyguLiYjI4OMjAy+/PJLqqur2xR/VlYWaWlpZGZm8t1333Hvvfc2qvtQzz333MPnn39Oeno6N998MwsXLnS/V11dzeLFixk7dqx7X2JiIgUFBRw9erRN8fUkzpoUhBBjhBB7gey67WQhxFutOPY2IFoIESmE0ANzgNWntfkCuKTuuEEo3UlHziH+bkv9U4LT6WT7N5vQeuiIjIwkZGAo6Zv6sWZdAHpPwc1/rOT6IaXYB49CSonL5cLLywuXy47dfhKL9VhXfxWV86DD1Nl79pAycyaHT5xgbW4uF152GYlJSW1TZ7eS09XZJpOJjRs38uGHH7Y6KdTW1vLBBx/w1ltvYahLZAMHDmyzgXTVqlXMmTMHg8FAZGQkUVFRbN26tUk7IYT7SaeysrLRSupnnnmGxx57DKPR2OgzM2bMOKek19NpjSV1MTAd5QKOlHK3EOKSs31ISukQQtwHfI8yJfUjKWWmEOJ5YLuUcnXde1cKIbIAJ/ColPLEeX6XbkVhSSn2/fuVLgSXJHLcCKxmOxu3FvDZP2LQaATjb64iMtYOZYDdgc1uIyAgAN2xck6U/wiAh84fo/H0oRiVs3F4R9sqeTXH8IvGnr0R7azOlhIqKhjQv/8pdfYXX2Cx25kcE9M2dXYDfvnlF5KTkxk8eDCvvfYa8fHxTdps3LiR9957z739xRdfMGXKFGJiYggMDGTnzp2MHDmyxZ/NoUOHCA8Pb/S0cSYefvhh1q9f32T/nDlzeOKJxhWBCwsLGTdunHu7Xp19OkuXLmXq1Kl4enri5+fH5s2KsWfXrl3k5+czffp0XnvttUafGTVqFC+//LLbVdXbaU1S0Egp805bMNWqES0p5TfAN6fte7bB3yXwSN2rV+FyuQgNDUWr1eLpUPp6d5Xs5aNPEpBOb8aNdXLxNSYuLd+K/UgxTr0eGRaGv78/FOVi0Afj6xuvKC1UzpnWXsDbk3ZXZ1utit66tlaZclqPTseBzMy2qbMbMHLkSPLy8vDx8eGbb75h5syZZGdnN2lXXl6Or++pdTKpqanu882ZM4fU1FRGjhzZbursRYsWtbpta9TZ9cf85ptvGDt2LK+++iqPPPII77//Pg8//DAff/xxs8dW1dlNyRdCjAGkEEIrhHgIUMtxngXpkpSUlGA0GhEIcqpzWPlxJMdyBzEgSMMjj1SjwYXjaBFWQwT2mBiCYmPxqBOUabXeakLoYbSbOltKRVFx5IiSEHS6JnqKNquzG+Dn54dP3fGnTp2K3W5vtjymTqdzK69PnDjBunXrmD9/PhEREbz66qssX74cKWWL6uyoqCiOHj3aqjGEhx9+uFl1dn23W0Nao84uLS1l9+7d7jGDG2+8kU2bNlFdXU1GRgaTJ08mIiKCzZs3c80117h/Z6o6uyn3oNzJhwPHgXF1+1RawGazIaVEk2+lwl7Jl/8RbFsfgcEo+PMdGxhUtQ7N4c04y03IoUPR+vqSftzGDxk5aDW2rg5fpQ20SZ1tseA6fJjXX3lFSQ79+sHw4XBad0ub1dkNOHbsmDvJbN26FZfLRf9mpHmxsbEcOaIM+a1YsYK5c+eSV6fdzs/PJzIykp9//pno6GiKiorYt28fAHl5eezevZuUlBS8vLy44447eOCBB7DZlH/nxcXFfPbZZ03Ot2jRombV2ad3HYGizk5LS8NqtZKTk0N2djZjxoxp1CYgIIDKykr301K9Otvf35+ysjK3OnvcuHGsXr2aUaMUd1xfU2e3pvvIIaWc0+GR9CKO7NqOxWbDcMyJ1GjYeyCHL/8+ET0WHvxNJuXD7PzXfwyaijycycOx9+vHoOBgjuTXkjIoF63OB53u/MVlKl1PQ3X2rbfeyqpVq7j//vtZsGABTqeTW2+9tak6e84caisrEcC0Sy5RViWfQWDXUJ3tcDgYPXp0i+rskJAQRo4c2eyMnBUrVvDOO++g0+nw9PQkLS2t2SeMadOmsWHDBqKiokhNTW1ycb7uuutYtmwZEyZM4LPPPuO3v/0tFosFDw8Pli5dqnSNAgsXLuTpp58mLi4Oo9GIt7f3GafBtpb4+HhuuOEG4uLi0Ol0LFmyxC3tmzp1KkuXLmXw4MF88MEHXHfddWg0GgICAvjoo4/OemxVnX16AyEOo0wVXQ78R0rZtrljbaS7q7Nz0ndgsVgoya8hKnQYxQV7ue8vI6kw92fGNBcPPWTixxorE8xVuAoKcMbEoDMaOVxjQDrzSRxYSf/+k9WFaudIr1FnFxUpU03bSWDXnhQXFzN37lzWrFnT1aF0GlarlUmTJvHzzz+j07XmHrp70BZ1dmsqrw0HFgIXAXuFEF8IIdQnh2aoX8HsPTgM6RTYgvX8fWUkZWZ/oqNdjJx3gh9rrOgEOA8fgcBA7FISHByMw36CpEEmfHxGqAmhr+B0wrFjyrhBPSEhytqDbpYQQHnyuPPOOxstXuvtHD16lJdffrlHJYS20qqrj5Ryk5TyAWAkUAV83qFR9VBcTgf9tUOwZFbgKDvO/1bu5YdtoXh4wBX3VaLTwyQfA7/2NgASa//+BAYGYjDowXUCvT4Ig2FQV38Nlc7AZFIGksvLlcRQ/8TezbXoN9xwQ6umk/YWoqOjmTx5cleH0amcNf0JIXxQRHZzgAuAVcCvOjiuHktNtYlqHx0Wg5N3V4xBGI3cfHMlwUMddclAwWF3kH7MSj+7HVGcgZYT6A3qU0Kvp/7poLJS2TYaFb11N08GKn2H1jwTZQBfAq9IKX/q4Hh6NLLQQbWxBjwM/POHKMpKdcTHSmKutaOp+z8vbTaclZXsO26i3wX9uCJe0WJ7eSVh0Ad17RdQ6ViqqpSE4HCcqpHcv7+aEFS6Fa1JCsOklKq3+SzkpO/AZrVTE6Bh5+FK1uyIxtPbg0sWnESjxf2U4CotxZyXh3ZwGJcnDnF/3tNzaFeFrtIZOJ3KQjSnUxHYhYS02VekotIRnDEpCCH+KqX8PfBvIUSTKUqtqbzWl7BYzFR7S/IK1/Dlv8ejE1rmXefkhgu0KJYPcB07jiU7G69hw9H5DUUIgakmm6byWJVeQcNxAq1WGUB2OiEgQH06UOm2tNSBvbzuz7+hVFA7/aVSx87NP5B3+L+Yiw9QlRNNXk4SBm8PfvObU7NKpMmE+cB+9uv6s88zFA+tBqv1OBZzAb6+CepYQi+gkTp72jQq9u6FE4rKKzMzk0tnzSJm3DiiY2Laps5OSWH58uVN3q/nXNXZoOiuU1JSiI+Pb7IIrp7erM7++OOPCQ4Odq+aXrp0KaCsgp4ypdkyL72WM16JpJT1isELpJRrG75QBpxVAJd0UZq7kYG6oQQMHM332ybicNoZdWU5/v4S6XTiKinBun8/IiAQjxHx/Gp4LUnB2ZhM+zEaQzEYemUV0j6Hp6cn6bt2kfHjjwR6eLDkww/h5EnMNTXtp86220lPT+fGG29st7grKiq49957Wb16NZmZmWdc+dzb1dk33nije9X0/PnzAQgODiYkJKTNhtmeRGtuT29vZt8d7R1IT8TmtPHtus+ptpswBCSzv2IwezIE3t5OrrpZaeM6dhxHTg4Oo5HQX12MTueB01mDl9cw+vUbi7d3VNd+CZX2JS8Pjh3j4uRkCk+ehMhIlqWltZ86Oz2dlJQUDh8+zNq1a7nwwgtJTExskzp72bJlzJ49m/DwcEARwDVHb1dnn4mZM2fy+ed9ZxZ+S2MKN6JMQ40UQvynwVu+QEXzn+pbmB1mcLoYEx6DudrIex8o/cTjbrDh4610Dbjyj2IbOJAB8fFsKbagFVacLgsajR6t1tjS4VXagDmr/Q3snnFNfUCAMnZw4gS4XFBbi1MI1u7Zwx133w06XdvU2XUMGDDglDr7q6+wWCxMnjy5XdTZBw8exG63M3nyZKqrq3nwwQeZO3duk3a9WZ0N8O9//5sff/yRmJgYFi1aRFiYUiNs1KhRPP3002eNt7fQ0uyjrcAJlIppDccQqoFdHRlUT6HAVIDL7kLnoeHbjbUUFnsTMtiD0Vef4NfeBlyVlTjsdoxDhuDv74+jqIRfRTix2nRotarbqCM54wW8ozCZMFutpPzmN+QWFraPOrsFDhw40G7qbIfDwY4dO1i7di1ms5mLL76YcePGuY9dT29VZy9dupQZM2Zw0003YTAYePfdd7nttttYt24d0PfU2WdMCnXlMXOAHzovnJ5FaU0pXi5vtCcF/90UgkOvJ25qFV5GiaukBEf2IZw+voQOHMj/DpaiFWZqa3Px8opAq1WnI/ZoXC7lpdMpM4lCQpQxhcxMKisrmT59OkuWLOGBBx4gPj6eH3/8sdHHm1NnJycnt/r07anOHjJkCEFBQXh7e+Pt7c3EiRPZvXt3k6RQr87WaDRudXZGRgZCCJxOJ0IIXnnllVarsxsmmOY4lyeF81Vn1w8iN7TC3nnnnTz++OPubVWdXYcQ4n91f54UQpQ3eJ0UQpR3Xojdj5z0HRzesYWSXVlg0bF5dzDZBf4YvFw8MltZuSztduyBAQSOHYPRaMThlIwJd6HV+eDpGdHVX0GlLdTUKIqKwsJT004brDlokzobpUDT66+/3mII7anOvvbaa/npp59wOBzU1tayZcuWZuWCvVWdXR9DPatXr270/VV19inqS26qy2xPw+V0EJEyiu22PALzXaRuC0ProWHc1TUYbSdxlptwnDxJ5kkXXkWVaPJ/QquBWrNfXTU1dY56j8TphJISqL8LFkLZ14ws7bzU2TfdRG1tLUKIs6qa21OdfcEFFzBlyhSSkpLQaDTMnz+/2Ytgb1ZnL168mNWrV6PT6QgMDGxUhU1VZ5/eQIgIoEhKaRNCjAeSgM+klF2iSuwO6uzDO7bgFR7O+v0/UrQzhLffGY30cPDsO8VcVnEQvH0wSxdH9IO4fGwoppqD9PO/CBBqQuggOlydbTIpK5LtdiUZBAUpigpN31lf0hfV2QATJ05k1apVBAQEdHUoraZD1dnAFyilOIcDn6CsUVh2PoH2FpxOJzvzdxFW2Z+ffxqMQTiZcnEZlx7bA1odtpBB7PMegmdwEDZbKQKBEBo1IfREpFRqHBw9qiQET0+IjFS8RX0oIUDfVGeXlpbyyCOP9KiE0FZa4z5ySSntQojZwBtSysVCiD45+ygnfQcup4PaQ1vxPhnAL84h7NodiI8ebhubjQgMRERHo7Hb8fQ2Mjl2AGVlmfj6xnd16CrnixDg4aH8OWAABAb2aUVFW9cT9DSCg4OZOXNmV4fRqbTmVschhPgNcCvwVd0+j44LqXuSk74DUexkgEcEvrZQdg8exvr0JLx0eq68UhISYEIbE4PFYiE4OBit5lSRFL1qP+1Z2O2NC9/076/USVaNpip9gNauaL4ERZ19RAgRCaR2bFjdD5fTwcChw6kZIHH1t3CwzIf87T7o9S7m3aYs0bdarXh5eeHj4wOuCkymA6iyux6ElFBRocwsKihQFNegdBPp9V0bm4pKJ3HW7iMpZYYQ4gEgSggxAjgkpXyh40Prftjsdk6WVbLB0o+f0pQ50NOmlRJQnofU6XA4nYQEBuJ0msCWgdMZqZbX7CnYbMpAck2Nsn2WOfQqKr2V1lRemwB8ChQCAhgkhLhVStl3DFEoN5FVlZXoBujYvD0Y8zEjAwNrufrqE2B14QoPx9PTE4PBg/9lbker88Xfv+Ul/yrdACmVkpilpcpitHrFtZ+f2lWk0idpzS3sImCqlPLXUspfAdOANzs2rO6H3WbDZrPhzNnFxm8jwSG5aU4Jw4aFgktit9vJOgnr9v4EzhJ+PeKsM79UugNFRXD8uJIQ/P2VsQN///NKCI3U2TNmUFFxShGWmZnJpZdeSkxMDNHR0d1Knf3qq6+6ldEJCQlotVrKy5uuT+3N6ux6VqxYgRDC/fPbu3cv8+bNa3NsPYnWJAW9lDKrfkNKuQ/ocx2sssiOsXI/P2ztT63Fk+joGq6d5oVjxw4cJ0/i4eWFRqtl1FBffh03Cb0+sKtDVmkN/fops4vCwiA0tNmFaK3F09OT9PR0MjIyCAwMZMkSRRlmNpu7tTr70Ucfda8Wfumll5g0aRKBgU3//fZ2dXZ1dTWLFy92azAAEhMTKSgo4OjRo22KryfRmqSwUwjxnhBifN3rHfqYEC8nfQd2m5P8fnms2jYM4ZJMSsjBdSgbodFivyCGgNAghDMfp7MGjUb1GnVbzGYoKzu17e2tPB208xjCxRdf7LZ0Llu2rFursxuSmprKTTfd1Ox7vV2d/cwzz/DYY49hNDa2F8+YMeOckl5PpzW3Rb8DHgAeQxlT+BF4qyOD6m447HZ8Bg4iv2of+Zl+eOpdjIwvQxMcidPDA5cmD4ejCpwmvL1T0OnUQcqu5sCBA413uFxQVQX1d6QDBpxzjeTY2NhWtXM6naxdu5Y77lDKjnR3dXY9tbW1fPfdd/ztb39r9v3erM7etWsX+fn5TJ8+nddee63RZ0aNGsXLL7/sdlX1dlpMCkKIRGA4sFJK+UrnhNS9yEnfgcvlIr36KF9tuoAaBMmjHfhEDUQTHExNdSX9/Lzx84sDgxOjcWBXh6zCaRfwmhplZpGvrzKAHBjYISuSzWYzKSkp5Obm9ih1dj1ffvklv/71r5vtOoLeq85+//33efjhhxv5jhrS19TZLVlSn0RRXNwCrBFCNFeBrdfjcjoYEHMBZpeDY1vD8dZquOyiMjQaDRaLBaOxBg89aDRqwZxuh9OpJIO8PGXKqcEAEREwcGCHKCrqxxTy8vKw2WzuMYX4+PgmA7/NqbPPhfZUZ9eTlpZ2xq4jOKXOBtzq7Pnz5xMREcGrr77K8uXLkVK2Wp19Nh5++GH3AHjDV323W0POV529adMmqqurycjIYPLkyURERLB582auueYa9++sr6mzkVI2+wIyAe+6vwcD287UtoVjTAEOAIeAJ1podz3KKq9RZzvmRRddJDuTQ9s3y/z8fPnaR1/K6IhSmZJYLdetU/ZlZ2fLyspsuW7vFrkm85hcv/94p8amcoqsrKymO4uLpczMlDIrS8qSEimdzg6Nwdvb2/33nTt3yrCwMGmz2WRtba2MjIyUa9askVJKWVtbK6dNmyYXL14spZRy9+7dcvjw4fLAgQNS2S+lJAAAIABJREFUSimdTqf861//2uT469evl9OmTZNSSmk2m2VYWJjMzs6WUkp52223yTfeeENKKeWkSZPktm3bZFFRkQwPD5dlZWXSZrPJ8ePHywULFjQbe0VFhQwICJAmk+mM32/s2LHu87377rvyrrvuavT+xIkT5Y8//igtFouMiIhw/05yc3NleHi4rKiokFJK+eijj8p58+ZJq9UqpZSyqKhIfvrpp2c8b2vIyMiQSUlJ0mKxyCNHjsjIyEjpcDgatbHb7bJ///7un/PSpUvl7Nmzmxyr/udXz4oVK+Tdd9/dpvg6m+b+PwDbZSuu2y3dLlmllDV1iaOU1g1KuxFCaFEqtl0NxAE3CSHimmnnizJmseVcjt8Z5KTvUER2B/7L/s39EC4YlWzGaHRhNpsJCgpCo3HidEkujxvI5Njma9uqdCIN76CDgpQuo2HDOl1g11Cd7enpyapVq1i4cCGxsbEkJiYyevToZtXZF1xwAQkJCY38/s3RUJ2dmJiIRqNpUZ19+eWXt9jfv3LlSq688kq8vb3P2KZenQ1K19GsWbMavV+vzjYYDG51dkpKCtdff30TdXZwcDBxcXEkJCQwc+ZMdxfX+dJQnT1lypQm6uyioiJ0Op1bnZ2cnMynn37Kq6++etZjq+rs+jeEqADW1W+iqC7qt5FSzm7xwEJcDDwnpbyqbvuPdZ976bR2b6BUd/sD8AcpZYte7M5UZx/esYXswcM5lv4t7700nvLi/jz5RC3DYrIZOnQovr7HSc87CvoRXBKnSu+6kn379nHB4MGKpiIsrM8ZTDuDvqjOtlqtTJo0iZ9//hldG6YrdzZtUWe39C2vO227+SkJZyYUyG+wXQCMbdhACHEhECal/EoI0XS1zql2dwF3AYSHh59jGG3DVrCT4WVWKo764eXvwejRdkrKnfj7+2O3H8apu5DL45rOKVfpREpKlFXJ9Tc4VVXK+gOVdqWhOrs1s4d6A0ePHuXll1/uUQmhrbRUo3ltG4/d3AiX+7FEKEKgRcC8sx1ISvk+8D4oTwptjKv1FO3G5jeALTtHgISURAuensrpDQYDO3Mr0BpGdFo4KqfhcsEXX8Abb8BzzymKioEDlRXJKh1CX1NnR0dHEx0d3dVhdCodmf4KgLAG20OAhvO6fIEEYEPdDIlBwGohxDVn60LqLFxOO9VmX7L2DcKl0ZGSXItGp+S6X3IrAdRxhK4iPx8WLoT6WTtGozJ24NHnrO4qKu1KRyaFbUB0nWq7EJgD3Fz/ppSykgb1n4UQG2jFmEJn4nK5cLlgd0EgTg+YcJkgvzCXQlM+g601JIb60/wDkUqHs2uXkhACA+GxxyAgQE0IKirtQKuTghDCIKVsuo7+DEgpHUKI+4DvAS3wkZQyUwjxPMrUqNXnHm7nYq2oorhaYKoVhAwGuz2PgyUnCAnVMzY6AYMhWNVidybV1ad0FDNmKIPK116rdBft29e1samo9BLOekUTQowRQuwFsuu2k4UQrdJcSCm/kVLGSCmHy7oaDFLKZ5tLCFLKyd3pKQGgsqqCnUc8QSNISbDjcrnQ+wcxMnIIXl5D0Wq9ujrEvoHNBu+9B9OnK7WSQbGYzp2rjh+oqLQzrbnNXQxMB04ASCl3o0xP7Z1k/wAHvsW2dxW1VjPHjoSg1xpIjLO6V5F69KGZCF3O3r3w//4ffPCBoquoc9V0R3qqOruyspIZM2aQnJxMfHw8f//735s9rtlsZtKkSY3so4sWLcJoNFJZWdnieRrGZDKZuPvuuxk+fDjx8fFMnDiRLVvatkxJSskDDzxAVFQUSUlJ7Ny5s9l2qampJCYmkpSUxJQpUyirkyM++uijjBgxgqSkJGbNmuX+3anq7DO0kVLmnbavqZO2t+CyQ+zVVGujschBFOcGIBDEjbDidDrx8PBAqHPgOx6zGV5/HW6/XSmPGR6uJIZuPPulp6qzlyxZQlxcHLt372bDhg38/ve/x2azNWn30UcfMXv2bPeiMFAusqNHj2blypWtPt/8+fMJDAwkOzubzMxMPv74Y/fF+Xz59ttvyc7OJjs7m/fff5977rmnSRuHw8GDDz7I+vXr2bNnD0lJSW753xVXXEFGRgZ79uwhJiaGl15SllOp6uzmyRdCjAGkEEIrhHgIOLNVq5dQmZXFEXMMTpeWYWE2/HxdOJ1ODEZVi93hZGTAnDmwbJnSTXTbbZCaCi0YPrsbPUmdLYSguroaKSUmk4nAwMBm5+Wfrs4+fPgwJpOJhQsXkpraurLthw8fZsuWLSxcuBBN3c3VsGHD2rxieNWqVcydOxchBOPGjaOioqLJqvB6jUNNTQ1SSqqqqtx+pCuvvNL9nceNG0dBQYH7c6o6uyn3oHQhhQPHUVYfN03DvQSX1YYlJwe71caBIuVuLTnWzLHSIoz9NOh1esDetUH2dnx9lfKYMTHwzDNw2srM1lBa1tZlNk0JDrqsVe16mjr7vvvu45prrmHw4MFUV1ezfPly9wW7HpvNxpEjR4iIiHDvq6+9MGHCBA4cOEBJSQkDBrQ8RTszM5OUlJRGTxtn4sYbb2yqQAceeeQR5s6d22hfYWEhYWGnZsDXq7NDQkLc+zw8PHjnnXdITEzE29ub6Oho99NcQz766KNGT2KqOvs0pJQlKNNJ+wSO8kpqyo9iGeDPpkPBaNAQG2pC4qLA2Y+BHjVdHWLvJD0dkpOVJ4OhQ+HddyEu7rwrobX2At6e9FR19vfff09KSgrr1q3j8OHDXHHFFUyYMKHRquWysjL6nbZKPC0tjZUrV6LRaJg9ezb/+te/WLBgQbt915bGTU6nOV3P6eez2+2888477Nq1i2HDhnH//ffz0ksv8fTTT7vbvPDCC+h0Om655Rb3PlWdfRpCiA+EEO+f/uqM4LqKWp0WS9Awygr60V9oiBlqpsBhQ++hJznEhKcxtKtD7D2Ul8OTT8L8+fDNN6f2JyW1qTRmV9BT1dl///vfmT17NkIIoqKiiIyMbFRzGZTvZrFY3Nt79uwhOzubK664goiICNLS0txdSC2ps+Pj49m9e7dbwd0SN954Y7Pq7E8++aRJ29aos9PT0wHlKU0IwQ033MCmTZvc7//jH//gq6++4vPPP2/0c+tr6uzWjCn8AKyte20EBgCtXq/QY6ibdeREQ8GhA/zyXRkgGDHMiiZUj83u5LJ45VHUYFAL6bQZKZUkcP318N//KiuS7b2jW87f35/Fixfz2muvYbfbueWWW/j555/54YcfAOWJ4oEHHnB3Rzz66KO8+OKL7rt4l8vF66+/3uI5RowYQW5urrvw/KeffsqkSZMatRk7diwbNmzgxIkT2O12/vWvfzV7rPDwcNauVbrbjh8/zoEDB5oMdAcEBOB0Ot2JITU1leeee47c3Fxyc3MpKiqisLCQvLw8Ro8ezcaNGzl27BgA27dvx2q1EhYWxvDhwxk1ahR/+tOf3IktOzubVatWNYlr+fLl7trRDV+ndx0BXHPNNXzyySdIKdm8eTP+/v6Nuo4AQkNDycrKorS0FIA1a9a4pXHfffcdf/nLX1i9ejVeXo2nmh88eJCEhIRmf3a9kdZ0HzV6hhNCfAr0Pk1i3awjsyMT15FNHCiMQ+OhY1RCrbuJp6cnJlMXxthbOHYMXnwR6u/Sxo6Fp56C0+7sejIN1dm33norq1at4v7772fBggU4nU5uvfXWZtXZtbW1CCHOOvDaUJ3tcDgYPXp0i+rskJAQRo4c2Wwx+2eeeYZ58+aRmJiIlJK//OUvBAUFNWl35ZVX8vPPP3P55ZeTlpbGt99+2+j9WbNmkZaWxuOPP86bb77J1KlTcblc+Pj4kJqa6h6nWLp0Kb///e+JiorCy8uL/v37t0ph3RJTp07lm2++cR+z4bTalJQU0tPTGTx4MH/605+YOHEiHh4eDB061F1t7b777sNqtbq7/MaNG8e7774LqOrss39AiOHA91LKqI4JqWU6TJ194FuIvZqty5dRa3Jy/9vTOWnWserFUo478zjqsnL39Vdx4sQG+vefiFIuQuWcyciAe++F2lplQPmRR5RFaefZt15Pc6pglfZl165dvP7663z66addHUqnoaqzm0EIcZJTdlMNUA48ceZP9FzsVdVYCgsoMlyL2QIR4Q58PCwU2Rz0D40470FBlQbExCgm04gIePxxpRCOSo/gwgsv5JJLLsHpdLZq9lBvQFVnn4ZQroLJKEI7AJc810eLHoSltgYMBn7ODAXsJI+uwR5swKjVIrz6zkBTu+J0wvLlytOAnx/o9fDhh8rfVXoct9/et0q190V1dosDzXUJYKWU0ln36p0JIfsH0HhQVV1NjVXLhk12pISYuCocNhdHyi14GvRdHWXP4+BBZeHZ668rr3rUhKCi0m1pzeyjrUKIMxd37Q247LiGX8rhPTvYkt0fu11DfIQLLy8H5bXFaAweXBYXcvbjqCjYbPD223DrrbB/PwwaBFdd1dVRqaiotIIzdh8JIXRSSgcwHrhTCHEYqEEpICCllL0qUVgsFios5ezJuxgP9PxqdA0VwsXByhqGDIvt6vB6Dnv2wPPPQ26uMnh8ww1w333gpRplVVR6Ai2NKWwFRgIzOymWLkNKiam8nOMnTRw6HIZWJ5h8mZ2vi08yZpAXY+MH17XrvR7AdiE/X1mE5nIpq5KffVZZpayiotJjaKn7SABIKQ839+qk+Dqe7B+wlVZRuXETh3YMwOXSc0G0hVxXDUJKwsLCMBgUCV51dSYajZ7W9br1QcLCYNYsxWyamtrnEkJPVWefPHmSWbNmkZSUxJgxY8jIyGj2uFJKLr30Uqqqqtz7Vq5ciRCi0QroDRs2MH369EafnTdvHitWrAAU3cQTTzxBdHQ0CQkJjBkzpsmah/PhpZdeIioqitjYWL7//vtm26xdu5aRI0eSkpLC+PHj3Yv/3n33XRITE937s7KyAFWdfTrBQohHzvTqtAg7Gpcd28CRyKD+7ClNosZDT9DFtRw8XsJQvRNvb2/3VFQpXfj4xKpTU+upqlK6inbtOrXviSeUdQj6vjcw31PV2S+++CIpKSns2bOHTz75hAcffLDZdt988w3JycmNnEipqamMHz/+nCyizzzzDMXFxWRkZJCRkcGXX35JdXV1m75DVlYWaWlpZGZm8t1333Hvvfc2u1Dvnnvu4fPPPyc9PZ2bb76ZhQsXAnDzzTezd+9e0tPTeeyxx3jkEeUSp6qzG6MFfADfM7x6DSdNJ9lTfoDD2YOw2F0M9ztInMXGxMQIfOvKP1osRTgcVWc5Uh9i3Tr4zW9g9Wp45RVFWwFtXoTWW+hJ6uysrCwuu0wRCNbrM44fP96k3enqbJPJxMaNG/nwww9bnRRqa2v54IMPeOutt9xP4AMHDuSGNtbJWLVqFXPmzMFgMBAZGUlUVBRbt25t0k4I4X7SqaysdPuRGia6mpqaRjd+qjr7FMVSyuc7LZIu5Hh1CQU5EWhcXvQbVMWvkgyEuQZj1WgwGo04HNVUm/ZhMAzEwyOgq8PtWk6cgL/8RUkKACkpit66myWD/5ZVnr3ROXJlUOtKf/Y0dXZycjL/+c9/GD9+PFu3biUvL4+CggIGDmzs+Nq4cSPvvfeee/uLL75gypQpxMTEEBgYyM6dOxk5suX5J4cOHSI8PLzRRfhMPPzww6xfv77J/jlz5vDEE43XzxYWFjJu3Dj3dr06+3SWLl3K1KlT8fT0xM/Pj80NKvktWbKE119/HZvNxrr6f9+o6uyGdK//5R1B9g+YpIP86nyyD0wGIHREBUMjh2LdfxI/vwA0Gg0mUy4eOn/8fPuOFKsJUsLXXyvrDaqqlNlE998P110H3bASXWsv4O1JT1VnP/HEEzz44IOkpKSQmJjIhRde2OwK3vLycveTMyhdR/XnmzNnDqmpqYwcObLdvuuiRYta3bY16uz6Y37zzTeMHTuWV199lUceeYSlS5cCsGDBAhYsWMCyZctYuHAh//jHP4C+p85uKSl0vpC+s3HZsYX9GkP2F2RmhuBwOhkSXwn44ZIufH19MZvzsdlK6ddvdFdH27VUV8OiRUpC+NWv4I9/hBB17UZD6scUKisrmT59OkuWLOGBBx4gPj6eH3/8sVHb5tTZyecwMN+e6mw/Pz+3QE5KSWRkJJGRkU3a6XQ6XC4XGo2GEydOsG7dOjIyMhBC4HQ6EULwyiuvtKjOjoqK4ujRo1RXVzdKMM1xLk8KrVFnl5aWsnv3bsaOHQsoSXLKlCnNHr9hOU9VnV2HlLK8MwPpKmw2GwW5PhQec2D0NBM6zKC4XTRaDAYDNtsJvL2j0Ol61TBK63C5FE0FKKuQn3pKGVh+8001IbRAT1NnV1RUuGsyL126lIkTJzbbvRMbG8uRI0cAWLFiBXPnziUvL4/c3Fzy8/OJjIzk559/Jjo6mqKiIvbt2wdAXl4eu3fvJiUlBS8vL+644w4eeOAB9zmLi4v57LPPmpxv0aJFzaqzT08IoKiz09LSsFqt5OTkkJ2dzZgxYxq1CQgIoLKy0v1zbqjOzs7Odrf7+uuvG6ktVHV2H8N68iT7tvkjgbhkDcNDvLDZbPj5nJp1pNX2wYVXubnwf/+nPBXU9Y1z6aVdGlJPoieps/ft28fcuXPRarXExcXx4YcfNnvOadOmsWHDBqKiokhNTW1ycb7uuutYtmwZEyZM4LPPPuO3v/0tFosFDw8Pli5dir+/0qW3cOFCnn76aeLi4jAajXh7e/P8820bvoyPj+eGG24gLi4OnU7HkiVL3NK+qVOnsnTpUgYPHswHH3zAddddh0ajISAggI8++giAv/3tb/zwww94eHgQEBDg7joCVZ3d7WlXdfaBb8myB/G72wzklQzmjgUniZ6lZZTGySCzH75JA6msTMfTcwh6fR+xeToc8Mkn8MEHStGbwYNhxYpuP8VUVWd3PMXFxcydO5c1a3pfOZUz0RfV2d1vhLCT+SU7l9ziIJzeOoZcIRAuFwaDAZ1Oh81WhsNRSV8YcwfgwAGYO1fxFtntcO218Nln3T4hqHQOISEh3HnnnY0Wr/V2VHV2H8Nit5KxIxiBhoTRLq4a5EltbS0+JzS4PK2YTTno9cG9fxqqwwHvvQf/+IcyjjB4MDz9NJzWJ6ui0tb1BD2NvqjO7tNJIctUSN5BZUpf3Dg7Qgg0BVY8BvjjCClAuHR4e0cjRC9/oNJqlYpoUsJNN8E996gCOxWVPkqfTgp2h52iQ8GAk/ARNpxO0AktvvEDKCvLJCBgHBqNR1eH2THU1kJNDQQHKwvPnnkGysogKamrI1NRUelCevktcMscL9JzskyDy0cQMsSBzWZrNB9Zkd/1Qn75RVFaP/30KT3F4MFqQlBRUembTwpVJ8yc3LGRPbvCsNhtBMaYmeBrpLa2FoPBQE3NIZSy1L1sgLmyUlmR/PXXynZAgLKvX7+ujUtFRaXb0KFPCkKIKUKIA0KIQ0KIJitO6oyrWUKIPUKItUKIoR0ZTz3lRTV4edk4ahsGQnLRr33dK0SlpgKzOR9f34TeM5YgJaxdqwjsvv5amU30wAPw8cdqQmhnjh07xpw5cxg+fDhxcXFMnTqV999/v4lKWkWlu9JhVz0hhBZYAlwNxAE3CSHiTmu2CxglpUwCVgCvdFQ8p+NpP0HRDhcAYTE27HY73t7euLBiMIZgMAzorFA6FimVbqLHH4fychg5EtLSlKmndYt7VNoHKSWzZs1i8uTJHD58mKysLF588cVmjaMqKt2VjrwVHgMcklIekVLagDTg2oYNpJTrpZS1dZubgSEdGE8jzDUW9hUGIXU6wmId2O12fHx8AFDyWS9BCBg2TJlN9Mc/wrvvQnh4V0fV8YwadebXf/5zqt1//tNy23Ng/fr1eHh4NFpZnJKSwoQJEzCZTFx//fWMGDGCW265xf1k+vzzzzN69GgSEhK466673PsnT57M448/zpgxY4iJieGnn34CFAPrH/7wBxITE0lKSuKtt94CYMeOHUyaNImLLrqIq666iuLi4rb89FT6MB2ZFEKB/AbbBXX7zsQdQLPll4QQdwkhtgshtpeWlrZLcIfyfXA4tAwaYMbbz1V3nkocrl6wMKeoCBq65G+7TVmV3E2Npr2FjIyMJorsenbt2sUbb7xBVlYWR44ccdc2uO+++9i2bRsZGRmYzWa++uor92ccDgdbt27ljTfe4M9//jMA77//Pjk5OezatYs9e/Zwyy23YLfbuf/++1mxYgU7duzg9ttv56mnnur4L6zSK+nIgebmRmmbdWoIIf4fMAqY1Nz7Usr3gfdB0Vy0R3AZOUpfenhYOS6nLwZvAxZLNhrhiUEf3B6n6HxcLli+HJYsAYMB/vUvCAwEnQ4G9JLusNbSWhXK7NnKq4MZM2YMQ4YoD8L1eu3x48ezfv16XnnlFWpraykvLyc+Pp4ZM2bUhabEddFFF5GbmwvADz/8wO9+9zv3CtvAwEB3BbN6VbfT6SREFRaqnCcdmRQKgLAG20OAJlJyIcTlwFPAJCll09JRHcS+nECkw0F4eCV2hyda7QlshdUEGBLx8OiBg69HjsDChbBnj7I9caL6VNDJxMfHu+sQn059lTFQajk7HA4sFgv33nsv27dvJywsjOeeew6LxdLkM/XtofnaDFJK4uPj+eWXX9r7K6n0QTryqrENiBZCRAoh9MAcYHXDBkKIC4H3gGuklCUdGEsT9h/tDxLiL9KDlOj1Am9NLJ4jepj4zuGADz+EW25REkJwsDLt9MUX1ZlFncyll16K1Wrlgw8+cO/btm0b//vf/5ptX58AgoKCMJlMZ0woDbnyyit599133UmivLyc2NhYSktL3UnBbreTmZnZ1q+j0kfpsKQgpXQA9wHfA/uAf0opM4UQzwshrqlr9ipKHeh/CSHShRCrz3C4dqUqey9Hjvug1ToZEmZGSjsajZ0euZbvqafgnXcUgd2sWUqX0cSJXR1Vn0QIwcqVK1mzZg3Dhw8nPj6e5557rkmxl3r69evHnXfeSWJiIjNnzmT06LMXcpo/fz7h4eEkJSWRnJzMsmXL0Ov1rFixgscff5zk5GRSUlLYtGlTe389lT5Cn1Rnr3htM0+8NYyBgwpZ8plgj9XGFFcNPvoL8BoxqJ0i7STS0+HPf4Ynn4RWXFR6K6o6W0XlFKo6+xzZn+sDEoZEm9lS48TLoEeLT89ICDt3wvvvn9pOSVFmFvXhhKCiotJ+9EnNRd4RQa2AmqFWcGn4lXcpGnM3n61RUwOLF8O//61sjxqlLEQDdRGaiopKu9HnkoK9pITycg1SoyEuzMglPkaMxgCMuk5bN3fubNwIL7wAJSXK9NLbb4fExK6OSkVFpRfS55KCY+tqSq2XYZcSPz8HRqMRobF3dVjNU1EBf/0rfFu3pi8+Hp59FoYP79q4VFRUei19LikcLSiipFqPRmiYER+Kt7ceqO7qsJrngw+UhGAwwL33KgVw1LUHKioqHUjfSgrZP1Bt9cAhvdEawMvLhV6vpTanCk9DN9FkS6n4igDuvluR2C1YAEO6cfeWiopKr6Fv3Xa67BRZR4CQGH3tOJ1FmM0ZaKQRY2xg18YmJaxcqYwX2GzKPj8/eOklNSGoqKh0Gn3rSQGoqNFjddjx9zMjxHF8fEYhPPy6NqiCAkVRUb/+Ys0amData2NSUVHpk/StJwWg0mRASggJ0eDvH4WXV2TXBeNyweefw403KgkhIEDRU0yd2nUxqbQJIQS33nqre9vhcBAcHNzhRXa0Wi0pKSkkJCQwY8YMKioq3O8VFBRw7bXXEh0dzfDhw3nwwQex1T+N0nxhoIMHDzY5h9lsZtKkSTidTve+lStXIoRg//797n25ubkkJCQ0+uxzzz3Ha6+9dk7nO1e+++47YmNjiYqK4uWXX262zaJFi4iPjychIYGbbrqpkWsKFJnghRde2G6/r9bE1FKb22+/nQEDBjT6edpsNiZOnOhWnbQ3fS4pHDkuAImPvwMPD48uDOQI/Pa3sGgRWK1w9dWKouLKK0+NKaj0OLy9vd0abIA1a9YQGtqSMb598PT0JD09nYyMDAIDA1myZAmgyPJmz57NzJkzyc7O5uDBg5hMJrda+1wKA3300UfMnj0bbYN1MampqYwfP560tLRWxdlRhYicTicLFizg22+/JSsri9TUVLKyshq1KSwsZPHixWzfvp2MjAycTmeTuN98881WrYzfsGED8+bNa3NMZ2szb948vvvuu0af0ev1XHbZZSxfvvyscZ4Pfa77qMpkQKcVePezN/rH3ens3w+ZmYrS+sknYfz4roull3GOtXFaTWvtKldffTVff/01119/Pampqdx0003uIjmfffYZixcvxmazMXbsWN5++220Wi0zZ84kPz8fi8XCgw8+yF133UVubi5XX30148ePZ9OmTYSGhrJq1So8PT1bPP/FF1/Mnjpb7rp16zAajfz2t78FlCeKRYsWERkZyZ///Gc2b97cbGGg5vj8889ZtmyZe9tkMrFx40bWr1/PNddcw3PPPXfWn82ZChG1la1btxIVFcWwYcMAmDNnDqtWrSIurnGxR4fDgdlsxsPDg9ra2kZeqoKCAr7++mueeuopXn/99U6J6WxtJk6c6NamN2TmzJn88Y9/5JZbbmlznKfT554UqmsUHbFPgBVHvglz1gmEtpPuzE+ePPX3q6+Gxx6Df/5TTQi9jDlz5pCWlobFYmHPnj2MHTsWUHw0y5cvZ+PGjaSnp6PVavn8888B5S58x44dbN++ncWLF3PixAkAsrOzWbBgAZmZmfTr149/169oPwNOp5O1a9dyzTWKczIzM7NJ4R8/Pz/Cw8M5dOhQi4WBGmKz2Thy5AgRERHufV988QVTpkwhJiaGwMBAdu7cedbjtPZ8ABMmTCAlJaXJ64cffmjStrCwkLCwU6b+IUOGUFhY2KhNaGgof/jDHwgPDyckJAQBRlnEAAAT6klEQVR/f3+uvPJK9/sPPfQQr7zyCpoWpn2PHTuWlJQU5s+fz+rVq90xff/99+cVU2vaNEdCQgLbtm07a7vzoU89KeRk51FaE4RA4u1biY7+eMb17/gTWyxKGcwVK+DTTyEyUukiuuGGjj93H6SNvsQ2k5SURG5uLqmpqUxtMD60du1aduzY4bahms1mBtQVP1q8eDErV64EID8/n+zsbAYNGkRkZKT7TrphsZ3TMZvN7uI9F110kbvgTnP1F1rafybKysrod5qKPTU1lYceeghQEmFqaiojR44843HP5XyA++mqNTQn9vz/7d15dFXVvcDx7w/IQADDpF1qAFMmIQyBUMC65KEgIvBUMBVYTYGnPqrIc6Dhoe3rAvuwrYKrlgpSqi6koKA8RVSsr1RSLTLIVAggr0ySLCyBMMUQMt3f++OcXG5ChpPhZrj391nrrnXvufue/dsZ7u/uvc/du2x9586d4/333+fYsWO0bduWH/zgB6xcuZKUlBQ+/PBDrrvuOpKSkkhLS6uwnm3btgHO8NHy5ctZvnx5rWLyUqY8zZs3JzIykpycHNq0aVNl+eoIq6SQcS6PvMJYolpEEtuxBVHN62E3sh07nCuLMjOdL57t3u0kBRPS7rnnHlJTU0lLS/N/6ldVpk6dyq9+9atSZdPS0ti4cSNbtmwhJiaG4cOH+ydAy27OUzJXUVbJnMKFCxcYN24cixcv5vHHHychIeGq3sXFixfJyMiga9euZGVledrHoWXLlqUmZbOzs/n0009JT09HRCguLkZEeOGFF+jQoQPnAnvFOPs+xMfHExcX56k+cHoKOTlXf7F04cKFjBw5stSxuLg4MjKu7P6bmZl51ZLlGzduJD4+nmuvdXZWnDBhAl988QUpKSls3ryZ9evXs2HDBi5fvszFixdJSUlh5cqVnmItj5eYvJSpSH5+PtHR0TWOr0Kq2qRuSUlJWhOFBUX6P4te0D69MrRn97P6m3c/1byvsmt0Lk9yclSfe041Kcm5TZyoun9/8OoLcwcOHGjoEFRVtVWrVqqqmpGRoS+99JKqqm7atEnHjh2r+/fv127duumpU6dUVTU7O1uPHz+u69at03Hjxqmq6sGDBzUqKko3bdqkx44d04SEBP+5FyxYoHPnzq20XlXVXbt2aadOnbSgoEB9Pp8mJSXpG2+8oaqqRUVF+vDDD+usWbNUVdXn8+ngwYN12bJl/tdv375d09LSrqojLi5O8/LyVFV16dKlOn369FLPDxs2TD/77DNVVU1KStKNGzf629m9e3c9fPhwteqrjsLCQo2Pj9ejR49qfn6+9uvXT9PT00uV2bp1q/bu3Vtzc3PV5/PplClTdNGiRVedq+T3VVteYvJSpuzfgarqmTNn9Oabb66w7vL+H4Ad6uE9NmzmFIoKism7mEfepQiaN2tGbNvi4H1hbc8eZ2jo3XedBeweecQZNioz6WVCV1xcHE888USpY71792b+/PmMGjWKfv36ceedd/LNN98wevRoioqK6NevHz//+c8ZOnRoreoeMGAA/fv3Z/Xq1f6Nf9555x26d+9Ojx49iI6O5pe//CVQvY2BRo0axd/+9jfAGToaP358qefvv/9+/0T0ihUrmD9/PomJidxxxx3MnTuXrl27VnsjIq9atGjByy+/zF133UWvXr144IEHSEhIAGDMmDGcPHmSIUOGkJyczMCBA+nbty8+n4/p06dXq56SOYWyt/LmFLzEVFkZgMmTJ3PLLbdw6NAh4uLieO211wBnwn5MsC5d95I5GtOtpj2F3FNn9Q//+QuN73pWvzfovK7cvLFG5/HkxAnVW25RnTZN9ciR4NVj/BpLTyGU7dq1S1NSUho6DKOq48eP16+++qrC52vTUwifOQVVcvNb4YtoQdt2hYjUYSdJFbZtgyFDnAnkTp2cfZN79rQF7EzIGDBgALfffjvFxcUNezl3mCsoKOC+++6jZ8+eQTl/WL1j5VyKAYXCyEIi6uoy1FOn4KmnYOZM+OCDK8d79bKEYELOgw8+aAmhgUVGRjJlypSgnT98ego4SeEyENPmIiO6xdTuZD4frFsHL70Ely5B69bQkN+QNsaYOhBWSeHbvBjQYnq3PUdsbP+an+jECecy05Iv6wwfDnPmgHupmzHGNFVhlRQO63cQfFzXti3Nm0dV/YLy7N3rXE1UUADt2zvfSh4xwtYrMsaEhLBKCrmXWhJFER3b1eIkvXpB587OJPKsWRAbW2fxGWNMQwurpJCXGw0KHdr7vL+ooABWroQJE6BtW2fe4PXXIaaWcxLGGNMIhdXlMXm50SjQsd3V642Ua98+SEmBJUvgxRevHLeEYIwJUeHVU7gUSYtiH9d2rOKSurw8eOUVeOst5zsInTs7PQVjjAlxYZMUCgqKyb8cQasWzYkb3KXigtu3O1cWnTzpfM9g6lSYPh0iI+svWFNrGRkZ5Ofn19n5oqKiSi1xXBcefPBB/+qc6enpnl93/vx53nzzTWbMmFHu8/PmzaN169akpqZ6Ol91y5vQFjbDR9nnLgNKq1Y+IiIq6CmcOAGPPeYkhB49YMUK50tplhCanPz8fGJiYursVt0E42VnrvJ21fLi/PnzLFmypNqvM8aL8EkKZ5sDQuvWvoq/kdm5M0yeDDNmOAnh5pvrNUYTXoYNG0b79pUvypibm8vYsWPp378/ffr0Yc2aNTz99NMcOXKExMREZs+eDcBzzz1Hz549GTlyJIcOHaqy7srKr1y5ksGDB5OYmMiPf/xjiouLmTNnTqlENG/ePF4MnGczISNsho+yzzYDlDZtCq9sYnH2LCxYAPfff2UPx1mzGixG0/QNGTKE/Px8vv32W86ePevfIOf555/nrrvuqvb5/vSnP3HDDTfw0UcfAXDhwgWGDBlCeno6e/bsAWDnzp2sXr2a3bt3U1RUxMCBAyvd3ayy8oG7w0VERDBjxgxWrVrFpEmTePLJJ/1DVm+//XaNejmm8QubpHD2XDNQH7GxRc7k8ccfw8KFcPEifP01rFplX0AztVadnbm86Nu3L6mpqcyZM4dx48Zx2223XbWBzeeff8748eOJca+KK9mKsyKVla9od7gpU6aQlZXFyZMnOX36NO3ataNz5861aptpnIKaFERkNPBboDnwqqr+uszzUcAKIAnIBiaq6vFgxHLqjA8FunUphCeegC++cJ4YOhR++lNLCKZR6tGjBzt37mTDhg0888wzjBo1qtzF0Kq71WVF5bWC3eEAkpOTWbt2Lf/85z+ZNGlSteozTUfQ5hREpDmwGLgb6A1MFpGyu8w8BJxT1W7Ab4DngxVP9lloeekSvdatchLCNdfAvHnwu99BLTf4MKas4cOH17qXAHDy5EliYmJISUkhNTWVXbt20aZNm1LbVA4bNoz33nuPvLw8cnJy+CBwtd5yVFZ+xIgRrF27lqysLMDZRvPrr78GnH2YV69ezdq1a0lOTq5120zjFMyewmDgsKoeBRCR1cC9wIGAMvcC89z7a4GXRUTcDSHq1PkspdW3uXSIPQ133OEsYNehQ11XYxqJqKgoLl26VKfn86JkTqGs8uYUJk+eTFpaGmfOnCEuLo5nn32Whx56qFSZffv2MXv2bJo1a0ZERASvvPIKHTp04NZbb6VPnz7cfffdLFiwgIkTJ5KYmEiXLl247bbb/K8fM2YMr776aqmdzQYOHFhh+cDd4Xw+HxERESxevJguXbqQkJBATk4ON954I9dff32ldZimS4Lw/uucWCQZGK2qD7uPfwQMUdWZAWXS3TKZ7uMjbpkzZc41HZgO0Llz56SSTy7VkfqTS3zw9kn+OCeLwTO/X9NmmUbq4MGD9OrVq6HDMKZRKO//QUR2quqgql4bzEtSyxu0LJuBvJRBVZep6iBVHXRtDZenXvhiDIcyuvG9xywhGGNMRYKZFDKBwK+AxgEnKyojIi2AWOBsEGOy+WRjjKlEMJPCl0B3EYkXkUhgErC+TJn1wFT3fjLwaTDmE0x4sD8dY2r/fxC0pKCqRcBM4BPgIPC2qu4XkV+ISMmF0a8BHUTkMDALeDpY8ZjQFh0dTXZ2tiUGE9ZUlezsbKKjo2t8jqBNNAfLoEGDdMeOHQ0dhmlkCgsLyczM5PLlyw0dijENKjo6mri4OCLK7BnvdaI5bL7RbEJbREQE8fHxDR2GMU1e2CyIZ4wxpmqWFIwxxvhZUjDGGOPX5CaaReQ0UP2vNDs6AmeqLBVarM3hwdocHmrT5i6qWuW3f5tcUqgNEdnhZfY9lFibw4O1OTzUR5tt+MgYY4yfJQVjjDF+4ZYUljV0AA3A2hwerM3hIehtDqs5BWOMMZULt56CMcaYSlhSMMYY4xeSSUFERovIIRE5LCJXrbwqIlEissZ9fpuI3FT/UdYtD22eJSIHRGSviPxFRLo0RJx1qao2B5RLFhEVkSZ/+aKXNovIA+7ver+IvFnfMdY1D3/bnUVkk4jsdv++xzREnHVFRF4XkSx3Z8rynhcRWeT+PPaKyMA6DUBVQ+oGNAeOAN8FIoG/A73LlJkBLHXvTwLWNHTc9dDm24EY9/6j4dBmt1wb4DNgKzCooeOuh99zd2A30M59fF1Dx10PbV4GPOre7w0cb+i4a9nmYcBAIL2C58cAH+PsXDkU2FaX9YdiT2EwcFhVj6pqAbAauLdMmXuBN9z7a4ERIk16T7Yq26yqm1S1ZCf7rTg74TVlXn7PAP8NvACEwpraXtr878BiVT0HoKpZ9RxjXfPSZgWuce/HcvUOj02Kqn5G5TtQ3gusUMdWoK2IXF9X9YdiUrgRyAh4nOkeK7eMOpsBXQA61Et0weGlzYEewvmk0ZRV2WYRGQB0UtUP6zOwIPLye+4B9BCRzSKyVURG11t0weGlzfOAFBHJBDYA/1E/oTWY6v6/V0so7qdQ3if+stfdeinTlHhuj4ikAIOAfwlqRMFXaZtFpBnwG2BafQVUD7z8nlvgDCENx+kNfi4ifVT1fJBjCxYvbZ4MLFfVF0XkFuCPbpt9wQ+vQQT1/SsUewqZQKeAx3Fc3Z30lxGRFjhdzsq6a42dlzYjIiOBnwH3qGp+PcUWLFW1uQ3QB0gTkeM4Y6/rm/hks9e/7fdVtVBVjwGHcJJEU+WlzQ8BbwOo6hYgGmfhuFDl6f+9pkIxKXwJdBeReBGJxJlIXl+mzHpgqns/GfhU3RmcJqrKNrtDKb/HSQhNfZwZqmizql5Q1Y6qepOq3oQzj3KPqjblvVy9/G2vw7moABHpiDOcdLReo6xbXtp8AhgBICK9cJLC6XqNsn6tB6a4VyENBS6o6jd1dfKQGz5S1SIRmQl8gnPlwuuqul9EfgHsUNX1wGs4XczDOD2ESQ0Xce15bPMCoDXwjjunfkJV72mwoGvJY5tDisc2fwKMEpEDQDEwW1WzGy7q2vHY5p8AfxCRp3CGUaY15Q95IvIWzvBfR3eeZC4QAaCqS3HmTcYAh4FLwL/Vaf1N+GdnjDGmjoXi8JExxpgasqRgjDHGz5KCMcYYP0sKxhhj/CwpGGOM8bOkYBodESkWkT0Bt5sqKXtTRatJVrPONHclzr+7S0T0rME5HhGRKe79aSJyQ8Bzr4pI7zqO80sRSfTwmidFJKa2dZvwYEnBNEZ5qpoYcDteT/X+UFX74yyWuKC6L1bVpaq6wn04Dbgh4LmHVfVAnUR5Jc4leIvzScCSgvHEkoJpEtwewecissu9fb+cMgkist3tXewVke7u8ZSA478XkeZVVPcZ0M197Qh3nf597jr3Ue7xX8uV/SkWusfmiUiqiCTjrC+1yq2zpfsJf5CIPCoiLwTEPE1EflfDOLcQsBCaiLwiIjvE2UfhWffY4zjJaZOIbHKPjRKRLe7P8R0RaV1FPSaMWFIwjVHLgKGj99xjWcCdqjoQmAgsKud1jwC/VdVEnDflTHfZg4nAre7xYuCHVdT/r8A+EYkGlgMTVbUvzgoAj4pIe2A8kKCq/YD5gS9W1bXADpxP9Imqmhfw9FpgQsDjicCaGsY5GmdZixI/U9VBQD/gX0Skn6ouwlkX53ZVvd1d+uK/gJHuz3IHMKuKekwYCbllLkxIyHPfGANFAC+7Y+jFOGv6lLUF+JmIxAHvquo/RGQEkAR86S7v0RInwZRnlYjkAcdxll/uCRxT1f9zn38DeAx4GWd/hldF5CPA89LcqnpaRI66a9b8w61js3ve6sTZCmfZh8Bdtx4Qkek4/9fX42w4s7fMa4e6xze79UTi/NyMASwpmKbjKeAU0B+nh3vVpjmq+qaIbAPGAp+IyMM4ywy/oarPeKjjh4EL5olIuXtsuOvxDMZZhG0SMBO4oxptWQM8AHwFvKeqKs47tOc4cXYg+zWwGJggIvFAKvA9VT0nIstxFoYrS4A/q+rkasRrwogNH5mmIhb4xl0j/0c4n5JLEZHvAkfdIZP1OMMofwGSReQ6t0x78b4/9VfATSLSzX38I+Cv7hh8rKpuwJnELe8KoByc5bvL8y5wH84+AGvcY9WKU1ULcYaBhrpDT9cAucAFEfkOcHcFsWwFbi1pk4jEiEh5vS4TpiwpmKZiCTBVRLbiDB3lllNmIpAuInuAm3G2LDyA8+b5vyKyF/gzztBKlVT1Ms4KlO+IyD7AByzFeYP90D3fX3F6MWUtB5aWTDSXOe854ADQRVW3u8eqHac7V/EikKqqf8fZm3k/8DrOkFSJZcDHIrJJVU/jXBn1llvPVpyflTGArZJqjDEmgPUUjDHG+FlSMMYY42dJwRhjjJ8lBWOMMX6WFIwxxvhZUjDGGONnScEYY4zf/wPAKjyf2+9VfAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.27145401, 0.72216815, 0.89101219, ..., 0.1970748 , 0.64862049,\n", + " 0.88528073])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Logistic regression (SGD)\n", + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " for mod in range(5):\n", + " print('>>')\n", + " curr_try = 0\n", + " while curr_try <10:\n", + " print('.')\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(1, activation='sigmoid'))\n", + " # Compile model\n", + " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", + " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + " \n", + " # Fit the model\n", + " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", + " if history.history['acc'][-1] > 0.53:\n", + " break\n", + " else:\n", + " curr_try += 1\n", + "\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", + " \n", + " # evaluate the model\n", + " probas_ = model.predict(X[test,:])\n", + " prbs.append(probas_)\n", + " # Average the predictions\n", + " probas_ = np.mean(np.hstack(prbs), axis=1)\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr.v4.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAEJVJREFUeJzt3X+MZeVdx/H3t2wRsSA/dsANyzg02WoJiSmZELRJXbuNAWzY/gEEtO2CGzepFbVtFNQ/mKp/UH9RmjTgCtjFVH4UG9kYtDFbVtTIxqVUyo+QrhSXLSu7tYAaVMB+/eOexSk7s3PmnnPvPee571cymXvPPfee55k7872f85znnInMRJJUrrdMugGSpNGy0EtS4Sz0klQ4C70kFc5CL0mFs9BLUuEs9JJUOAu9JBXOQi9JhVsz6QYArF27Nufm5ibdDEnqlUceeeRbmTmz0nqdKPRzc3Ps3bt30s2QpF6JiH+ps55DN5JUOAu9JBXOQi9JhbPQS1LhLPSSVDgLvSQVzkIvSYWz0EtS4Sz0klS4TpwZ23sLC0vflqQOMNFLUuEs9JJUOAu9JBXOQi9JhbPQS1LhLPSSVDgLvSQVzkIvSYWz0EtS4VYs9BFxR0QciojHFy07LSL+OiK+Xn0/tVoeEfGZiNgXEY9FxPmjbLwkaWV1Ev3ngIvetOx6YFdmbgB2VfcBLgY2VF/bgFvaaaYkaVgrFvrMfAj49psWbwZ2VLd3AB9YtPzOHHgYOCUi1rXVWEnS6g07Rn9mZh4EqL6fUS0/C3hu0XoHqmVHiYhtEbE3IvYePnx4yGZIklbS9sHYWGJZLrViZm7PzPnMnJ+ZmWm5GZKkI4a9TPELEbEuMw9WQzOHquUHgLMXrbceeL5JA3tnucsUe/liSRMybKLfCWypbm8B7l+0/MPV7JsLgZePDPFIkiZjxUQfEXcBG4G1EXEAuAG4Ebg3IrYC+4HLq9UfAC4B9gGvANeMoM3j5T8VkdRzKxb6zLxqmYc2LbFuAh9t2ihJUns8M1aSCmehl6TCWeglqXAWekkq3LDz6DWKGTirneHjjCBJNZjoJalwFnpJKpyFXpIKZ6GXpMJZ6CWpcBZ6SSqchV6SCuc8+tVwrrqkHjLRS1LhTPST4BmtksbIRC9JhbPQS1LhHLqR1DuOfq6OiV6SCmeiL5FxR/LPYBETvSQVzkQvqRemPZU3YaKXpMKZ6CWpRV08NmCil6TCTXei7+JH70r60k6pEH0sE29mopekwlnoJalwFnpJKlyjQh8RH4uIJyLi8Yi4KyJOiIhzImJPRHw9Iu6JiOPbaqwkafWGLvQRcRbwi8B8Zp4HHAdcCXwKuCkzNwAvAlvbaKgkaThNZ92sAb43Il4DTgQOAu8Ffrp6fAewANzScDvl6uthfKkjujArput/xkMn+sz8JvB7wH4GBf5l4BHgpcx8vVrtAHBW00ZKkoY3dKKPiFOBzcA5wEvAF4CLl1g1l3n+NmAbwOzs7LDNkNRxXUjc067Jwdj3Ad/IzMOZ+RrwReDHgFMi4sgHyHrg+aWenJnbM3M+M+dnZmYaNEOSdCxNxuj3AxdGxInAfwGbgL3Ag8BlwN3AFuD+po2UpFErec+jyRj9HuA+4CvA16rX2g5cB3w8IvYBpwO3t9BOSdKQGs26ycwbgBvetPgZ4IImrztxo/g4H3VEKC2CSGqNZ8ZKUuGm++qVXWZC1xTp2vh4F9rQJhO9JBXORC+pGKUl8baY6CWpcNOX6P3IlzRlTPSSVLjpS/SSesMd8HaY6CWpcBZ6SSqchV6SCmehl6TCeTBW0kR4oHV8TPSSVDgT/RGlxouuXS1KrfGtnaw+/cxN9JJUOAu9JBXOQi9JhXOMXt/NgV9NmL927TPRS1LhTPRSwdxBO9o0/kxM9JJUuOlI9NPysS21aBTJt+9/in1tv4lekgo3HYlekpbQ14S+WiZ6SSqciX5aTePUA2lKmeglqXAmemkKuUM3XUz0klS4Rok+Ik4BbgPOAxL4WeBp4B5gDngWuCIzX2zUSk2GsU9qpCt/Qk0T/c3AX2XmDwM/AjwFXA/syswNwK7qviRpQoZO9BFxMvAe4GqAzHwVeDUiNgMbq9V2ALuB65o0UpKamPYd0iaJ/u3AYeCPI+LRiLgtIr4PODMzDwJU389ooZ2SpCE1GaNfA5wPXJuZeyLiZlYxTBMR24BtALOzsw2aobHrysCjOsVfi+5qkugPAAcyc091/z4Ghf+FiFgHUH0/tNSTM3N7Zs5n5vzMzEyDZkiSjmXoQp+Z/wo8FxE/VC3aBDwJ7AS2VMu2APc3aqEkqZGmJ0xdC3w+Io4HngGuYfDhcW9EbAX2A5c33IYkqYFGhT4zvwrML/HQpiavK2kyHFsvk2fGSlLhLPSSVDgvaqb21Jlf5xy8qeBb2y0mekkqnIVekgpnoZekwjlGr+5xHF9qlYlekgpnotdomMqLstzb6VvbDyZ6SSqciX6adC1+GQ17ybeqf0z0klQ4E73qRTRjnNRbJnpJKpyFXpIKZ6GXpMKVO0bvmLL0XfyTmF4mekkqXLmJXiqQE6T6a5KnjZjoJalwFnpJKpyFXpIK5xi9mmlrsNGB5Ub88elYTPSSVDgTvbqtL1e47Es7l9Cz5moIJnpJKpyJXmpoYQHYvXHRgkXLpQ4w0UtS4Uz00gT1eGhfPWKil6TCNU70EXEcsBf4Zma+PyLOAe4GTgO+AnwoM19tuh31WIeiqgla06iNoZtfAp4CTq7ufwq4KTPvjohbga3ALS1sRxqLUX8Y+AGjcWs0dBMR64GfAm6r7gfwXuC+apUdwAeabEOS1EzTRP9p4FeBk6r7pwMvZebr1f0DwFlLPTEitgHbAGZnZxs2Q+o/h5U0KkMn+oh4P3AoMx9ZvHiJVXOp52fm9sycz8z5mZmZYZshSVpBk0T/buDSiLgEOIHBGP2ngVMiYk2V6tcDzzdvZk3GoLIZeaWhDJ3oM/PXMnN9Zs4BVwJfzsyfAR4ELqtW2wLc37iVkqShjeKEqeuAuyPit4FHgdtHsA1Nu+XSfSGpv5BuqCNaKfSZuRvYXd1+BrigjdeVJDXnJRCkYxhcsGz34PbG3UPH6yap3ESvprwEgiQVzkSvqTX2cfBqzwCAjRvHsEFpwEQvSYUz0asYo/4HIAu7N77xmrXbI3WAiV6SCmeiV2ctLErnCxt3H2PFhcH3xWm+rjdm1OC4uYplopekwpno9YbaCVpSr5joJalwFnpJKpyFXpIK5xi9psPimTl1ZtcsPot1tUZ9Bqxn2GqVTPSSVDgTvcp1JPku7J5kK6SJM9FLUuFM9Ood5/sv0mS83rH+qWGil6TCmei1pFGk5oVlrkUz6lR+1HabzKiReshEL0mFM9FrpJZL8X15/ZFZ7fi4eyFqwEKvoRV3ULTUYupB16nn0I0kFc5EX5jiUnaX1dkDKHUvQb1iopekwpnop1xvD2ZOgy7vDTju3ysmekkqnIlerWhybKBXxxX6krKlRUz0klS4oRN9RJwN3An8APAdYHtm3hwRpwH3AHPAs8AVmfli86ZOp3FeimCcbdAyupDKHX8vTpNE/zrwicx8J3Ah8NGIOBe4HtiVmRuAXdV9SdKEDJ3oM/MgcLC6/R8R8RRwFrAZ2FittgPYDVzXqJXqFWfy9NByexJNLtXg3kBntDJGHxFzwLuAPcCZ1YfAkQ+DM9rYhiRpOI1n3UTE24A/A345M/89Iuo+bxuwDWB2drZpM3pjrJf/dWy9H7owLj8K/pP0zmiU6CPirQyK/Ocz84vV4hciYl31+Drg0FLPzcztmTmfmfMzMzNNmiFJOoYms24CuB14KjP/YNFDO4EtwI3V9/sbtVBSt5W6R1KQJkM37wY+BHwtIr5aLft1BgX+3ojYCuwHLm/WRElSE01m3fwdsNyA/KZhX1fLc8xd0jA8M1aSCue1bjpoFPPQ+zK3vc4MIvWcs2XGzkQvSYUz0U9QW1d8lMZiUrNr3ANozEQvSYUz0dfkjBepJ9wDOIqJXpIKZ6IfkdXuATjmLtVQ5yqbPbCwsPTtUTHRS1LhTPTSNOnCzJk66zi23ioTvSQVzkQ/BMffpZ5wLwEw0UtS8Uz0LTK5S6vUhWMGU/C/cC30laWGYyzcR/Nnot7q2RTMNjl0I0mFKzbRe8kCSbX0fFimDhO9JBWu/4l+lecPm/SlHujCeHoX2tASE70kFa7/iV6S2rLaFN+T8X0TvSQVzkTfkPPKJXWdiV6SCmehl6TCWeglqXCO0S/BcXdJJTHRS1LhLPSSVDgLvSQVbiRj9BFxEXAzcBxwW2beOIrt1LXcmLtj8ZJaU+es2gmdPdt6oo+I44DPAhcD5wJXRcS5bW9HklTPKBL9BcC+zHwGICLuBjYDT45gW5I0Xj28quUoxujPAp5bdP9AtUySNAGjSPSxxLI8aqWIbcC26u5/RsTTq9jGWuBbx17lk6t4ud6o0e8i2e/pUm6//+boRZ/8/1I1TL9/sM5Koyj0B4CzF91fDzz/5pUyczuwfZgNRMTezJwfrnn9Zb+ni/2eLqPs9yiGbv4R2BAR50TE8cCVwM4RbEeSVEPriT4zX4+IXwC+xGB65R2Z+UTb25Ek1TOSefSZ+QDwwCheuzLUkE8B7Pd0sd/TZWT9jsyjjpNKkgriJRAkqXCdLvQRcVFEPB0R+yLi+iUe/56IuKd6fE9EzI2/le2r0e+PR8STEfFYROyKiFpTrLpupX4vWu+yiMiIKGJmRp1+R8QV1Xv+RET86bjbOAo1fs9nI+LBiHi0+l2/ZBLtbFtE3BERhyLi8WUej4j4TPVzeSwizm+80czs5BeDA7n/DLwdOB74J+DcN63z88Ct1e0rgXsm3e4x9fsngBOr2x+Zln5X650EPAQ8DMxPut1jer83AI8Cp1b3z5h0u8fU7+3AR6rb5wLPTrrdLfX9PcD5wOPLPH4J8JcMzkm6ENjTdJtdTvRvXEohM18FjlxKYbHNwI7q9n3ApohY6oStPlmx35n5YGa+Ut19mMG5Cn1X5/0G+C3gd4D/HmfjRqhOv38O+GxmvgiQmYfG3MZRqNPvBE6ubn8/S5yP00eZ+RDw7WOsshm4MwceBk6JiHVNttnlQl/nUgpvrJOZrwMvA6ePpXWjs9pLSGxl8Onfdyv2OyLeBZydmX8xzoaNWJ33+x3AOyLi7yPi4erqsH1Xp98LwAcj4gCDWXzXjqdpE9f6ZWS6/K8E61xKodblFnqmdp8i4oPAPPDjI23ReByz3xHxFuAm4OpxNWhM6rzfaxgM32xksPf2txFxXma+NOK2jVKdfl8FfC4zfz8ifhT4k6rf3xl98yaq9brW5URf51IKb6wTEWsY7N4da5eoD2pdQiIi3gf8BnBpZv7PmNo2Siv1+yTgPGB3RDzLYOxyZwEHZOv+nt+fma9l5jeApxkU/j6r0++twL0AmfkPwAkMrgdTulo1YDW6XOjrXEphJ7Clun0Z8OWsjmb02Ir9roYw/pBBkS9hvBZW6HdmvpyZazNzLjPnGBybuDQz906mua2p83v+5wwOwBMRaxkM5Twz1la2r06/9wObACLinQwK/eGxtnIydgIfrmbfXAi8nJkHm7xgZ4ducplLKUTEbwJ7M3MncDuD3bl9DJL8lZNrcTtq9vt3gbcBX6iOPe/PzEsn1ugW1Ox3cWr2+0vAT0bEk8D/Ar+Smf82uVY3V7PfnwD+KCI+xmDo4uoCghwRcReDYbi11fGHG4C3AmTmrQyOR1wC7ANeAa5pvM0Cfm6SpGPo8tCNJKkFFnpJKpyFXpIKZ6GXpMJZ6CWpcBZ6SSqchV6SCmehl6TC/R9gpZLVQ8R5UAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.8370533804349155\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.8573200992555832\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.8643731129826793\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.8609691689336569\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.8549095654731269\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.862216013528417\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.8659841188147931\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.8646361787123495\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.8428761365585865\n", + " 0.8554946940175967\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "data": { + "image/png": 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9GIViPdEgGoil5YL7SAr0CHvhDoDForVeqH9fAgHYtw9qaoxtlwuCQcMonGCO25BUpdRVSqlMpZRNKZWjlHpFKfWPkEFAGdyulOqhlBoYWmTcxKTDYrFYyMvLY8CAAVx00UVUVVWFj61fv55zzz2XXr160bNnT/785z83qYEvWLCAYcOG0bdvX/r06cN99913UPxer5cJEyaQl5fHO++802o6xo0bx/Lly1FKEQzoBAM6AX+AmS//g9tvv42Ghhoa6mtoqK3BU+ehtk7jF7/6A70GjGDg8DP5ePE6CkosFOyzsnO/g+17HWzZ5SR/E4yZcCFbCmzsKHSyc6+T//fkP+jaM5lde+uparBS02Bhzj9n8/Af78Xnt+EPWAnqFm686QdsWL8Ci6bj9VbxyCM/48ILe3L5jwdz80/PZduub0lI0UlPa6BnRg29M6vpkVZDt/QqOqdUk51cT3qaRmqKlZRkO67kKFzJsdjjErDFJmGNSeLe3/2RfkNGMWT0ONauXUNCtIOEKAfxUfbwZ/7c9zl75BmcM3IYV1x6Eb76auJctvDnpeefIT7KHt7/xcKP+dujjzQJc0wNglJQXQ3btxsGQdMgPR26dgX7oTuUjxfmjGYTk2OEy+UiPz+fdevWkZSUxPPPPw+A2+1m8uTJPPDAA2zZsoXVq1fz1Vdf8cILLwCwbt06fvnLX/Lmm2+yceNG1q1bR/fu3Q+Kf9WqVfj9fvLz85kyZUqLaQgGjBEzwYBOdU0t5eVVlJSWU7yviopKndpaobAwisI9UewpjGHX3iTeeX8pmzYX8NG/N/HgQ//ggQd/hdvtwOdz4fc7CQbsoDQ++tcszp9wMVF2wWHRcVp1Pvl4DoMHDuXbJe+RFuMmI76BmBg/TleQlFQ3qWlu0jMasNuDJKe5Sc9q4JG//pSMrFiWr1rO1998xT/+8Xekfg/pznqSo/3YE+OwxMdjiY/HGp2ENToJW0wyTqcTh8OB3W7HZrPR4Nep9+nUeYN8MPc/bNq8lRVrNvDMczO471d3HHRvAoEAd911F4sWLWLNmjUMGjSI5557Lnx8z549fPrpp01GOF544YV89NFHNDQ0HNWz0Sr79kFRkdEqiI42XEbJyRG92ice0yiYmBwHRo8eTVFREQBvv/02Z511Fueffz4AUVFRPPfcczz66KMAPPbYYzz44IP06dMHAKvVym0/+xm6zxf+7Css5NprriE/fxWDBg0kP38lH374AQMHDqJv335cccW1bNxYxo6dbtzuILv3+Cjc7eDFFz9g9OgzuezSy/j225XoQSvBgJ2gbkfHjmhWPl/8ET++/CoSYwKMP3so7vpKdPc2UuKqSYuvIiOxgqT0Gj7+9C0uu/J8krvpJHfTqda34fHX8+BfpvHRJ+9jTbdjSXNgi7HgclpISXaSnOQkKTEKq9VCbHQU5aUl5K9axeOPPUZyYgKJ8XEMyRvMxVOmYE80DIDFYgl/NE2j3hek1hOgxu1v8gHCtfdP/zuPm268nvgoO+eNPZuqqiqKi5t2URp9HIr6+nqUUtTU1JCVlRU+fvfdd/PYY4818cmLCOPGjeM///nP8XlQYmMNF1FWFnTu3G6tg0g63CI7JiZt4bMN+495nBP6pbcpXDAYZOHChdx8882A4To644wzAND9fvD76ZqaSl1dHZV797JuzRp+ddttBOvqDkQS8uVLyKecmpzM008/ybPPvsDrr35IZY2XX/ziYl5+6WO6du3J7353I7Ne+z+uu+4OlNLQgxZKS/fxwow/8a8Pl5KYEM81115Av4GDiU+uRTQ9PIqmonoX3ftejiPeKGgzs7PYX7OP7N5GjVkB+Pzs2bWLvAF9w4XmCx+8zzVXXcWF553LXT+7Fb2uhrS0NGJsNjRNazJOXkTwBBQrV69jwMBBuANAIHjI+3jDdVezbcsWENAiCup77rmHqVOnNglbVFREp04HBjPm5ORQVFREZmZmeJ/NZmPGjBkMHDiQ6OhoevbsGW7NffTRR2RnZzN48OCD0jFs2DC+/PJLrrjiikOmt014vdDQAImJxnZMDOTmtkvfQWuYRsHklKStBfj3RQWDqGCoUAsEUIEAbrebvEGDKNi9m6F5eZw7ahTB2lqCXi/K6yVYW2v4kK02dJvTiMfuRIngC+q4/UGCwdA4e10IBMHjDeILCIGAhf0lUXi9VkorbWzesoGsrK5069Ydu83LlJ9M4c23XyI14UYctiAJKV52Fy9hzLgzye0XA7rOZT++hO3bt5HuVChbFGB0ttqwEGdxkOR0Ig4LdpuVxLhokmMPjHgpLq4iITEBq/VAkTHnnXd4a867NPgVP5p8CW+8NYef/vw2PP4gvoAers0DBHWFiBDlsGK1aMS5Du+X/+C9d9v+e7QwQqr5KBy/38+MGTNYtWoV3bt354477mD69Oncc889/OUvf+GTTz5pMe60tDT27j3K0fJKQXk5lJYa3x0OiDJ+g5PJIIBpFExMjggVCKD8fpTfb9TkrVbjv8uFy+VixcqVVFVVMXnyZJ596UV+8fOf07NXb5Z+9RU3+wP4Axpeb4DtO7bjckZTVeWic+c+fLpoHdEJI1Gq2RQtpSMCmmhYREPTFHHRXhJiGnDag+RmNaCsTrYlaFhsGirWia5pYLVix4YFK3blAgGLzYZmseF2xjTJU3pODjv2FjJUBHw6u/fsIT45nVrPgUlkfqy43Z5wQb9u7Rq2b9vGpRdNAsDn89G9e3fuvftOcjLT+bK2uknBX11VSZfsDLLTU1i9ejW6rqNph/ZeT5kyhc2bNx+0v6WWQk5ODnv2HBBIKCwsbOIaAsjPzwegR48eAFxxxRU8+uijXHzxxezcuTPcSigsLGTo0KF8++23ZGRk4PF4cB3BkNCD8Hhg717jP0B8/EnhJmoNs0/BxKQFVCCA7vWGP3UVJdRVllBfU069r456zU+tJUC17qaioYqSikqUgj2F9VRVOfjN/Y/z5NPPsbNQ4+xxU1ny1TfM+eAbivZHU7gPHvzD/Vx/w300uG1MnfprXn75cXbt3IJNU9g0H7PfeoIYl5ukuAYyUj1kZ9SSkVaH0xUgPVvoO2Iguwr3sGJPMZUWjVn/nMPIMWOIdtqwWzWSY5yMO/ccln71Jb5APc4oC//+14fYrVqTkTRxLhs/vuwS3p3zNrFOKxtWryAxIYFe3Ts3CdMlKw1dD2KXIHEuG//+8D2mTZtGQUEBBQUF7N27l6KiInbt2sXw4cNZunQp+/btA2D58uV4vV46depEjx49GDZsGA8//HC4dr9161bmzp170G/wzjvvkJ+ff9CnuUEAmDx5Mq+//jpKKZYtW0Z8fHwT1xFAdnY2GzZsCE/s+vTTT+nbty8DBw6kpKQknJecnBxWrlxJRkYGAFu2bGHAgAFH/hDpOpSUwM6dhkGw2Yx+g+xssJ689fGTN2UmJu1EXX0leH0oi4YKuSCUVcPpiMVd58Hv1vAHNPx+DZ/fgs8fCxiTserqYhEgN3c0vXoN5qO5HzD5out49pkP+ev0u/jrX+5E14NcfvlV3H77LdikhvSzOvPY9If5/e+vxO12IyJM/OFEMnM06nUriKCL4LdHoYsNj2bH6rTywoszue36a9CDQYYPH85v7vwlDseB2nlmZibTpk1j9OjRZGZmMnToUILBg/34kyZNYv78+eTm5hIVFcVrr73W4n05//zzWbJkCRMmTGDOnDksWLCgyfFLL72UOXPmcP/99/PMM88wadIkdF0nJiaG2bNnh1sGM2fO5N577w1fLzk5mccff/yofrND5SEvL4/8/HyysrJ4+OGHGTNmDDabjS5dujBr1qzDxr1o0SKmT59+5IkqKYGKCuN7UhKkpp50rqKWkMPOVjzJGDZsmFq+3JzSYNKUjRs30rdv38OGU8GgMVmrBRoaqlGhGbx2RwzKasPvF9xuob5Wx+O1GnO8xCjcVGgWl0Jh1XRstiA2axCr1R+aUGXMqtU0Hc2qY9UEzaKwaUIdltBkLlBWhzGlS7PgC0Y6j4zpVVEtuFkOOSv2OLFq1SqefPJJ3njjjRN63fZk//79XH311SxcuPDITw4EYM8eY95BY//BCaKl90FEViilhh3uXLOlYHJaYPQDBFABvyFxYLGggAZ/fdiNEVAWAp546urBH9AIBAU9XGcyCmaLprBafWBTWGw6drsfR4wNTQuEwyhsSLNXSwd8GJ2fNUGjUI/StEbhg1AiwW458YV9WxkyZAjjx48nGAxi6QA13mPB7t27+dvf/ta2wHV1UFVluIdEDBdR167tOufg+2AaBZNTCt3vN3y5zWhw16CLoGuCTgDlhWBAw++3Q8CBxy00uCUsANH4IltsOnaHjsvlw+Hyo1mMVoaGRjQBQNAtFmOPZkfTtLCbpPl4d4A6bwC79eQt+A/HTTfd1N5JOKEMHz788IGCQdi/3zAIYExCaxxy2sEMAphGwaQDs6RoCQHdKKTjA/HU+moRtwelaQSC+gExtpCbx4YTn8eC22Oh1mshGGjmlhFwRekkxgVxOHxYrUE0TRCxgbce3RaNiA0Re0jdUtA0Lfy9+RDIWo+f5t7Z9nD7mBxHamqMWcmBgPHjpqZCQkJ7p+qoMI2CSYcg0gA0YlUaZ8cMIlhezuZAHVp9AKVriN2O1WJMVlIKGho0Gho06msEX0jjXikdu03hcARxOHQcjiAuVyDUF2BDKQ0RR+hKgtViRxzRBxX+LRX8jYjQpvH4Jh2Q5gJ2UVGQmWnMP+jgmEbB5KQl0hBYNSvjOo0LH1NKkf/ee3zTsBO/1UZ8n95YrFawWtGVYQQqKjTq6xV6aMCNjo7FqhMb5SXK5Scmzo6mOULeJg2whWv/DX4dq8+YYSwCQYSA9+CRO2bBf5pSW3tAwC4tzXAXdUBXUUuYRsHkpOJQhgBA13U2b95MTVUVDZWVdD7vPIJBB7W1VZTXuGhoAJ/XCBsMVeGtNh1ndACnK0BKnBNwopQjrIVjDbrxBfRwf28QQ+M9ymEDZ9wJybdJB6BRRhwMF5HPZxiDk3gi2vfBnLxm0q4sKVrC4j2Lwx+AcZ3GMa7TOM7KOguPx8OqVatYsmQJixcvZvHChZSsXcva7eV8uGM4P/99KhdOSaGySiivBLcXdAGrSxGXHKBHN50umW5y0q0kxzrQdd2YTeuvx667seserBYLAXscUXHJTT5HahBONuns5syaNYtf/vKXB+3ftGkTo0ePxuFw8MQTT7Qar1KKc889l5pGlwnw4YcfIiJs2rQpvG/x4sX86Ec/anLuDTfcwHvvvQcYchMPPPAAPXv2ZMCAAYwYMeKgOQ/fh+nTp5Obm0vv3r35+OOPWwyzcOFChg4dSl5eHmeffTbbtm0LH/vnP/9Jv3796N+/P1dffTUApaWlTJw40ZhvsG2bYQjAaBWkp59yBgHMloJJO9C8NTAmdRSRjnlfbS1r81dQU7QbZakB/GQkJFLb4GDnzliWfteN/67tgUO3IoDD58GmBUiK9uBwBbFZdGNdGE1QxgChsKyCxWJB89ejWa3hQr/W4z8mLf9G6WyA66+/nueff54HH3wwLJ09Y8YMzj//fBoaGrj88st54YUXuP3228PS2fPmzaNPnz4EAgFeeumlg+KPlM4+liQlJfHss8/yr3/965Dh5s+fz+DBg4mLO2AsZ8+ezdlnn82cOXOYNm1am673+9//nuLiYtatW4fD4WD//v18/vnnR5MFNmzYwJw5c1i/fj179+5lwoQJbNmy5aChs7fddhtz586lb9++vPDCCzzyyCPMmjWLrVu3Mn36dJYuXUpiYiIlJSUApMbFkRkby9L58zlr6FDDZZSSclRpPdkxjYLJCWVJ0RIAxqSMBKUI1tZRv2wZ2Oz4/T7cbjd19fVUeZdjoTtrd/Xh2286s3N3ApXVDhp0QYVWzcrrXsP44WUM7FNNTJyLlAwbocX9wssrhg2BpiHeWtAViFCLCxXS8TkeI4JGjx7NmjVrgNals8eNG8ftt9/eonT2L37xiybxlZSUcO2111JaWkpeXh7vv/8+BQUF3HfffQQCAYYPH86MGTNwNOvofO2115g+fTqZmZn06tXroONgCL6lpaUxb968Q+bprbfe4tZbbw1v19WjgGGJAAAgAElEQVTVsXTpUhYtWsTkyZPbZBQaGhp4+eWX2blzZzgt6enpR61AOnfuXK688kocDgfdunUjNzeXb7/9ltGjRzcJJyLhlk51dXVYH+nll1/m9ttvJzE0lDQtNRXKyqC0lEvGjuWt+fM5a/JkiDv13YmmUTA55ugNDbQ0JOer4q8BGFIRR/3mZSiHHa/Xy7a6Orxxcei6Tq3PyberkvnqqyHsKslG10HpCouAzaXo1snHuaOEiRPdZGcLezfuRwWDiMSEDYGIYNn2KZqm4ffUEzlmKWg1hM0sQJT9CB//3j9sU7BDSWc30qNHD+rq6qipqWHdunXce++9h4wzLS2NmTNn8sQTT/Cf//wHj8fDuHHjWLhwIb169WLq1KnMmDGDX/3qV+FziouLefjhh1mxYgXx8fGMHz+eIUOGHFmeI1i6dCkvvvhiePtf//oXEydOpFevXiQlJbFy5UqGDh16yDi2bdtG586dm7Q2WuPuu+9m0aJFB+2/8soreeCBB5rsKyoqYtSoUeHtRuns5sycOZNJkybhcrmIi4tj2bJlgKFvBHDWWWcR9PuZdtttTBw5EoBho0fz0IsvnhYGAUyjYHKMCZSW4tmwAXE4w/vWlq0lqIJYxUL/pP7U11bi6dIFb6hp7yncR1lpH179t4fN38Wg6wLKgssR4KzeHoYN8jB4QIBOo+xoGjj2fAmBAHsWFSHBAFmd0qmQFGy6G2/AmH4W7HRm6OpCVFzSCcm72+0mLy+PgoICzjjjDH7wgx8Ahi++tcXUv+8i65s3b6Zbt2706tULOOCuijQK33zzDePGjSM1NRUwVEcbC7/vQ0VFBbGxseHt2bNnh6935ZVXMnv2bIYOHXrM8vrUU0+1OWxbpLMb45w/fz4jR47k8ccf55577mHmzJkEAgG2bt3K4sWLKdy+nXPGjWPdvHkk9O5NmsNx9NLZHQjTKJgcNUop/IWFBCsqCJRXsM5Rgjf7wHoGluzeDEsZRm1tLfuqqyE1BeveIF+vrGb52ni+WdmfBo+NWmUl0WJn6JkBRudVMvYH4IzRsFg1HHu+ofCL3QQCfnSl4UnqjS1lAD2HjSQmJob9m7dSLzGIvf0mhzX2KVRXV/OjH/2I559/njvvvJP+/fvzxRdfNAm7Y8cOYmJiiI2NpX///qxYsaLFBV5ao62aZd/X6LSE1WoN982Ul5fzv//9j3Xr1iEiBINBRITHHnuM5ORkKisrm5xbUVFBSkoKubm57N69m9ra2iYGpiWOpKXQFuns0tJSVq9ezchQC2DKlClGJzKQk5nJqDPPxGaz0a1PH3r36cNWv5/hMTF4amuPTjq7g2EaBZOjRq+uxrt1G7bsLFxD8vDWrWZM9hg8Hg+1tbXU1dVRVFSEVuhj2/ZoPvhMsWp9MqW+bGNuAZCYU82Es2q58Rwr8dWriIvzEV0VYM+qIvz+AIgFX2p/uuWdQUxMDC6XK7yy1+LNJcRz8swXiI+P59lnn+Xiiy/mtttu45prruGvf/0rn332GRMmTMDtdnPnnXfym9/8BoBf//rXXHbZZZx99tn06tULXdd5+umnueeee1q9Rp8+fSgoKGDbtm3k5ubyxhtvMHbs2CZhRo4cyV133UV5eTlxcXG8++67R2R4mtO7d2927NhBbm4u7733HlOnTm3iTho7dixLlixhxIgR7N27NyzKtmvXLlavXk1eXh5RUVHcfPPN3Hnnnbz44ovY7XaKi4tZuHAh1157bZPrHUlLYfLkyVx99dXcc8897N27l61btzJixIgmYRITE6murmbLli306tXLkM7u0wf27eOSYcOY/emn3HDjjZSVlbFl+3a69+wJHIV0dgfFNAombUYphV5Tg7+4GL3BjWjC6tLVBD0eVHwMgSjBX7kbv8dPQUEBwWAQq9XK5s1VvPN1PPkL06iscAIKi0VIyapn0oB1nH3GdtISqol3puMQK/542O2NRnl0HGmD6TlsZHjR9siFWRZvLiEQVFgtgtN2cgm0DRkyhMGDBzNnzhyuu+465s6dyx133MHtt99OMBjkuuuuCw8PHTRoEE8//TRXXXUVDQ0NiAgXXnjhIeN3Op289tpr/OQnPwl3NP/85z9vEqat0tn79u1j2LBh1NTUoGkaTz/9NBs2bDjI73/hhReyePFicnNzmT179kG19csvv5y3336bc845hzfffJMbb7wRj8eDzWZj5syZxMfHA/DII4/w0EMP0a9fP5xOJ9HR0fzpT3864nscSf/+/bniiivo168fVquV559/PjzyaNKkScycOZOsrCxefvllLr/8cjRNIzEujlenTYOKCi445xw+yc+nX79+WCwWHn/8cZKTkwFDOvtwv8ephCmdbdJmGr77jrUF3xJw2QimJ6McNqyalRFZo6jf46G2uhaf30dxZQk19Ta+WpHKirUJFOyJwWu1EouQlAnnnrGQscN30SXFgpBEqTaIsl0bcMYrLFYNl8tFVEwMvYaPajKksNEINGK1CON6pwFtl842+f4UFxczdepUPv300/ZOytERDBoSFdXVxrbTCVlZxv8WGDNmDHPnzg2PTOoImNLZJscd3e0mWFuHe3AuY3saY+0bGhpwu90UlJdTvKUAyXCyryKaTz/tzYoVaQQCUKcrrPGKgUM9XHeBl0GDN1C1Zj4NwfPZUqSj0FGyhtg0F/3POoeYmJgWZZkXbzbGjR/vtZdNWiczM5Of/vSn1NTUtGn00EmJxwO7dzcVsEtOblWiorS0lHvuuadDGYSjxTQKJoekcaKZbcMOo0NZ68yuXbvYtWsXUuZHEwGlsWFbKsvm9eTLFVZjnWFR9B3hYdj4BqaeqbDZatm7ZQmV+avR3Wk4MrqRmu4gMTWW2NhYoqOjw66h5i0CaNoqMGk/jnY+QbtjtxtSFW0UsEtNTeWSSy45QYk7OTCNgkmrLClagnXjDgbH9qZBz8QtaQR32dlbsxOHaMTFdGfhlkTmzXNSXGyhTlfY7Iof/9DH5MkV6LUfoYJCyXc7qW/wQcBJUuJALLnjiUuIpXOfVDRNCxmBhvB1rRYxWwQmxwaljFnIMTHGUpiaBl26GAvgnCICdsca0yiYHITSdb5a/18o2k9WQzzFyXYspGKNjqbMXs3OfcksW9aNr75y0LiypS3Fz6Qf1vPLi4I0rJiNe2sRiE5aYm9qLQOxdh9Mp56dWL3Pi0JjX0Bn2yZjAXXTCJgcF3w+KC6G+npDuC4z09hvOzlGqZ2smEbBJMyKr74kGAhi2b4HW72H5NgcgskplO+qRlewaa+DefO6sn274V+t1xW5QzyMuKCBHMfHjPCWUP+dos5fi6VLfxz2ARCXRWGpF3uii6oqHXu03XQDmRxflILKSigpMZRNLZYTvkZyR8Y0Cqc5ns0VrC9dT1APogCpracm4IE+3fC7XOQrnV1bUvlufmd2bTIUIR1RQUZOrGdgl4Wc59yFUuDz+PB37Y2noRaRVDp1O4NtnnSqlBCbYxoCkxOE12u0DhpC7si4OMjIMNxFJm3ClM4+zVFBRUWWh6zuuSRlZtNQVUWP4cPp3a8fwWBP/v1SHz5+picVWx1kxCpuvaqWp65+jakprzPMux/V5Xx8WaPxpfYFFUNl1DAaUs9jizsDTdOY0C/9tDEIHVU6+6233mLQoEEMGjSIM888k9WrV7cY70kvnf3II+T27Env8eP5+OuvoVMnyMlpYhC+t3T2aYRpPk9DPJsrUEFjgZmV+1ZRpRWxo6oOqa7GJsK2wjiefCaaDd85jVF70YpJ59dy3ugKAnVbsFZ4yDrvx4gG1TWriNY0KjyxeFydibMnnTZGoDkdVTq7W7dufP755yQmJrJgwQJuvfVWvvnmm4PCnfTS2e++y/rFi9lbUsKEa65hyw030Hxw8xFLZ6emkpmZydKlSznrrLOOKo0dhePaUhCRiSKyWUS2icgDLRzvLCKLRGSViKwRkUnHMz2nM57NFbg3lOPeUA7AN1FrmeddxGbPbjL2O7EGAiRkD+DNry7m7gdz2PCdk2QnXDGxjt9c/xmjun+JBLaQENxC78FZKC3AV7sq2OWGYsnFFZ/HDwb2OW0NQnNGjx4dVulsTTr70UcfBTgi6ez8/Hzy8vLYvn07CxcuZMiQIQwcOJCbbroJr9d7UDpee+01evXqxdixY1m6dGmLaT3zzDPD4/BHjRpFYWFhi+HeeustLr744vB2o3T2K6+8wpw5c9p0Xxqls//+978fvXS2rhv9Bm73Aensbt3oNmpUWDq7OW2Wzk478BxfcsklvPXWW0eevg7KcWspiIgFeB74AVAIfCciHymlNkQEewj4p1Jqhoj0A+YDXY9Xmk5nVFDh6mdM2/9k6ydUllQyMm0kRSWbyLE3sNJzBrf9MY3aKg27M8hZF9Tx0/E1JCVDRaFOt2HDcRZ9iT8+hcrkM4iLjiYpSTGiUyoxMenY7SdGibStNK7idixpvjRoa3Rk6exXXnmFH/6wZYnwk0o6+847Ye9eY4RRXR1FhYWMGj06PMz0qKSzg0GmTZsWdhsNGzaMhx566LDpPVU4nu6jEcA2pdQOABGZA1wMRBoFBTQ+HfHA6aNPe4JodBWJxVCyLC8vp2BTAf2T+jOvcD9en43nPh3GN1+mIwJn5gX4+c0VeIpWoe3ZjrfMT7xVsO0sY12Zn6qcc3F5XFh9ARzabkSLwWqNbu9sHkRbC/BjSUeXzl60aBGvvPIKS5YsafH4SSGdHQwarYOCAmPb4YCMDFoS6/ne0tmFhZxzzjmsW7eOhIQE0tLSTOnsY0Q2sCdiuxAY2SzMNOATEbkDiAYmtBSRiNwK3ArQuXPnY57QU5FGY/C534NkReP2eKjZuI1tddsJBOOIqqzDUmln6au5bN2XTIJLuO6aOiadX0PR4n8TF+Ojx+Ac3Nln8cXWMjZZ7KT0TWRy73iqq1cR1D0AxET3QtMOPSv0dKEjS2evWbOGW265hQULFoSF4JrT7tLZug5+P1dOnMgDt95qLIuZnAyadvTS2Tk5jBo1ypDO7taN3r17s3XrVoYPH47H4zmtpLOPZ59CS09j8yf5KmCWUioHmAS8ISIHpUkp9ZJSaphSalhjrcekdTybKwBw9UtGT3fR31uHt/hzovxrOSPKz4VaFAn7M3n7pVFsK88gLdvCk09WGQZh3VpcLj8Z5/2EiqSheDweYuLi+eEgncFpm6io/BoQkhJHk5I8Hpstvn0zexLSKJ39xBNP4Pf7ueaaa1iyZAmfffYZQIvS2X/961/DtXhd13nyyScPeY1I6WygVensxYsXU15ejt/v5913320xrt27d3PZZZfxxhtvhFseLdEonQ2EpbN37dpFQUEBe/bsoVu3bixZsoSePXuGpbOBVqWzfT4fYLi53nzzzYOu99RTT5Gfn298Vqwg/913yX//fcN11K2boVsUkkaZPHkyc+bMwev1snPnzsNKZwOGdHZINO6SSy4JG6CysjK2bNlC9+7dAVM6+1hSCHSK2M7hYPfQzcBEAKXU1yLiBFKAkuOYrlOaRoPg7J2E1+ulrKwUX5QNm8PGqNRR1Ox38+TrVSxb3wNsFgb283D3HVWIz82ulZuoq9iEnhPPkh3VxMTG4HQ4iY3yEAhUExs3EJs1AU0zB60djo4knf2nP/2J8vLycOe21WptcUjrCZfObmwNiRgT0DIyDCG7pKSDJCq+l3R2YiKvvvoqABdccAGffPKJKZ3NcZTOFhErsAU4DygCvgOuVkqtjwizAHhHKTVLRPoCC4FsdYhEmdLZh8a9oZyv023Ue71UlFdgt2iMTYjm65Jl+DaO5anHgtTUWImKj+HHl9cx8exytjV4qCvcTJJ/C3FpSQw5bwoJCQmICHV1m3F7CrFaYoiPH4Km2ds7iy1iSmcff06odLbfb8hbR0UZLqJ2xJTOPkYopQIi8kvgY4x10l9VSq0XkT8By5VSHwH3Ai+LyN0YrqUbDmUQTA6NZ3MFYhHqvV761FexNrCe2uJaPlhvY947g9m5zgFeD4MHK66ZUoTTt4bV6z3Y4qwMjdlFSmYWUYMmk5AQTzBYR1XVchQ6cbEDcTjMoaanOydEOlspY52D/fuNTmW329At0tpnnq0pnX2MUUrNxxhmGrnvDxHfNwCnx4yQE4AKKr5MFmpKK3DEuaABKjcM5733elBTqRNDPRed/Q3Dx3lx6BpxWS6c9eX0S7Xh8SfjHHAeur6asnIdAJs1nri4wWiaKSBmYnBcpbMjBezAUDbNzGw3gwCmdLZJB8azuQJdFCXlFZwVbWdl5So+e787/5uTjV28ZMZv50djlhPdMx5nTAM2uw9PHYhmoSJ1JKmpqTgcVfj9CcTGDjANgcmJQymoqIDS0gMCdhkZhm6RKW99wjGNwinCooYGqqL94A1irazjPy+k8MVnnQnqHiZfUkT/HmsZNX4CKe5vARfeTucTCATweDxkZmYSGxtLQ0MVVmucaRBMTjy1tYZBiI+H9HRTwK4dMe/8KYCu61TV1aH5V7NvSwl3vtWPFRsH4HJYuenylZx3jo+GqiTsxSsgATw5Z+Nxu1FKkZ2dTXT0yTf5zOQUR9eNT+NiN5mZhvvoMHMXTI4/plHowCwpWoLH52H1Zp1gMEjXBAefvng2O4s6EeNQ3HDN1/TuVo7NeQbpzvW4ogJUpQxHb2ggPj6exMRErFYrwaAXpXx4vaXY7e070sPkNMDtNiQqbDZDyVTEmJl8mKUxTU4MpnR2B2VJ0RKs2/z0KMgkqs5GXr2dVx8cyuadmSSlR/Pr+4v4wfju5OScQUyynbS0emrShxIdHU2XLl1IS0vDZrPhdu+msvKr0EijIE5nZntnrcPSUaWz586dy6BBg8jLy2PYsGGtyly43W7Gjh3bZK7DU089hdPppLq6+pDXGTduHMu//Rb276du/Xp+9tvf0uPss+k/YABjxoxpUZX1SFBKceedd5Kbm8ugQYNYuXJli+Fmz57NwIEDGTRoEBMnTqSsrCx87O9//zu9e/emf//+4YmFa9eu5YYbbjiqtHU0TKPQAfFsrkA2ebDtdjPPvYe9uwP8v6fy2L4rjsRkH/f9ZgMZ8ZU4yteSY11OQvXn+IOKzMxMMjIysNsj5xrouFydSUkZT1LiaCwWc4Wq70ujzMW6detISkri+eefBwhLZz/wwANs2bKF1atX89VXX/HCCy8AhKWz33zzTTZu3Mi6devCs2kjiZTOnjJlyjFL93nnncfq1avJz8/n1Vdf5ZZbbmkx3Kuvvspll10WnhQGRiE7fPhwPvzww0NfJBiEoiIoL+eWP/yBpIwMtu7Ywfr165k1a1aTwvn7sGDBArZu3crWrVt56aWXuO222w4KEwgEuOuuu1i0aBFr1qxh0KBBPPfcc4AxQW3u3LmsWbOG9evXh43ywIEDKSwsZPfu3UeVvo6EaRQ6CJ7NFRTM+4ad//6agnVr2Lx/JcvSnKxf3J3/vDiM0moXnXt4eORPBQwflkG2qiItw0999mi8Xc4lZdSVh9WaMTl2dCTp7JiYmLBGUn19fat6Sc2ls7dv305dXR2PPPIIs2fPbvlGKGVMQvN6we9n+759fLNhA4889RRaqDO5e/fuRz1jeO7cuUydOhURYdSoUVRVVVFcXNwsKcYaIvX19SilqKmpCesjzZgxgwceeCAs5x0pnX3RRRe1WRr8VMDsUziJaRS1AxCL4E9TJPfoTVlZGbK3Nx+8cAZF3/nxasKIMwv5waRCUrWd1K/ch9VqpSrlDBw2G1lZWU1qd6cDtf87WHL5aIk9d3ybwnVE6ewPP/yQ3/72t5SUlDBv3ryDjvt8Pnbs2EHXrl3D+2bPns1VV13FOeecw+bNmykpKWlSmAJGf0HjPIPERNbX1JA3ZEibnscpU6awefPmg/bfc889TJ06tcm+oqIiOnU6oKrTKJ2dmXnAHWqz2ZgxYwYDBw4kOjqanj17hltzW7Zs4csvv+TBBx/E6XTyxBNPMHz4cMCQzn700UfDLqVTHdMonIREyl03roGw+duv2bpzJ9F1bv5dUcd/nxxA3V4fmc5arr9uDVPGGxN+auviqHTmEZVi6ONkZma2+ALquhdd96HrPgxFklOLthbgx5KOLJ196aWXcumll/LFF1/w+9//Pize10hZWRkJCQlN9s2ZM4cPP/wQTdO47LLLePfdd7n99tsRXTc0ihpJSQGnE0lKMjqZ28ih+k2a05IQQvN76/f7mTFjBqtWraJ79+7ccccdTJ8+nYceeohAIEBlZSXLli3ju+++44orrmDHjh2IiCmd3RIiYgc6K6W2HTawyVERqXAKsH3ld1RXVbF73z5Su3QnJaU7n07XsRUKA7O8PPqssNHbm4bsWPSAos7mQYIBomNjDzIIuu6ltnYjiODzlSFoWCxRuKK6tEteTzU6snR2I2PGjGH79u2UlZWRkpIS3u9yufB4POHtNWvWsHXr1rDh8/l8dO/enduvvZZkn4/KoiKjH8FiAU2joqqKlJQUEhISWL16dViC+1AcSUuhLdLZjcuY9ujRAzBmZze68HJycrjssssQEUaMGIGmaZSVlZGammpKZzdHRC4E1gKfhrbzROQwvUomR0rjcplgKJwqpdjw9RJK9u8nrnM30rr2YFtUNrf8OprKHVayU2p59HmduBQrwdoA+7bWUrStEm+Dn8zsNLKzs8MGQSlFQ8MuyiuW4POX43RkERc3mOTksSQmjsTpyGjPrJ9ydDTp7G3btoWNzMqVK/H5fAetqZCYmEgwGAwbhtmzZzNt2jQKCgooKChg765dFO3eza7vvmN4v34sXbWKfaHa9fLly/F6vXTq1IkePXowbNgwHn744fA1t27dyty5cw9K1zvvvHNAOjvi09wggCGd/frrr6OUYtmyZcTHxzdxHQFkZ2ezYcMGSktLgYOls//3v/8BhivJ5/OFjaIpnX0wf8JYHGcRgFIqX0Ryj2uqTkMil8vcvvI7KivK8Xi9dB44hOLiYuZtjubDGVYCJfV0iS3h13d5Wb/Lg3+7jtKD2Lva6NW7C7FxsU1qYA0NBXg8RQR1Dy5nDtHRPWlhyQqTY0xHks5+//33ef3117HZbLhcLt55550WWxjnn38+S5YsYcKECcyZM4cFCxYYHclVVbB/P5eOH8+cBQu4/6GHeOa555h08cXouk5MTAyzZ88OP5czZ87k3nvvJTc3l6ioKJKTk3n88ce/760GDHns+fPnh+N87bXXwsfy8vLIz88nKyuLhx9+mDFjxmCz2ejSpQuzZs0C4KabbuKmm25iwIAB2O12/u///i98D0zp7OYBRJYppUaJyCql1JDQvjVKqUEnJIXNOBWlsxtdRsXunfg8Hqpqqknp0RuXy8WXVQ3MfbGBb//bBc1TTf+uPu6+YSNF8Z3R4jTycuwkJiWSlJSEtZk0gK77qa5ehcORhsORdkoPNzWls48/q1at4sknn+SNN944sHPvXsMogDEbOSPDmJR2iuD1ehk7dixLliw56P06mTne0tkbReQKQBORbsBdwLLvlVKTFmlsJTQsXYczI5u0lHRidnzOx7UJvPJib/ZtTEGz1TJ+4gZ+fn1PttZ0w+KEIdkOcnJyWvR3BoMeKiu/BhQOx4BT2iCYnBiGDBnC+PHjCQaDB/qq4uOhrs7QKzoFBex2797No48+2qEMwtHSlpz+EvgDoAMfYKyP8NvjmajTka3Lv6G6poaY7M44v5vLGzuzePP9sTSUCknpNn54y0r65HZifXEt9gQrQ7IdZGVlNTEIXu9+6ht2oIkNpQJYLFHExw81Be5Mjhk3XX210TJo7HOIjobc3HaVtz6e9OzZk549e7Z3Mk4obTEKFyil7gfub9whIpdhGAiTo6RgwbfowSDVzhrSO3dDKy3j6W9yee+/47AHLHTqUslv7qikPLoKX00CgzvFYI0OkJ6eHhay83pLaXAXEAjU4HRk4HRmA6BpTtMgmBwbdB3Ky6GszOhHcLmMVdHglDUIpyttMQoPcbABeLCFfSZHyM78FYiuSDq7L/6iIpYX7mfdugTe/WgkDs3CBefV0W/CZ9R0jcZlseCutuJI0ElJSQuvfOXx7KWubjMORwaxMf2wWk3FU5NjTKOAXeOM6cREU7zuFKZVoyAiFwATgWwRiRwfF4fhSjI5SvRggLROXdlXWoqrtpbt26KZ90ZfXMrN+PG7uezKUra7dUanjyIQCLC4tJyMjE7ExsZQW7cJv688PKooKqoHmnb6+D1NTgC6DiUlxgI4AHa7IXFtSq2f0hyqFCkB1gEeYH3E/lrggeOZqFOdnfkr0IMBRDSqqqqw2HQ+XOvnrVcG46mpZeTILQyeuI2SuDgS4hPw+XwEg0FSklOIihLKyg0Jh5jontjtqVgsp8/EGpMTSKNBEIGkJEhNNV1FpwGt/sJKqVVKqVeA3kqpVyI+/1RKHZ2k4WnMzvwVAHQfOgKnLwl/0M9n/5+98w6Pqkwf9v3OTHogjQCBJBISiKRTA0oHAcFCE7EhIuoqCj/RXV0r67KyKyqIi66Crp8iCcqKWBAXEFYF6QRMQgkhCSmUQHqd9n5/nGRIZyA9c+7rmkvOyTvnPBPhPPO2+8nU8vEHIWiLjITemMGTT+kI696Pod5DKcvtzZ7UQpKKHXF2cqC4+DR2Oje8PEfg5OSvJoQ2RHtVZ1dy4MABtFotGzduVE506aL0Cnr1gm7dKK1Ynnnd6uyKmIqKinjssccIDAwkNDS0TaizlyxZQs+ePYmKiiIqKootW5TS8qo6u256CiFihRDHhBCnKl/NHlkHxWwyEhA1kMuXL1NaXEJ5dy/WLHHHmGtHcK88pjyQiFYr8PPzo7S0FCk0zBrej0kRPRkdrMjGnJ0D0Gjsr3InlZamvaqzQZH4Pffss0wcOVKZSAalKtoNNyiTyjRSnV2F+fPn4+npSVJSUptRZwM8/fTTll3TkydPBkjvjqcAACAASURBVFR1dn18AvwbEMCtwBeA7Xhkm4iUuEMkH9qHRqujoKCAnJwczDjy6gvOnMvS0LlHAeNnfYGzl7QkBCcnJ8umNClNFBQcQ2/IQQjbMp62R9qTOhujkXf/+ldmjBhBVzc3KC6us9l1qbNrkJyczL59+1i6dKllh3NbUGc3hKrOro2zlPJHIcSbUspk4CUhxC/NHVhHw2wyEjgwmrKyMtLT03FycmbOeyZO/W7E2a2M/3s0jT5unnQPG4der8fOzo7u3buTmJeNwZBHuV75b+dOYdjZuV/9hjZOyrGmH+EMiOhy9Ua0I3W2lFBQQObRo2z6/nt++uQTDixdWudE8nWrs2uQkJBAVFRUm1NnA/zzn//k008/ZdCgQbz11lt4eHgAqjq7LsqFIgFJFkL8AcgEGv4/r1KNlLhDaLQ6DAYDWVlZHD5n4Kfv7UmK86CLyOfZ6T8zrLsWl85ulJrNGAwGfHw687/jR9Gac8nLl9jp3HBxCcLBoVtrf5x2gbUP8KakXamzDQY4dw6Kivi/v/2Nf7zwAto+fZSlpnXEdE3q7Cb6rC2pzn788cd5+eWXEULw8ssv88wzz/Dxxx8DqOrsOngacAUWAn8D3IB5zRlUR8NsMhLQfzCZmZnsPVtC1hl79n7fFXvTZeZP3sWoGYPQubtRDpQWF+Pp6cqBM4dAODM4oCf29l7Y27f8Q07l2mhX6uyiIuWl1XLwxAlmP/00PP00ly5dYsuWLeh0OqZOnVrts1mlzl6wAC8vL3Jzc6vdLicnp02rs7t1u/Jl65FHHuG2226zHKvq7BpIKfdJKQullGellA9IKe8A0logtg7FxQNpGE7lI/Oy+P4DHeWlRQyKSmTq/YHo3N2Ayr98ArM5AZNJMip0GK6uwWpCaGe0C3W2u7uyxLR3b1LS0iwK7JkzZ/Lee+9VSwhghTo7K4vMzEzS0tIYPHgwu3fv5vz580D7UGdXnX/YtGlTNVW2qs6ughBiMNAT+FVKeUkIEYqiuxgL+LZAfO2ayv0IMtNIkWsR7i4JbP0qgpRsN7r5lvDg5N/BbiRSSkpLS7G3t8fLy5mycjdw8EejUXeNtlfanDq7SxcGBAVdWU4qhJIUroE61dlVmDZtGrGxsTz33HO88847TJ48ud2os//0pz8RFxeHEIJevXrxwQcfWN6vqrMrfyDEMmAGcBQIADahGFL/AbwvpSxpqSCr0p7U2cmH9tF7wBAyfjmFveYgZ8+7c8eLN+FkMrH6yWOEhpkRwcGUlpbi7u5Oly5dyM3bw7FMA8I+1LIEVeXqqOrseigrUxQVlUM/3bpdkdldI3Wqszs4qjq7OncCkVLKUiGEJ5BVcVx7kE+lFpWTy8XFxYjzR9HdoOOlz3sjTXD7TUUEdstD9B1ESUkx3bt3RafLo7S0ELO5HJMmhPFqQlBpDGazIq+7fFlZZWRnpygqXF2v+5J1qrM7OKo6uzplUspSAClljhDihJoQrKdycjktLQ0nreBg3hhOxgtcHbTcPe4MDr17kF9wEGdnDXpDJgaDBmfnXsRf8EKns41/cCrNRFkZZGZeEdhVKiqa4EE+b55trTFR1dnV6S2EqDShCqBXlWOklNOvdnEhxCTgHUALrJVS/r2ONrOAJYAEjkop77U+/LZNYWEh5tRihEbDxx+7ACVEj72EX18nCjuloEGLr+9E7O2VieZdJy8idC7qsJFK4xAC9HpFYNejxxXFtYqKFTSUFGbUOP5nna3qQSjbblcDtwAZwAEhxDdSysQqbfqgFOy5WUqZK4ToME9Ds1lStP2/uGoEv53z4sQJHcLVxLBhGZjNGgwGE3363IG9vQu7Tl7EaJLotEJNCCrXR2kpODoqCcHBAfz9FT2FKrBTuUbqTQpSyh2NvPYQ4LSU8gyAECIWZZ4isUqbR4DVUsrcintebOQ92wzy95Pg5oxh8E28NseRi8VF3DT5BDcbiihy1uPi0hknJ2d2nVQ+8vgQdVOaynVgMsGFC0o1tJ49lfKYoOqtVa6b5vwa0RNIr3KcUXGuKn2BvkKI3UKIvRXDTbUQQjwqhDgohDhYuca4rWMs15PSzYcff7zMpSwHenYq4E9DNehu8AV7HT16DOV/p5TPovYOVK6LggJITlYSghBKglBRaSTNmRTq2kpZc/2rDugDjAbuAdYKIWqJfaSUH0opB0kpB3lf49rq1sBkMmE0GtFoNMTF9UKr0TIwJAN7L1ec/VxxcXVlX6qy41NNCB2HFlNnR0ay4b33ICMDjEZlzqB3b2VCmWtXZ+/atQs3NzeLNvq1116r8/NJKRk7diwFBQWWc5s2bUIIwYkTJ6pdr+qOYIC5c+dalNwGg4Hnn3+ePn36EBYWxpAhQ2rtebgeli1bRlBQEMHBwfz44491ttmxYwcDBgwgKiqK4cOHWzb/AXzxxReEhIQQGhrKvfcqU5vZ2dlMmlTnd9UOi9VJQQhxrTupMgC/Kse+KMtaa7bZLKU0SClTgJMoSaJdc3L/HoQQFBbasXu3A3pZzvChyXj7OWA2m3FxccFklmpC6GC0iDq7vJy4L77g7tGjlfmC7t0VvXUjy2OOGDHCsmP4lVdeqbPNli1biIyMtJSCBWVn8/Dhw6/JIvryyy9z7tw54uPjiY+P59tvv6WwsLBR8ScmJhIbG0tCQgJbt27liSeeqFb3oZLHH3+czz//nLi4OO69916WLl0KKLuqly1bxu7du0lISGDlypUAeHt74+PjU79htgNy1aQghBgihPgdSKo4jhRCvGvFtQ8AfYQQAUIIe2A28E2NNl8DYyqu2wVlOOnMNcTf5kj5fi2GlL108enKr796kFdm5oaAC/T39MRkNGJvb0d8Vj5azfWJ0FTaB82mzj52jKipU0m+fJkdqan0HzeO8IiIxqmzraSmOruoqIjdu3fz0UcfWZ0USkpKWLNmDe+++y4OFYmsW7duzJo1q1Gxbd68mdmzZ+Pg4EBAQABBQUHs37+/VjshhKWnk5+fb/EjrVmzhgULFljMqFVtr1OnTuXzzz9vVHztCWt2ZKwCbkN5gCOlPCqEGHO1N0kpjUKIJ4EfUZakfiylTBBCvAYclFJ+U/GzCUKIRMAE/FFKefk6P0uro2gt9JwI8ufEqRK+32FPqb6c2/sl0unGXhSKLNy0WZiMxYwOqTm9otKUJB9qXCWvuggcGG1VuyZVZ0sJeXl09fK6os7++mvKDAZG9+3bOHV2FX777TciIyPp0aMHb775JqGhobXa7N69u5r+4euvv2bSpEn07dsXT09PDh8+zIABAxr83Zw+fRp/f/9qvY36ePrpp9m5c2et87Nnz+b556tXBM7MzGTo0KGW40p1dk3Wrl3L5MmTcXJyonPnzuzduxfA4p26+eabMZlMLFmyxDJsNGjQIF566aWrxttRsCYpaKSUaTVsi1bNaEkptwBbapx7pcqfJbC44tXuMZuMeOn8cD9nJD0rCFNxd7p4lTJ7nBPabl0w5yZwKi8CrbMbOt317yxVuTrWPsCbkiZXZ5eXK3rrkhJlyWklOh0nExIap86uwoABA0hLS8PV1ZUtW7YwdepUkpKSarXLycmhU6dOluOYmBjL/WbPnk1MTAwDBgxoMnX2ihUrrG5rjTq78ppbtmwhOjqa5cuXs3jxYtauXYvRaCQpKYldu3aRkZHBiBEjiI+Px93d3ebU2dbMKaQLIYYAUgihFUL8H6CW46yLrKOUlRko8DOy61h3ikySqGH5aDTKJKGLswtSdGHMjf6tHalKM1A5p5CWloZer7fMKYSGhtaa+K1LnW1BSkVRceaMkhB0ulp6ikars6vQuXNnXCuuP3nyZAwGQ53lMXU6HWazGYDLly/z008/MX/+fHr16sXy5cvZsGEDUsoG1dlBQUGcPXvWqjmEp59+2jL5XfVVOexWFWvU2dnZ2Rw9epToaOULw913382ePXss77/zzjuxs7MjICCA4OBgS2JU1dm1eRzlm7w/cAEYWnFOpQopcYeQlx0ocu9DXqorBw90wmQ0M/cm5S+/2WzGWd1ZahM0Sp1dVoY5OZm333hDSQ7u7hAYCDWGWxqtzq7C+fPnLUlm//79mM1mvOqQ5gUHB3PmjDLlt3HjRubMmUNahXY7PT2dgIAAfv31V/r06UNWVhbHjx8HIC0tjaNHjxIVFYWzszMPP/wwCxcuRK/XA8ow17p162rdb8WKFXWqs2sOHYGizo6NjaW8vJyUlBSSkpIYMmRItTYeHh7k5+dbektV1dlTp061DFVdunSJU6dOWSb7VXV2bYxSytnNHkk7JiXuEGQacO7kSJm/K/v+0wOJIGRoGcERWgxnTDg4OHDiYjFaB3WC2Ra4LnX27NmU5OcjgCljxii7kusR2F2zOtvHhwEDBtS5Imfjxo28//776HQ6nJyciI2NrbOHMWXKFHbt2kVQUBAxMTG1Hs4zZsxg/fr1jBgxgnXr1vHQQw9RVlaGnZ0da9euxa1iY93SpUt56aWXCAkJwdHRERcXl3qXwVpLaGgos2bNIiQkBJ1Ox+rVqy3SvsmTJ7N27Vp69OjBmjVrmDFjBhqNBg8PD0t1tYkTJ/Lf//6XkJAQtFoty5cvtyRGVZ1ds4EQyShLRTcAX0kpG7d2rJG0FXV22ckcpEn53Z1PPoVzdx9MxT9xwX4Uc5/qRG6xM0+8ns/9gZcoTUvjZM8wXHRHGR1xJ0Ko6oGmpsOos7OylKWmTSSwa0rOnTvHnDlz2LZtW2uH0qKMHDmSzZs3W1YmtQcao862pvJaILAUGAj8LoT4Wghh8z0HaZI4hXhxXp+KvhtccixDmh3ZuDOb4iIdnt1KudcjCVNSEtLVFa2dPeE9a+3LU7FlTCY4f16ZN6jEx0fZe9DGEgIoPY9HHnmk2ua1jk52djaLFy9uVwmhsVj1lVVKuUdKuRAYABQAtrNo9yoYDQZcS1IwJhylqNiB337xwcko6B+dBvpyCAriiNYbJ3v71g5VpS1RVKRMJOfkKImhssd+jSt0WppZs2ZZtZy0o+Dt7V2rNGlH56pzCkIIVxSR3WygH7AZuKmZ42oXpMQdoqSklNILEk3AIC7qXUk5o8XNrpzQCRJdSLhSZrNcMvrGrly6lNDaIau0NpW9g/x85djRUdFbt/FkoGI7WDPRHA98C7whpfylmeNpF5SdzEFoBWUlpZSUdcFFZOPWzYFly3MAb24bepGevoqHRkqJg6Naa1kFRWB3/rziK6qskezlpSYElTaFNUmht5TS3OyRtCMq5xMKfijCuSgTHz+4UKTjwE4vHKWOyUMzSCMYs9mMRqPBTlNAbu5eavsAVWwGk0nZiGYyKQI7H59G+4pUVJqDepOCEOItKeUzwH+EELWeZtZUXuvInNz/G+ZzR7H3CsJwwzDeetGE0aBlTEQO/sN6kqbRoNfr6dSpE6IoF53Ojc6dI9WVR7ZE1XkCrVaZQDaZwMND7R2otFkaekJtqPjvP1EqqNV82Sznz5ymNHkPvjf4YegaybZtl9m71w1HrZFHpqYgKibizGYzR86Xo9WCRmOHVms7uyJtkWrq7ClTyPv9d7isqLwSEhIYO20afYcOpU/fvo1TZ0dFsWHDhlo/r+Ra1dmg6K6joqIIDQ2ttQmuko6szq66e7pv3764uysrBVV1dhWklJWKwX5Syh1VXygTzjaLwaDH3dOT0yZ/Ms5l8tlnPbAzGJk09DA+A30QDg5IsxkhBELAQD915ZEt4OTkRNyRI8T//DOednas/ugjyM2ltLi46dTZBgNxcXHcfffdTRZ3Xl4eTzzxBN988w0JCQn17nzuyOrsqrunn3rqKaZPVwZCVHV23cyr49zDTR1Ie6HkRA6FuSXkXHJEX2ok4VQI2dmd6NT5Ijc/Xo6m4huG3mDA09MTYThMuf4idnaerRy5SouQlgbnzzMsMpLM3FwICGB9bGzTqbPj4oiKiiI5OZkdO3bQv39/wsPDG6XOXr9+PdOnT8ffX3FyVdVGV6Ujq7OrEhMTwz333GM5VtXZFQgh7kZZhhoghPiqyo86AXl1v6vjYyg1kq0porOuABz78uUXTsjiEmbe/RvDeo5jd3E5BpMZe40GR0cDYMLLcyQajV1rh25TlCY2vYHdKaS2DwhQ5g4uXwazGUpKMAnBjmPHePixx0Cna5w6u4KuXbteUWd/9x1lZWWMHj26SdTZp06dwmAwMHr0aAoLC1m0aBFz5syp1a4jq7MrSUtLIyUlhbFjx1rOqersK+wHLqNUTKs6h1AIHGnOoNoqZSdzkJjR5Z/B3tODn3YEUlZsJnpgMTdMd2ZPqQGAwVoz3t27otengC5QTQitQL0P8OaiqIjS8nKi7rqL1MzMplFnN8DJkyebTJ1tNBo5dOgQO3bsoLS0lGHDhjF06FDLtSvpyOrsSmJjY5k5c6bFmwTYnDq73qRQUR4zBdjecuG0baRJIkwHyTUYKTQN4setDmhkGfMeLeOcTotRwnAnHQaDpHPnzijDpGpC6JCYzcpLp1NWEvn4KHMKCQnk5+dz2223sXr1ahYuXEhoaCg///xztbfXpc6OjIy0+vZNqc729fWlS5cuuLi44OLiwsiRIzl69GitpFCpztZoNBZ1dnx8PEIITCYTQgjeeOMNq9XZVRNMXVxLT+F61dk1J5FjY2MtyvNKVHV2BUKI/1X8N1cIkVPllSuEyGm5ENsWZ06cwuTqw9atQUizYMJN2dzQ78pfmLKyMrp06YJGo+Hw2Vy0WnXpYYejuFhRVGRmXll2WmXPQaPU2Sir1t5+++0GQ2hKdfadd97JL7/8gtFopKSkhH379tUpF+zI6mxQel+5ubkMGzas2vtUdfYVKktudmmJQNoDEsnlSyZyCGXvXnscHIzce2s6By4VcdzgQpCDCTtHO8s3IJNJMjJY/fV1GEwmuHgRKr8FC6Gc09X+Z3Rd6ux77qGkpAQhxFVVzU2pzu7Xrx+TJk0iIiICjUbD/Pnz63wIdmR1NijDYbNnz67Vu1LV2TUbCNELyJJS6oUQw4EIYJ2UslVUia2pzj7x5S+kpe9n1Y4HyMhw597pOfQamshxTz2h7sFEYaRHjx44OEh+if8vIBkeOh47O7dWideWaHZ1dlGRsiPZYFCSQZcuiqJCYzubEVV1dvsxpTarOhv4GqUUZyDwKcoehfXXE2h7pexkDqnf76OkuJx8Uz/OnHHG3d3M9DvySS3JpL9DOYPtBPb2JqQ8R2npWUy4MCZqhpoQ2jtSKjUOzp5VEoKTEwQEKN4iG0oIoKqzbQVr3EdmKaVBCDEdWCmlXCWEsKnVR9IkMXUHe+8ADn2g2C0n9D+L/Zl4THaCQV6DKCzMpnPncxgMXsRladHaq3WYOwRCgJ2d8t+uXcHT06YVFY3dT9DeUNXZdWMUQtwFPABU/nZsbkmN2SwpKSlj31EfiiXYR+SxO6AvJlMG5eXlODqW4OzsTdwFf4QORgfXvQFIpR1gMCivypraXl7g5gZqTQwVG8DaHc1jUNTZZ4QQAUBM84bV9ijILSb5twxyixzo7F7KXW4ZjHJ1oJ+uGJPJhKNTMU7OvTCapJoQ2itSQl6esrIoI0NRXIMyTKQmBBUbwZpynPHAQuCgEOJGIF1K+bdmj6yNUVZSztEkL3BwJSgiF20PHw6WHcdsNOPp6YlOq8NeVVm0X/R6Zd4gK0tZUWRD69JVVKpiTeW1EcBnQCYggO5CiAeklDZjiMo6dYrilFMcSIwEKel7YxbggMFYRn+PCDp1cqSgAHadvIhOa1uTj+0eKZWSmNnZyma0SsV15842PXegYrtY8wRbAUyWUt4spbwJmAK807xhtS30ej0lMpBLpd54uBnp7puPpmtXRNkJ7O1SKSw6glbrhMmkziW0O7Ky4MIFJSG4uUFgoPLf60gI1dTZt99OXt4VRVhCQgJjx46lb9++9OnTp02ps5cvX27RRoeFhaHVasnJqb0/tSOrswG++OILQkJCCA0N5d577wVUdXZ92EspEysPpJTHAZsZYC07mUNxUSkHErqDlIT0zUSr0yIdHBBC0rPnOLw8h+PhMVT9ZtkecXdXVhf5+UHPnnVuRLMWJycn4uLiiI+Px9PT06JLKC0tbdPq7D/+8Y+W3cLLli1j1KhReHrWHgrtyOrspKQkli1bxu7du0lISGDlypWAqs6uj8NCiA+EEMMrXu9jQ0K84rxyLpgKOHyqG7K8HKfATLp7ebA3czMuTi5otdf/EFFpBUpL4dKlK8cuLkrv4Coenmtl2LBhFkvn+vXr27Q6uyo1tdFV6cjq7DVr1rBgwQLLfoSq+nBVnV2bP6BMNP8JZU7hZ+Dd5gyqrZASd4iCfYkUFxRz7pIr5Q56bhhkxNnpN8wmIzcF3W8pr6nMJ6g9hbbCyZMnq58wm6GgACq/kXbtes01koODg61qZzKZ2LFjBw8/rJQdaevq7EpKSkrYunUr//znP+v8eUdWZ1f6kG6++WZMJhNLliyxDBup6uwqCCHCgUBgk5TyjZYJqe1gTj9EZ4+e/H5hAtLOlcDQFCZ0ySC+XMOYvo/SybU7oCQEUOcT2hLVHuDFxYqiolMnZQLZ07NZdiSXlpYSFRVFampqu1JnV/Ltt99y88031zl0BB1bnW00GklKSmLXrl1kZGQwYsQI4uPjcXd3tzl1dkOW1BdQFBf3AduEEHVVYOvQSLORIsee7N7TCX1pKf2iMkBnh1740rmz4utXE0IbxmRSkkFamrLk1MEBevWCbt2aRVFROaeQlpaGXq+3zCmEhobWmvitS519LTSlOruS2NjYeoeO4Io6G7Cos+fPn0+vXr1Yvnw5GzZsQEpptTr7alStm1z1VTnsVpXrVWfv2bPH8v4777wTOzs7AgICCA4OJikpCbA9dTZSyjpfQALgUvFnb+BAfW0buMYk4CRwGni+gXYzAQkMuto1Bw4cKFuKExvfkbF/T5TBfQtk6IBz8pNdm+Smvf+Qm49+LXeeuCC3JZyXO09caLF4VOonMTGx9slz56RMSJAyMVHKixelNJmaNQYXFxfLnw8fPiz9/PykXq+XJSUlMiAgQG7btk1KKWVJSYmcMmWKXLVqlZRSyqNHj8rAwEB58uRJKaWUJpNJvvXWW7Wuv3PnTjllyhQppZSlpaXSz89PJiUlSSmlfPDBB+XKlSullFKOGjVKHjhwQGZlZUl/f3956dIlqdfr5fDhw+WCBQvqjD0vL096eHjIoqKiej9fdHS05X7/+te/5KOPPlrt5yNHjpQ///yzLCsrk7169bL8P0lNTZX+/v4yLy9PSinlH//4Rzl37lxZXl4upZQyKytLfvbZZ/Xe1xri4+NlRESELCsrk2fOnJEBAQHSaDRWa2MwGKSXl5fl97x27Vo5ffp0KaWUP/zwg5wzZ46UUsrs7Gzp6+srL126JKWU8uDBg3LixImNiq+lqevfA3BQWvHcbujrUrmUsrgicWRj3aS0BSGEFqVi261ACHCPECKkjnadUOYs9l3L9VsCvUHPr3HuSLNkUHQ+N3dzoFS6Mz7oFowmyfiQbmoPoa1R9Rt0ly7KkFHv3i0usKuqznZycmLz5s0sXbqU4OBgwsPDGTx4cJ3q7H79+hEWFsa5c+cavH5VdXZ4eDgajaZBdfb48eMbHO/ftGkTEyZMwMXFpd42lepsUIaOpk2bVu3nlepsBwcHizo7KiqKmTNn1lJne3t7ExISQlhYGFOnTrUMcV0vVdXZkyZNqqXOzsrKQqfTWdTZkZGRfPbZZyxfvhyAiRMn4uXlRUhICGPGjGH58uV4eSmjAao6u/IHQuQBP1UeoqguKo+RUk5v8MJCDAOWSCknVhz/ueJ9y2q0W4lS3e1Z4FkpZYNe7BZRZydtR5r0HPoxg8fX3McFvZk/vvg7E28q5EhOOrOGPMyO4xcZH9KteeNQsZrjx4/Tr0cPRVPh52dzBtOWQFVntx9TamPU2Q1NNM+ocVz3koT66QmkVznOAKKrNhBC9Af8pJTfCSFq79a50u5R4FEAf/8WsI+aDZTdMIZ9GcfILzfT9YYS7uxWQJHBiIuLy3VPDqo0ExcvKruSK7/gFBQo+w9UmpSq6mxrVg91BFR1dhWklDsaee26npyWbolQ1nKuAOZe7UJSyg+BD0HpKTQyroZJ2g4aO4qKith+2A0kDB6lR0o4nn8Cz6591eWnbQWzGb7+GlauhCVLFEVFt27KjmSVZkFVZ3d8mnPnVQbgV+XYF6i6rqsTEAbsqvjm3R34Rghxx9WGkJoVswFzn4mkHkghIakLBmcdA4cXk3guAXtveyjvC1p1tVGrk54OS5dC5aodR0dl7sDO5qzuKipNSnMOvB4A+gghAoQQ9sBs4JvKH0op86WUXaSUvaSUvYC9QOsmhArKysrY+tVlzFIQPETPrT0EeqOBIX5DMKlq7LbBkSNKQvD0hL//HTw81ISgotIEWN1TEEI4SClr76OvBymlUQjxJPAjoAU+llImCCFeQ1ka9U3DV2g9CgsL+SWxK2X29vQbeI4jFzaSZzzHsawsdA62M7bY5igsvKKjuP12ZVL5zjuV4aLjx1s3NhWVDoI16uwhwEeAG+AvhIgE5kspn7rae6WUW4AtNc69Uk/b0dYE3KwkbccstJz4+XdOJ0fhLOCuwee5kKelu8MIxoRPRKt1bu0obQ+9Hv79b1i/Hj77DPz9FfngnDmtHZmKSofDmuGjVcBtwGUAKeVRlOWpHQ+zgTLf4aScdcJotKdnDwNeXhexy8pH2603Op268qjF+f13uP9+WLNG0VVUuGraIu1VnZ2fn8/tt99OZGQkoaGh/Pvf/67zuqWlpYwaNaqafXTFihU4OjqSn5/f4H2qxlRUVMRjjz1GYGAgoaGhjBw5kn37GrdNSUrJwoULCQoKIiIigsOHD9fZLiYmhvDwcCIiIpg0aRKXqsoRgTfffBMhhOX8d999x6uvvtqoD71NmwAAIABJREFU2Nob1iQFjZQyrca52k7a9k7FqqOEnYc5eUYZIgrtl8/v5x04X2iProdvKwdoY5SWwttvw7x5SnlMf38lMbTh1S/tVZ29evVqQkJCOHr0KLt27eKZZ55Br9fXavfxxx8zffp0y6YwUB6ygwcPZtOmTVbfb/78+Xh6epKUlERCQgKffPJJrYfztfLDDz+QlJREUlISH374IY8//nitNkajkUWLFrFz506OHTtGRERENflfeno627Ztq7bsfcqUKXzzzTeUlJQ0Kr72hDVJIb1iCEkKIbRCiP8D6rdqtVfMBmTQOMpKykjPV/5ShIcWcKb4JDf6eDJKnVxuOeLjYfZsZbhICHjwQYiJgQYMn22N9qTOFkJQWFiIlJKioiKlvGwddSVqqrOTk5MpKipi6dKlxMRYV7Y9OTmZffv2sXTpUjQVGwx79+7d6B3DmzdvZs6cOQghGDp0KHl5ebV2hVdqHIqLi5FSUlBQUM2P9PTTT/PGG29UGw0QQjB69Gi+++67RsXXnrBmovlxlCEkf+ACyu7j2mm4PVPRSzAYDJSWQ2KiPVCKU8+9FApBZLfI1o7QtujUSSmP2bcvvPwy1NiZaQ3Zlxq7zaY23l3GWdWuvamzn3zySe644w569OhBYWEhGzZssDywK9Hr9Zw5c4ZevXpZzlXWXhgxYgQnT57k4sWL1eoQ1EVCQgJRUVHVehv1cffdd9dWoAOLFy9mTo35pMzMTPz8rqyAr1Rn+/j4WM7Z2dnx/vvvEx4ejouLC3369LH05r755ht69uxJZGTtf+uDBg3il19+sZk9GldNClLKiyjLSTsuZgME30pZQQHfp3uQp5d09yukwHieG7vcCcV5V7+GSuOIi4PISKVncMMN8K9/QUjIdVdCs/YB3pS0V3X2jz/+SFRUFD/99BPJycnccsstjBgxotqu5UuXLuFeY5d4bGwsmzZtQqPRMH36dL788ksWLFjQZJ+1oXmTmtSl66l5P4PBwPvvv8+RI0fo3bs3Tz31FMuWLWPx4sX87W9/47///W+d11bV2TUQQqwRQnxY89USwbU0xcXFpJ12p5NWMNz/LLpCAzcV5YFZqqU2m4ucHHjhBZg/H7ZUWagWEdGo0pitQXtVZ//73/9m+vTpCCEICgoiICCgWs1lUD5bWVmZ5fjYsWMkJSVxyy230KtXL2JjYy1DSA2ps0NDQzl69KhFwd0Qd999d53q7E8//bRWW2vU2XFxcYDSSxNCMGvWLPbs2UNycjIpKSlERkbSq1cvMjIyGDBgAOfPnwdsT51tzZzCdmBHxWs30BWwer9Ce6H0RA7JuxNJP618O+rndRphNuPQwweXYUMRqmCtaZFSSQIzZ8J//6vsSDYYWjuqJsHNzY1Vq1bx5ptvYjAYuO+++/j111/Zvn07oPQoFi5cyJ/+9CdAqZH8+uuvW77Fm81m3n777QbvceONN5KammopPP/ZZ58xatSoam2io6PZtWsXly9fxmAw8OWXX9Z5LX9/f3bsUIbbLly4wMmTJ2tNdHt4eGAymSyJISYmhiVLlpCamkpqaipZWVlkZmaSlpbG4MGD2b17t+WhevDgQcrLy/Hz8yMwMJBBgwbx6quvWhJbUlISmzdvrhXXhg0bLLWjq75qDh0B3HHHHXz66adIKdm7dy9ubm7Vho4AevbsSWJiItnZ2QBs27aNfv36ER4ezsWLFy2fxdfXl8OHD9O9u1JE69SpU4SFhTXwf6NjYc3wUbU+nBDiM6DDaRL15Xpy7B3IPu+Ih64YV894LnXywMXKEowq18D58/D661BR4IToaHjxRajxza49U1Wd/cADD7B582aeeuopFixYgMlk4oEHHqhTnV1SUoIQ4qoTr1XV2UajkcGDBzeozvbx8WHAgAF1FrN/+eWXmTt3LuHh4Ugp+cc//kGXLl1qtZswYQK//vor48ePJzY2lh9++KHaz6dNm0ZsbCzPPfcc77zzDpMnT8ZsNuPq6kpMTIxlnmLt2rU888wzBAUF4ezsjJeXl0Vhfb1MnjyZLVu2WK5ZdVltVFQUcXFx9OjRg1dffZWRI0diZ2fHDTfcwCeffHLVa+/cuZNly5ZdtV1HoV51dr1vECIQ+FFKGdQ8ITVMc6izk3bGkFvsy/+Ou/PumhsYN9jA9JHvUNb9Ru66894mvZfNEx8PTzwBJSXKhPLixXDbbY0enqtLFazStBw5coS3336bzz77rLVDaTEuXLjAvffea+lJtReaS51deaFcrthNNUAO8Hz972h/mKWkh1s3LlxQVk5E+J5H79YJnerSaXr69lVMpr16wXPPKYVwVNoF/fv3Z8yYMZhMJqtWD3UEzp49y1tvvdXaYbQoDSYFocxSRQKZFafM8lq7Fm2cyjqs5fpyDu0tB+FMeN9CLpg86GTXviY62yQmE2zYoPQGOncGe3v46CPlzyrtjnnzbKtU++DBg1s7hBanwdnTigSwSUppqnh1qIQAyqRery7OXM7Tcv6iI84uGlKKfuJCgRkXb9tZcdAsnDqlbDx7+23lVYmaEFRU2izWfBXeL4QYIKWsWybSAdDr9Rw/7QzSjKfXBQwOMKJPFIEtUOStQ6LXw9q18P/+n9JT6N4dJk5s7ahUVFSsoN6kIITQSSmNwHDgESFEMlCMUlFNSinrrwLezijN0LLrqD15JkFYYBEeAwLQ6QtR6gCpXBPHjsFrr0FqqjJ5PGsWPPkkOKt2WRWV9kBDPYX9wACgQ9eik1Ji1BtJzHLBTgvzIwu4LCVCm4uzs6q3uCbS05VNaGazsiv5lVeUXcoqKirthobmFASAlDK5rlcLxdfsmM1mcorsuXjWjJ2dGVPnw+g0OrRaLXZ2avH3a8LPD6ZNU8ymMTE2lxDaqzo7NzeXadOmERERwZAhQ4iPj6/zulJKxo4dS0FBgeXcpk2bEEJU2wG9a9cubrvttmrvnTt3Lhs3bgQU3cTzzz9Pnz59CAsLY8iQIbX2PFwPy5YtIygoiODgYH788cc62+zYsYMBAwYQFRXF8OHDLZv/AL744gtCQkIIDQ3l3nuVpejZ2dlMmjSp0bG1JxpKCt5CiMX1vVoswmbGZDIRf8YDJAT0KsTsLAiz719LCKZSBwUFylDRkSNXzj3/vLIPwd6+9eJqJdqrOvv1118nKiqKY8eO8emnn7Jo0aI6223ZsoXIyMhqTqSYmBiGDx9ObGys1fd7+eWXOXfuHPHx8cTHx/Ptt99SWFjYqM+QmJhIbGwsCQkJbN26lSeeeKLOjXqPP/44n3/+OXFxcdx7770sXboUUFYhLlu2jN27d5OQkMDKlSsB8Pb2xsfHp17DbEekoSefFnBFGViv69UhMGce4fdUT0DSK7CAkp7eSL0WV3cHNBrVd1QvP/0Ed90F33wDb7yhaCtAdURV0J7U2YmJiYwbpwgEK/UZFy5cqNWupjq7qKiI3bt389FHH1mdFEpKSlizZg3vvvsuDg4OAHTr1q3RBtLNmzcze/ZsHBwcCAgIICgoiP3799dqJ4Sw9HTy8/MtfqQ1a9awYMECPDyUWipVba9Tp07l888/b1R87YmG5hTOSSlfa7FIWgmjvpyjGTcgDGX0Ci7BUGrC7JiF1l59uNXJ5cvwj38oSQEgKkrRW7exZPDfS/lXb3SNTOjiZlW79qbOjoyM5KuvvmL48OHs37+ftLQ0MjIy6NatW7V2u3fv5oMPPrAcf/3110yaNIm+ffvi6enJ4cOHGTCg4fUnp0+fxt/fv1pvoz6efvppdu7cWev87Nmzef756vtnMzMzGTp0qOW4Up1dk7Vr1zJ58mScnJzo3Lkzeysq+VV6p26++WZMJhNLliyxDBsNGjSIl1566arxdhQaSgpt6195c5C0ndxCHWcztOg0Roxh2eiMRegcs/HwGoAQtrFr0yqkhO+/V/YbFBQoq4meegpmzIA2ONRm7QO8KWmv6uznn3+eRYsWERUVRXh4OP3796+zyE5OTg6dOl0ZJIiJibHcb/bs2cTExDBgwIAm+6wrVqywuq016uzKa27ZsoXo6GiWL1/O4sWLWbt2LUajkaSkJHbt2kVGRgYjRowgPj4ed3d3m1NnN5QUWl5I38KYjeX8flH55uTnmw/OGvrIIDp18sLF5YZWjq6NUVgIK1YoCeGmm+DPf4YaFkpbp3JOIT8/n9tuu43Vq1ezcOFCQkND+fnnn6u1rUudXVeBl/poSnV2586dLQI5KSUBAQEEBATUaqfT6TCbzWg0Gi5fvsxPP/1EfHw8QghMJhNCCN54440G1dlBQUGcPXuWwsLCagmmLq6lp2CNOjs7O5ujR48SHR0NKEmysjfg6+vL0KFDsbOzIyAggODgYJKSkhg8eLCqzq5ESpnTkoG0BiazmaQkVwB6+WZjMkrM5RpVk12J2axsPgNlF/KLLyoTy++8oyaEBmhv6uy8vDxLTea1a9cycuTIOod3goODOXPmDAAbN25kzpw5pKWlkZqaSnp6OgEBAfz666/06dOHrKwsjh8/DkBaWhpHjx4lKioKZ2dnHn74YRYuXGi557lz51i3bl2t+61YsaJOdXbNhACKOjs2Npby8nJSUlJISkpiyJAh1dp4eHiQn59v+T1XqrNBmTeoTECXLl3i1KlTlsl+W1Nn2/TTz2Q0kprqDFLi55ODyWDCzk7Syd2htUNrfVJT4ZFHoKpaeOxYmDy5zc0ftEWqqrOdnJzYvHkzS5cuJTg4mPDwcAYPHlynOrtfv36EhYXVqi9ck6rq7PDwcDQaTYPq7PHjx9c73n/8+HFCQ0O58cYb+eGHH3jnnXfqbDdlyhR27doFKENH06ZNq/bzGTNmsH79ehwcHFi3bh0PPfQQUVFRzJw5k7Vr1+LmpgzpLV26FG9vb0JCQggLC2Pq1KmWIa7rJTQ0lFmzZhESEsKkSZNYvXq1Rdo3efJksrKy0Ol0rFmzhhkzZhAZGclnn31mUXZPnDgRLy8vQkJCGDNmDMuXL8fLywtQ1NmNrSHdnrhmdXZr05Tq7Nx9XzJy3nhEsWDc0/+jc9/L3NW1M33CRuDg0O3qF+iIGI3w6aewZo1S9KZHD9i4sc0vMVXV2c3PuXPnmDNnDtu2dbhyKg0ycuRINm/ebFmZ1B5ojDrbpnsKyWftMRi0dHIpp8w5nwiHLHr1HWu7CeHkSZgzB957T0kId94J69a1+YSg0jL4+PjwyCOPVNu81tHJzs5m8eLF7SohNBabdUPLpG3En1Ymunq4XaRTZw03dgvCxbXlV620OkYjfPCBIrAzm5XewUsvQY0xWRWVxu4naG94e3szdWqHNv3UwmaTgtlQTkKOsq65q8cFHLp74eDY4UpPW4dWq1REkxLuuQcef1wV2Kmo2Cg2mxSMJhPJyfZICb7el/DxjMLRIaW1w2o5SkqguBi8vZWJ45dfhkuXICKitSNTUVFpRWxyTsGoN3EmGU6f1CHLjPh4Z4OUdW7Y6ZD89puitH7ppSt6ih491ISgoqJimz0Fk9HM/pNmyo0mvDvncS6okB5Cg7ajJ4X8fGVH8vffK8ceHso5d9UGq6KiotCsPQUhxCQhxEkhxGkhRK0dJxXG1UQhxDEhxA4hRMtsI07+HymZbmgNZm7wysPo1x17B/uO6/WQEnbsUAR233+vrCZauFDZg6AmhCbl/PnzzJ49m8DAQEJCQpg8eTIffvhhLZW0ikpbpdm+GgtFHLQauAXIAA4IIb6RUiZWaXYEGCSlLBFCPA68ATSdE7gukraTfD6Xk2eDEIBvYAlBng4WY2OHQ0plmKjSLz9ggHLsr9YabWqklEybNo0HH3zQYg2Ni4vj22+/beXIVFSspzl7CkOA01LKM1JKPRAL3Fm1gZRyp5SypOJwL+DbjPEomA2Yu4Vw/oIXWnsH/Lvnc9zoiDSkIDHT4TyAQkDv3spqoj//Gf71L9tICIMG1f/66qsr7b76quG218DOnTuxs7OrtrM4KiqKESNGUFRUxMyZM7nxxhu57777LO6i1157jcGDBxMWFsajjz5qOT969Giee+45hgwZQt++ffnll18AxcD67LPPEh4eTkREBO+++y4Ahw4dYtSoUQwcOJCJEydedUe0ikp9NGdS6AmkVznOqDhXHw8DdZZfEkI8KoQ4KIQ4mJ2d3ejA9HrBuYuuaAT07FaAlKWM9tDg4R6NEB1g7j0rC6q65B98UNmV3EaNph2F+Pj4WorsSo4cOcLKlStJTEzkzJkzltoGTz75JAcOHCA+Pp7S0lK+++47y3uMRiP79+9n5cqV/OUvfwHgww8/JCUlhSNHjnDs2DHuu+8+DAYDTz31FBs3buTQoUPMmzePF198sfk/sEqHpDlnVuv6yl2nU0MIcT8wCBhV18+llB8CH4KiuWhsYEd+u4DZ7IO7WxG55uPYaYJwcOyCTufa2Eu3LmYzbNgAq1eDgwN8+SV4eoJOB1WKhtgE1qpQpk9XXs3MkCFD8PVVOsKVeu3hw4ezc+dO3njjDUpKSsjJySE0NJTbb7+9IjQlroEDB5KamgrA9u3b+cMf/mBZKefp6WmpYFap6jaZTPiowkKV66Q5k0IG4Ffl2BeoJSUXQowHXgRGSSlbZPdYapoLdhoHenll4h2Qj9atKw72jRNytTpnzsDSpXDsmHI8cqTaK2hhQkNDLXWIa1J1zkqr1WI0GikrK+OJJ57g4MGD+Pn5sWTJEsrKymq9p7I91F2bQUpJaGgov/32W1N/JBUbpDmfGgeAPkKIACGEPTAb+KZqAyFEf+AD4A4p5cVmjKUaZzNd0RvN+ATkY3R1xMM9Eju7droKx2iEjz6C++5TEoK3t7Ls9PXX1ZVFLczYsWMpLy9nzZo1lnMHDhzgf//7X53tKxNAly5dKCoqqjehVGXChAn861//siSJnJwcgoODyc7OtiQFg8FAQkJCYz+Oio3SbElBSmkEngR+BI4DX0gpE4QQrwkh7qhothylDvSXQog4IcQ39VyuScnPtwezmchuF5EagU5n1xK3bR5efBHef18R2E2bpgwZjRzZ2lHZJEIINm3axLZt2wgMDCQ0NJQlS5bUKvZSibu7O4888gjh4eFMnTqVwYMHX/Ue8+fPx9/fn4iICCIjI1m/fj329vZs3LiR5557jsjISKKiotizZ09TfzwVG8H21Nknf2DcIyGcTfHg7b/upLxfCp0DH2qV8o1NQlwc/OUv8MILYMVDpaOiqrNVVK6gqrOvkYJCpWfQyaUEO0evVo7mGjl8GD788MpxVJSyssiGE4KKikrT0cG9DrX55cApCgsHIwS4upZRat9ObKDFxbBqFfznP8rxoEHKRjRQLKcqKioqTYDN9RSKSzXo7DpjZ2fmnPE0jnaOrR3S1dm9W1FU/Oc/yvLSRx+F8PDWjkpFRaUDYnM9hcJiBySSTi4GzNLMTX438b/C1o6qHvLy4K234IeKPX2hofDKKxAY2LpxqaiodFhsKimkxB2iqNSJMoMJH/ciTjh0xS7XiKNdG/UerVmjJAQHB3jiCaUAjrr3QEVFpRmxqaRgTj+EnVMQUoLwkmi1nZjcvY3t/JRS8RUBPPYY5OTAggXg2/xaKBUVFRXb+tppNpJvFwxSIuzzGKxtQ+NGUsKmTTBvHuj1yrnOnWHZMjUhqKiotBg21VMAyM/XYDCV4exSSni/0a0djkJGhqKoqNx/sW0bTJnSujGpqKjYJLbVUwAKCrRIzHh6GnHo1KV1gzGb4fPP4e67lYTg4aHoKSZPbt24VK4bIQQPPPCA5dhoNOLt7d3sRXa0Wi1RUVGEhYVx++23k5eXZ/lZRkYGd955J3369CEwMJBFixahr+yNUndhoFOnTtW6R2lpKaNGjcJkMlnObdq0CSEEJ06csJxLTU0lLCys2nuXLFnCm2++eU33u1a2bt1KcHAwQUFB/P3vf6+zzYoVKwgNDSUsLIx77rmnmmsqLy/Pojfv169fk7ikrImpoTbz5s2ja9eu1X6fer2ekSNHWlQnTY3NJYX8fGVNv4uzGWfnVhyWOXMGHnoIVqyA8nK49VZFUTFhwpU5BZV2h4uLi0WDDbBt2zZ69mzIGN80ODk5ERcXR3x8PJ6enqxevRpQZHnTp09n6tSpJCUlcerUKYqKiixq7crCQKNHjyY5OZnExERef/11Lly4UOseH3/8MdOnT0dbZV9MTEwMw4cPtxQVuhrXcr9rwWQysWDBAn744QcSExOJiYkhMTGxWpvMzExWrVrFwYMHiY+Px2QyVYt70aJFTJo0iRMnTnD06NEGd8jv2rWLuXPnNjqmq7WZO3cuW7durfYee3t7xo0bx4YNG672a7kubHD4SKAxm3DpZKhlm2xRTpyAhARFaf3CCzB8eOvF0sG4xto4VmOtXeXWW2/l+++/Z+bMmcTExHDPPfdYiuSsW7eOVatWodfriY6O5r333kOr1TJ16lTS09MpKytj0aJFPProo6SmpnLrrbcyfPhw9uzZQ8+ePdm8eTNOTk4N3n/YsGEcq7Dl/vTTTzg6OvLQQw8BSo9ixYoVBAQE8Je//IW9e/fWWRioLj7//HPWr19vOS4qKmL37t3s3LmTO+64gyVLllz1d1NfIaLGsn//foKCgujduzcAs2fPZvPmzYSEhFRrZzQaKS0txc7OjpKSEouXqqCggJ9//plPPvkEUB689vb2zR7T1dqMHDnSok2vytSpU/nzn//Mfffd16gY68LmegoFBVo0GOncvRUkeLm5V/58663wpz/BF1+oCaGDMXv2bGJjYykrK+PYsWNER0cDio9mw4YN7N69m7i4OLRaLZ9//jmgfAs/dOgQBw8eZNWqVVy+fBmApKQkFixYQEJCAu7u7vynckd7PZhMJnbs2MEddyjOyYSEhFqFfzp37oy/vz+nT59usDBQVfR6PWfOnKFXr16Wc19//TWTJk2ib9++eHp6cvjw4atex9r7AYwYMYKoqKhar+3bt9dqm5mZiZ/fFVO/r68vmZmZ1dr07NmTZ599Fn9/f3x8fHBzc2PChAkAnDlzBm9vbx566CH69+/P/PnzKS4urnWf6OhooqKimD9/Pt98840lph8ry91eY0zWtKmLsLAwDhw4cNV214NN9RQyC8rJy9MihBl3jxasn1BWppTB3LgRPvsMAgKUIaJZs1ouBhuiMb7EpiAiIoLU1FRiYmKYXGV+aMeOHRw6dMhiQy0tLaVrRfGjVatWsWnTJgDS09NJSkqie/fuBAQEWL5JVy22U5PS0lJL8Z6BAwdaCu7UVX+hofP1cenSJdxrqNhjYmL4v//7P0BJhDExMQwYMKDe615rz7yyd2UNdYk9a94vNzeXzZs3k5KSgru7O3fddRfr1q3j/vvvx2g0cvjwYd59912io6NZtGgRf//73/nrX/9a7Rr79u0DlOGjTz75xNKzuN6YrGlTF1qtFnt7ewoLC+nUqdNV218LNpUUTGa4eFkHSNzcW8gXdPCgsrIoI0PZeHbkiJIUVDo0d9xxB88++yy7du2yfOuXUvLggw+ybNmyam137drF9u3b+e2333B2dmb06NGWCdCaxXkq5ypqUjmnkJ+fz2233cbq1atZuHAhoaGhtXoXBQUFpKenExgYyMWLF62q4+Dk5FRtUvby5cv89NNPxMfHI4TAZDIhhOCNN97Ay8uL3Kq9YpS6DwEBAfj6+lp1P1B6CoWFtZeNv/nmm4wfP77aOV9fX9LTr1T/zcjIqKUs3759OwEBAXh7K18Ip0+fzp49e7j//vvx9fXF19fX0qubOXNmvRPD1mJNTNa0qY/y8nIcHZte02NTw0dF+aAvLsTFqRwHx2bOh0VFykqiP/xBSQhBQfDJJy1S+lGl9Zk3bx6vvPIK4VUcVePGjWPjxo1cvKjUk8rJySEtLY38/Hw8PDxwdnbmxIkT7N2797rv6+bmxqpVq3jzzTcxGAyMGzeOkpISPv30U0AZXnrmmWeYO3cuzs7OVhcG8vDwwGQyWRLDxo0bmTNnDmlpaaSmppKenk5AQAC//vorrq6u+Pj4sGPHDsvn3Lp1K8OHD7+mQkS//PILcXFxtV41EwLA4MGDSUpKIiUlBb1eT2xsrGUIrRJ/f3/27t1LSUkJUkp27NhhmUzu3r07fn5+nDx5ElB6dTXnI6oyevToBnsJ1sZkTZu6uHz5Mt7e3tjZNf0wuE0lhdwcOySg83bGoTnNonFxytDQV18pArs//EEZNmrgL5lKx8LX15dFixZVOxcSEsLSpUuZMGECERER3HLLLZw7d45JkyZhNBqJiIjg5ZdfZujQoY26d//+/YmMjCQ2NtZS+OfLL7+kT58+9O3bF0dHR15//XXg2goDTZgwgV9//RVQho6mTZtW7eczZsywTER/+umnLF26lKioKMaOHcurr75KYGDgNRcishadTsc///lPJk6cSL9+/Zg1axahoaEATJ48maysLKKjo5k5cyYDBgwgPDwcs9nMo48+arnGu+++y3333UdERARxcXG88MILte5TOadQ81XXnII1MTXUBuCee+5h2LBhnDx5El9fXz766CNAmbCf3FxL16WU7eo1cOBAeb28+8xaecMNWXLYbakyPy7zuq9zVc6elXLYMCnnzpUyObn57qNiITExsbVD6PAcPnxY3n///a0dhoqUctq0afLEiRP1/ryufw/AQWnFM9am5hQKS+1BmHF1s0OjbcKPLiXs2wfR0coEsp+fUjc5OFgV2Kl0GPr378+YMWMwmUzV9iqotCx6vZ6pU6cSHBzcLNe3qSdWkV4AEhd3DZqmelhfuABPPw1PPgnffnvlfL9+akJQ6XDMmzdPTQitjL29PXPmzGm269tMT8FQZiA3z4kyYY+7h0TQyI1rZjN8/TWsXAklJeDqCs0w6aOioqLSkthOUsgvpLjYAbNWMLBbI7/pnD2rLDOt3KwzejQ89xx4t+DeBxUVFZVmwGaSgpRmissdkULQpYsHeRZJAAANxklEQVQGMF/fhY4dU1YT6fXg6ansSh43TvUVqaiodAhsJimYzMUUldsDgi5dtFx3UujXD/z9lUnkxYvBza0pw1RRUVFpVWwmKQAUFbuiNWlxKyxBaK2UXen1sG6dsunM3V2ZN/j4Y3B2bt5gVVRUVFoB20oKJcqDvPugzjj6WfFQ//13+OtfFc11SoryZ1ATgoqKSofFZpKC2QzFpU5gB56eV5loLi2F99+HmBhlD4K/v6qnUFFRsQlsJink5Wso1djh4GTEwaGBpLB/v7KyKCtL2Wfw4IPw6KPQSLe6SsuSnp5OeXl5k13PwcGhmuK4KZg3bx7fffcdXbt2JT4+3ur35eXlsX79ep544ok6f75kyRJcXV159tlnrbretbZX6djYzO6qvHwNEoGLe1H9m2/OnoUFC5SE0LcvfPqpsilNTQjtjvLycpydnZvsda0JxprKXHVV1bKGvLw83nvvvWt+n4qKNdhUUgBwaqjimr8/3HMPPPGEkhBuvLEFI1SxNUaOHImnp2eDbYqLi5kyZQqRkZGEhYWxYcMGnn/+eZKTk4mKiuKPf/wjAH/7298IDg5m/PjxFtNnQzTUft26dQwZMoSoqCgee+wxTCYTzz33XLVEtGTJEt56663r+NQqbR2bGT7KzVOSgrPrlYLl5OTA8uUwY8aVGo6LF7dCdCodhejoaMrLyykqKiInJ8dSIOcf//gHEydOvObrbd26lR49evD9998DkJ+fT3R0NPHx8cTFxQFw6NCh/9/e/QdJXddxHH++0JOTRDLMhjw5aARLUOm8jGSw1GKATNK58Y4RkQZz1KjIzkxtJrT+MNFxUkkyctSGAryRIsMxR0EZFRVFQVETEe2GDLjQUE7jx7s/Ph/Wddm7+97d/rjdfT9mbmb3u5/d7/uze7vv/X4+3/28WbRoEWvXrmXPnj3U1dV1Wt2ss/bp1eGqqqq47LLLWLhwIU1NTcyePTs1ZLVkyZIeHeW4vq9iksI77/QD28thg3aHyeMHHoAbb4T//hfefBMWLvQfoLle605lriROOOEEmpubufLKKznrrLMYP378AQVsVq1axTnnnMOAeFZcV+vxd9a+o+pw06dPZ+vWrWzZsoVt27ZxxBFHMHTo0F71zfVNeU0KkiYCvwYOAhaY2fUZt/cH7gFOBtqARjPbnI9YwpzCXo77ZDv88IfwxBPhhrFj4eqrPSG4PmnkyJE8++yzLF++nKuuuooJEyZkXQytu6UuO2pvHVSHg1CNrKWlhbfffpumpqZu7c+VjrzNKUg6CJgHTAKOB6ZKyqwyMxPYYWbHAjcDv8pXPDt2iEN3tXPi0paQEA4/HObMgVtvhV4W+HAuU5LKXEls2bKFAQMGMG3aNJqbm3nuuecYOHDgx8pUnnbaaSxdupT29nZ27tzJX9NX682is/YdVYeDUId50aJFtLS00NDQ0Ou+ub4pn0cKpwAbzWwTgKRFwBRgQ1qbKcCceLkFuE2SYkGInHpn+14GvPcegw9rgzPOCAvYDR6c6924PqJ///7s2rUrp4+XxP45hUzZ5hSmTp3KypUr2b59OzU1NVx77bXMnDnzY23Wr1/PFVdcQb9+/aiqquL2229n8ODBjBs3jtGjRzNp0iTmzp1LY2MjY8aMoba2lvHjx6fuP3nyZBYsWPCxymZ1dXUdtk+vDrdv3z6qqqqYN28etbW1jBo1ip07d3L00UczZMiQTvfhSpfy8PkbHlhqACaa2UXx+gXAl81sVlqbF2Ob1nj99dhme8ZjXQxcDDB06NCT939z6Y6fNH/IXxa/xR+u3MYps07tabdcH/Xyyy+n6u06V+myvR8kPWtm9V3dN5+npGYbtMzMQEnaYGZ3mFm9mdV/uofLU99wY39e/ecITwjOOdeJfCaFViD9J6A1wJaO2kg6GBgE/CePMTnnnOtEPpPCM8AIScMlHQI0Acsy2iwDLoyXG4BH8jGf4CqD/+s41/v3Qd6SgpntAWYBDwIvA0vM7CVJ10naf2L074HBkjYClwM/zVc8rrxVV1fT1tbmicFVNDOjra2N6urqHj9G3iaa86W+vt7WrFlT7DBcH7N7925aW1v54IMPih2Kc0VVXV1NTU0NVRk145NONFfML5pdeauqqmL48OHFDsO5klcxC+I555zrmicF55xzKZ4UnHPOpZTcRLOkbUD3f9IcHAls77JVefE+Vwbvc2XoTZ9rzazLX/+WXFLoDUlrksy+lxPvc2XwPleGQvTZh4+cc86leFJwzjmXUmlJ4Y5iB1AE3ufK4H2uDHnvc0XNKTjnnOtcpR0pOOec64QnBeeccyllmRQkTZT0qqSNkg5YeVVSf0mL4+1PSRpW+ChzK0GfL5e0QdI6SQ9Lqi1GnLnUVZ/T2jVIMkklf/pikj5LOi++1i9J+mOhY8y1BP/bQyWtkLQ2/n9PLkacuSLpTklbY2XKbLdL0i3x+VgnqS6nAZhZWf0BBwGvA58DDgFeAI7PaHMZMD9ebgIWFzvuAvT5dGBAvHxpJfQ5thsIPAasBuqLHXcBXucRwFrgiHj9qGLHXYA+3wFcGi8fD2wudty97PNpQB3wYge3TwYeIFSuHAs8lcv9l+ORwinARjPbZGb/AxYBUzLaTAHujpdbgDMlZSsNWiq67LOZrTCz/ZXsVxMq4ZWyJK8zwC+AG4ByWFM7SZ+/C8wzsx0AZra1wDHmWpI+G3B4vDyIAys8lhQze4zOK1BOAe6xYDXwSUlDcrX/ckwKRwP/TLveGrdlbWOhGNC7wOCCRJcfSfqcbibhm0Yp67LPkr4IHGNm9xcysDxK8jqPBEZKelzSakkTCxZdfiTp8xxgmqRWYDnw/cKEVjTdfb93SznWU8j2jT/zvNskbUpJ4v5ImgbUA1/Na0T512mfJfUDbgZmFCqgAkjyOh9MGEL6GuFocJWk0Wb2Tp5jy5ckfZ4K3GVmN0n6CvCH2Od9+Q+vKPL6+VWORwqtwDFp12s48HAy1UbSwYRDzs4O1/q6JH1G0teBa4CzzezDAsWWL131eSAwGlgpaTNh7HVZiU82J/3f/ouZ7TazN4BXCUmiVCXp80xgCYCZPQlUExaOK1eJ3u89VY5J4RlghKThkg4hTCQvy2izDLgwXm4AHrE4g1OiuuxzHEr5LSEhlPo4M3TRZzN718yONLNhZjaMMI9ytpmVci3XJP/bfyacVICkIwnDSZsKGmVuJenzW8CZAJK+QEgK2woaZWEtA6bHs5DGAu+a2b9y9eBlN3xkZnskzQIeJJy5cKeZvSTpOmCNmS0Dfk84xNxIOEJoKl7EvZewz3OBw4B745z6W2Z2dtGC7qWEfS4rCfv8IDBB0gZgL3CFmbUVL+reSdjnHwO/k/QjwjDKjFL+kifpT4ThvyPjPMnPgSoAM5tPmDeZDGwEdgHfyen+S/i5c845l2PlOHzknHOuhzwpOOecS/Gk4JxzLsWTgnPOuRRPCs4551I8Kbg+R9JeSc+n/Q3rpO2wjlaT7OY+V8aVOF+IS0Qc14PHuETS9Hh5hqTPpt22QNLxOY7zGUljEtxntqQBvd23qwyeFFxf1G5mY9L+Nhdov+eb2UmExRLndvfOZjbfzO6JV2cAn0277SIz25CTKD+K8zcki3M24EnBJeJJwZWEeESwStJz8e/ULG1GSXo6Hl2skzQibp+Wtv23kg7qYnePAcfG+54Z1+lfH9e57x+3X6+P6lPcGLfNkdQsqYGwvtTCuM9D4zf8ekmXSrohLeYZkm7tYZxPkrYQmqTbJa1RqKNwbdz2A0JyWiFpRdw2QdKT8Xm8V9JhXezHVRBPCq4vOjRt6Ghp3LYV+IaZ1QGNwC1Z7ncJ8GszG0P4UG6Nyx40AuPi9r3A+V3s/1vAeknVwF1Ao5mdQFgB4FJJnwLOAUaZ2YnAL9PvbGYtwBrCN/oxZtaednMLcG7a9UZgcQ/jnEhY1mK/a8ysHjgR+KqkE83sFsK6OKeb2elx6YufAV+Pz+Ua4PIu9uMqSNktc+HKQnv8YExXBdwWx9D3Etb0yfQkcI2kGuA+M3tN0pnAycAzcXmPQwkJJpuFktqBzYTll48D3jCzf8Tb7wa+B9xGqM+wQNLfgMRLc5vZNkmb4po1r8V9PB4ftztxfoKw7EN61a3zJF1MeF8PIRScWZdx37Fx++NxP4cQnjfnAE8KrnT8CPg3cBLhCPeAojlm9kdJTwHfBB6UdBFhmeG7zeyqBPs4P33BPElZa2zE9XhOISzC1gTMAs7oRl8WA+cBrwBLzcwUPqETx0moQHY9MA84V9JwoBn4kpntkHQXYWG4TAIeMrOp3YjXVRAfPnKlYhDwr7hG/gWEb8kfI+lzwKY4ZLKMMIzyMNAg6ajY5lNKXp/6FWCYpGPj9QuAR+MY/CAzW06YxM12BtBOwvLd2dwHfJtQB2Bx3NatOM1sN2EYaGwcejoceB94V9JngEkdxLIaGLe/T5IGSMp21OUqlCcFVyp+A1woaTVh6Oj9LG0agRclPQ98nlCycAPhw/PvktYBDxGGVrpkZh8QVqC8V9J6YB8wn/ABe398vEcJRzGZ7gLm759oznjcHcAGoNbMno7buh1nnKu4CWg2sxcItZlfAu4kDEntdwfwgKQVZraNcGbUn+J+VhOeK+cAXyXVOedcGj9ScM45l+JJwTnnXIonBeeccymeFJxzzqV4UnDOOZfiScE551yKJwXnnHMp/wdHCvjVEU1f9QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.29569575, 0.79363506, 0.87995268, ..., 0.2425908 , 0.71409916,\n", + " 0.89744282])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Logistic regression (scikit)\n", + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " model = LogisticRegression(random_state=0)\n", + " model.fit(X[train,:], y[train])\n", + " probas_ = model.predict_proba(X[test,:])[:, 1]\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr2.v4.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + " 0.8877751836594996\n", + ">>\n", + " 0.8965822831228838\n", + ">>\n", + " 0.8960688921756774\n", + ">>\n", + " 0.9045934122490994\n", + ">>\n", + " 0.9076875474842537\n", + ">>\n", + " 0.8974830282087102\n", + ">>\n", + " 0.9089221380780825\n", + ">>\n", + " 0.9136766904394286\n", + ">>\n", + " 0.8947411955003308\n", + ">>\n", + " 0.9068267039188295\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.52053967, 0.55151724, 0.89673963, ..., 0.33650874, 0.7162114 ,\n", + " 0.73551716])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " print('>>')\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " model = RandomForestClassifier(n_estimators=1000, random_state=0, max_leaf_nodes=200)\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train])\n", + "\n", + " \n", + " probas_ = model.predict_proba(X[test,:])[:, 1]\n", + " results[test] = probas_\n", + "\n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.rf.v4.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} |