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@arshad2101
Last active August 3, 2017 05:52
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2nd Version for demo on 3rd August with below implementation 1. Multiclass Implementation 2. Multimodel Implementation 3. Implemented for NLC, NLU and Conversation Service
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{
"nbformat_minor": 1,
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": "# Setting up input parameters\n \n User need to give below input parameters\n \n 1. Browse and upload the ground truth .csv/.json file from the DSX dashboard right column under 'Files' tab, these files would get stored in a object storage service.\n 2. Add the required ground truth files names in list provided below **groudTruthFiles=[]**.\n 3. Set the required service name methods to the variable **serviceName**. as below\n * **NLCEvaluation**: Natural Language Classifier\n * **NLUEvaluation**: Natural Language Understading\n * **ConversationEvaluation**: Conversation Service\n 4. Set the order degree of the polynomial for ploting the ROC curve by default its 2.\n \n\n"
},
{
"cell_type": "code",
"metadata": {},
"source": "groudTruthFiles = [\n 'NLC_import1.csv',\n 'NLC_import2.csv',\n \"NLC_import3.csv\",\n #\"NLC_import4.csv\",\n #\"NLC_import5.csv\",\n #\"NLC_import6.csv\"\n #'NLU_IBM_GT1.json',\n #'NLU_IBM_GT2.json',\n #'NLU_IBM_GT3.json'\n #'Conversation_import.csv'\n]\n \nserviceName = 'NLCEvaluation' \n#serviceName = 'NLUEvaluation'\n#serviceName ='ConversationEvaluation'\n\norder_degree =2 ",
"execution_count": 85,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": "# REST call to workbench code\n**callWrokbenchMethod** makes a REST call to workbench code taking input as Ground Truth and Service name, ground truth is being uploaded by the user by using the interface provided by the DSX on right panel\nThis method calls workbench REST APIs and returns the raw data in the form of json text."
},
{
"cell_type": "code",
"metadata": {
"collapsed": true
},
"source": "import json\nimport requests\nimport pandas as pd\n\ndef callWrokbenchMethod(payload,serviceName):\n endpoint = 'https://workbenchmodeleval.mybluemix.net/'\n #endpoint = 'http://localhost:9080/workbenchModelEval/'\n url = endpoint + serviceName\n head = {'Content-type':'application/json',\n 'Accept':'application/json'}\n payload = payload\n payld = json.dumps(payload)\n response = requests.post(url,headers=head,data=payld).text\n \n #Mock data if WS is down\n #response = \"1,.45,Tempreature\\n0,.45,Condition\\n0,.23,Humidity\\n0,.55,Tempreature\\n1,.15,Humidity\\n1,.85,Condition\\n1,.85,Humidity\\n\"\n return response",
"execution_count": 86,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": "# Fetch raw data from Ground Truth file.\nUser uploads the Ground Truth file from the interface provided by the DSX on the right panel, this file get stored in an object storage instance which is created by DSX itself.\n\nBelow code snippet fetches the raw data from ground truth file stored in Object storage and make call to workbench method."
},
{
"cell_type": "code",
"metadata": {},
"source": "\nfrom io import StringIO\nimport requests\nimport json\nimport pandas as pd\nimport numpy as np\n\n\n# @hidden_cell\n# This function accesses a file in your Object Storage. The definition contains your credentials.\n# You might want to remove those credentials before you share your notebook.\ndef get_object_storage_file_with_credentials_3f1dd24292234dfbb222aad421dd9b6f(container, filename):\n \"\"\"This functions returns a StringIO object containing\n the file content from Bluemix Object Storage.\"\"\"\n\n url1 = ''.join(['https://identity.open.softlayer.com', '/v3/auth/tokens'])\n data = {'auth': {'identity': {'methods': ['password'],\n 'password': {'user': {'name': 'member_60a0c38ab5d68c0f6d256fad2304aef8c439644b','domain': {'id': 'f8a071e349f845aa914cce38a36d2674'},\n 'password': 'yK3H}L0CV0v(rsyZ'}}}}}\n headers1 = {'Content-Type': 'application/json'}\n resp1 = requests.post(url=url1, data=json.dumps(data), headers=headers1)\n resp1_body = resp1.json()\n for e1 in resp1_body['token']['catalog']:\n if(e1['type']=='object-store'):\n for e2 in e1['endpoints']:\n if(e2['interface']=='public'and e2['region']=='dallas'):\n url2 = ''.join([e2['url'],'/', container, '/', filename])\n s_subject_token = resp1.headers['x-subject-token']\n headers2 = {'X-Auth-Token': s_subject_token, 'accept': 'application/json'}\n resp2 = requests.get(url=url2, headers=headers2)\n return resp2.text+'\\n'\n\n\nmodel_map={}\nclass_map ={}\n\nfor fileName in groudTruthFiles:\n payload =get_object_storage_file_with_credentials_3f1dd24292234dfbb222aad421dd9b6f('DefaultProjectmohdarshadinibmcom', fileName)\n wbResponse = callWrokbenchMethod(payload,serviceName)\n jsonToPython = json.loads(wbResponse)\n all_class_columns = []\n for k in jsonToPython.keys():\n all_class_columns.append(k[3:]) \n unique_classes = list(set(all_class_columns))\n micro_average_GT = []\n micro_average_RS =[]\n micro_average =[]\n for classes in unique_classes:\n res_Probabilities =[]\n res_Probabilities.append(np.array(jsonToPython['GT_'+classes]).astype(np.int64))\n res_Probabilities.append(np.array(jsonToPython['RS_'+classes]).astype(np.float64))\n micro_average_GT.extend(np.array(jsonToPython['GT_'+classes]).astype(np.int64))\n micro_average_RS.extend(np.array(jsonToPython['RS_'+classes]).astype(np.float64))\n class_map[classes] = res_Probabilities\n micro_average.append(micro_average_GT)\n micro_average.append(micro_average_RS)\n class_map['Micro Average'] = micro_average\n model_map[fileName] = class_map",
"execution_count": 87,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": "# Plots for ROC and Area Under Curve\n\nCalculates the Receiver Operating Characteristic(ROC) based on the **Ground Truth** and **Predicted Probabilities** Vector obtained form Waston service, Calculates the True Positive Rate(TPR),False Positive Rate(FPR) and Corresponding ROC curve.\n\nQuality of the model is determined by calculating the area under the ROC curve (AUC). In this example, quality is rated is based on the following criteria,\n \n* .90-1 = Excellent (A) \n* .80-.90 = Good (B) \n* .70-.80 = Fair (C) \n* .60-.70 = Poor (D) \n* .50-.60 = Fail (F)"
},
{
"cell_type": "code",
"metadata": {
"scrolled": true
},
"source": "from sklearn.metrics import roc_curve, auc\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport random\n\n\nplt.figure(figsize=(40,40))\ncolor = ['#0e5f56','#f2981c','#333333','#ee436e','#00c78c','#436eee','#ffa500','#008080','#a8da61','#468499','#003366','#5f1205','#d6a300','#fd7a16','#495b5b','#17cff6','#5f115f','#153ea8','#d2c9e9','#c7f439','#8db6cd','#268a47'\n ]\nfor model_key,model_value in model_map.items():\n print()\n \n fpr_micro, tpr_micro,_ = roc_curve(model_value['Micro Average'][0], model_value['Micro Average'][1])\n roc_auc_micro = auc(fpr_micro, tpr_micro)\n\n if 0.9 <roc_auc_micro <= 1:\n modelPerformace = 'Excelent'\n elif 0.8 <roc_auc_micro <= .9:\n modelPerformace = 'Good'\n elif 0.7 <roc_auc_micro <= .8:\n modelPerformace = 'Fair'\n elif 0.6 <roc_auc_micro <= .7:\n modelPerformace = 'Poor'\n elif 0.5 <roc_auc_micro <= .6:\n modelPerformace = 'Fair'\n else:\n modelPerformace = 'No Base'\n \n model_key_index = list(model_map.keys()).index(model_key)\n subplot_value = 321+ model_key_index\n model_number =model_key_index+1\n plt.subplot(subplot_value)\n plt.title('Receiver Operating Characteristic for Model %s'% model_number)\n for classes_key, classes_value in model_value.items():\n classes_key_index = list(model_value.keys()).index(classes_key)\n temp_groundTruth = []\n temp_predictedProbabilities=[]\n temp_groundTruth.append(classes_value[0])\n temp_predictedProbabilities.append(classes_value[1])\n groundTruth = np.array(temp_groundTruth)\n predictedProbabilities = np.array(temp_predictedProbabilities)\n false_positive_rate, true_positive_rate, thresholds = roc_curve(groundTruth[0], predictedProbabilities[0])\n poly_equation = np.poly1d(np.polyfit(false_positive_rate,true_positive_rate,order_degree))\n roc_auc = auc(false_positive_rate, true_positive_rate)\n plt.plot(false_positive_rate, poly_equation(false_positive_rate),'o')\n plt.text(-0.15,1.1,'Model Performace: %s with Micro Average AUC %s'%(modelPerformace,roc_auc_micro))\n line = plt.plot(false_positive_rate, poly_equation(false_positive_rate), color[classes_key_index],\n label='AUC = %0.2f '% roc_auc + classes_key)\n plt.legend(loc='lower right')\n plt.plot([0,1],[0,1],'g--')\n plt.xlim([-0.2,1.2])\n plt.ylim([-0.2,1.2])\n plt.ylabel('True Positive Rate')\n plt.xlabel('False Positive Rate')\n plt.grid(True) \n\nplt.show()\n",
"execution_count": 89,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "\n\n\n"
},
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Jvvv44xoxYoSGDRumjh07NtN3A8GY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M/ybx5fPIjPPgPLJzHp9++ulf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DKA2beO4f6RYwgLCnZ3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"text/plain": "<matplotlib.figure.Figure at 0x7fea3bce3320>"
},
"output_type": "display_data",
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": "# Max Accuracy vs Threshold.\n\nPlots for identifying the optimal Threshold for Maximum Accuracy.\n\nAccuracy has been calculated for the model for all threshold between 0-1 at the step of .001"
},
{
"cell_type": "code",
"metadata": {
"scrolled": false
},
"source": "from sklearn.metrics import accuracy_score\nfrom sklearn.metrics import f1_score\nimport matplotlib.pyplot as accuracyplt\nimport math\nimport copy\nfrom collections import OrderedDict\n\n\n\nif len(unique_classes) <= 3:\n fig_bias = 0\nelif len(unique_classes) <= 6:\n fig_bias = 5\nelif len(unique_classes)<= 9:\n fig_bias = 10\nelif len(unique_classes) <= 12:\n fig_bias = 15\nelif len(unique_classes) <= 15:\n fig_bias = 20\nelif len(unique_classes) <= 18:\n fig_bias = 25\n\n\nfor i in range(0,len(model_map.keys())):\n fig_x = fig_bias+ ((i+2)*10)\n fig_y = fig_bias +((i+1)*10)\naccuracyplt.figure(figsize=(fig_x,fig_y))\n\ncolor = ['r*','b*','g*','y*']\nacc_threshold_list=[]\nplot_count = 0\n\ntotal_classes = copy.deepcopy(unique_classes)\ntotal_classes.extend(['Micro Average'])\ndf = pd.DataFrame(index=model_map.keys(), columns=total_classes)\n\n\nfor model_key,model_value in model_map.items():\n model_key_index = list(model_map.keys()).index(model_key)\n plot_column =3\n plot_row = len(model_map.keys())*(math.ceil(len(model_value.keys())/plot_column))\n model_number =model_key_index+1\n temp_acc_threshold_list = []\n for classes_key, classes_value in model_value.items():\n classes_key_index = list(model_value.keys()).index(classes_key)\n plot_count = plot_count + 1\n accuracyplt.subplot(plot_row+1,plot_column,plot_count)\n temp_groundTruth =[]\n temp_predictedProbabilities=[]\n temp_groundTruth.append(classes_value[0])\n temp_predictedProbabilities.append(classes_value[1])\n groundTruth = np.array(temp_groundTruth)\n predictedProbabilities = np.array(temp_predictedProbabilities)\n threshold_accuracy_map = {}\n for k in np.arange(0, 1, 0.01): \n predictions_binarized = []\n for s in predictedProbabilities[0]:\n if s<=k:\n predictions_binarized.append(0)\n else:\n predictions_binarized.append(1) \n threshold_accuracy_map[k]=accuracy_score(groundTruth[0], predictions_binarized)\n \n \n def keywithmaxval(d):\n \"\"\" a) create a list of the dict's keys and values; \n b) return the key with the max value\"\"\" \n v=list(d.values())\n k=list(d.keys())\n return k[v.index(max(v))]\n \n threshold_accuracy_map_sorted = OrderedDict(sorted(threshold_accuracy_map.items(), key=lambda t: t[0]))\n acc_optimal_threshold = keywithmaxval(threshold_accuracy_map_sorted)\n temp_acc_threshold_list.append(acc_optimal_threshold)\n \n accuracyplt.plot(list(threshold_accuracy_map_sorted.keys()),list(threshold_accuracy_map_sorted.values()), color[classes_key_index%4],label='Threshold vs Accuracy %s'%classes_key)\n accuracyplt.text(.01,.9,'Model: %s'% (model_key))\n accuracyplt.text(.01,.1,'Optimum Threshold = %0.4f '% acc_optimal_threshold+classes_key)\n accuracyplt.legend(loc='upper right')\n accuracyplt.xlim([0,1])\n accuracyplt.ylim([0,1])\n accuracyplt.ylabel('Accuracy')\n accuracyplt.xlabel('Threshold')\n accuracyplt.grid(True) \n acc_threshold_list.append(np.mean(temp_acc_threshold_list)) \n df.loc[model_key] = temp_acc_threshold_list\n if(plot_count%plot_column == 0):\n reset_row = plot_column\n plot_count = plot_count + (plot_column - reset_row)\n\naccuracyplt.show()\ndf.head()\n\n",
"execution_count": 91,
"outputs": [
{
"data": {
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s2eKSzT179ujs2bOKiIjI9Tu7Z8+eXJ/LqFatmstkrhzfffed7rzzTgUHB6tc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\nd+50WWfkyJHG4XCYI0eO5Pm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LnPzzHomHbMMicn1rHEPziVz3B9nP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\nak2lnevXe0s7S6pVWe/NA+YisIpswyJyDYvIdT4w708fF2sAOffmm2+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ubFZmaDJlPmRfmlvzMOh0MREREuY9df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\nj5iwsDDj4+NjHA6Hy/K3337bNGzY0AQGBprQ0FBz8803mxEjRpiff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"text/plain": "<matplotlib.figure.Figure at 0x7fea3c1f58d0>"
},
"output_type": "display_data",
"metadata": {}
},
{
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>temperature</th>\n <th>conditions</th>\n <th>Micro Average</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>NLC_import1.csv</th>\n <td>0.39</td>\n <td>0.61</td>\n <td>0.39</td>\n </tr>\n <tr>\n <th>NLC_import2.csv</th>\n <td>0.39</td>\n <td>0.61</td>\n <td>0.39</td>\n </tr>\n <tr>\n <th>NLC_import3.csv</th>\n <td>0.39</td>\n <td>0.61</td>\n <td>0.39</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " temperature conditions Micro Average\nNLC_import1.csv 0.39 0.61 0.39\nNLC_import2.csv 0.39 0.61 0.39\nNLC_import3.csv 0.39 0.61 0.39"
},
"output_type": "execute_result",
"metadata": {},
"execution_count": 91
}
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": "# Max F1 Score vs Threshold.\n\nPlots for identifying the optimal Threshold for maximum F1 Score.\n\nF1 Score has been calculated for the model for all threshold between 0-1 at the step of .001\n"
},
{
"cell_type": "code",
"metadata": {},
"source": "from sklearn.metrics import f1_score\nimport matplotlib.pyplot as f1plt\nimport math\nimport copy\nfrom collections import OrderedDict\n\n\n\nif len(unique_classes) <= 3:\n fig_bias = 0\nelif len(unique_classes) <= 6:\n fig_bias = 5\nelif len(unique_classes)<= 9:\n fig_bias = 10\nelif len(unique_classes) <= 12:\n fig_bias = 15\nelif len(unique_classes) <= 15:\n fig_bias = 20\nelif len(unique_classes) <= 18:\n fig_bias = 25\n\n\nfor i in range(0,len(model_map.keys())):\n fig_x = fig_bias+ ((i+2)*10)\n fig_y = fig_bias +((i+1)*10)\nf1plt.figure(figsize=(fig_x,fig_y))\n\ncolor = ['r*','b*','g*','y*']\nf1_threshold_list=[]\nplot_count = 0\n\ntotal_classes = copy.deepcopy(unique_classes)\ntotal_classes.extend(['Micro Average'])\ndf = pd.DataFrame(index=model_map.keys(), columns=total_classes)\n\n\nfor model_key,model_value in model_map.items():\n model_key_index = list(model_map.keys()).index(model_key)\n plot_column =3\n plot_row = len(model_map.keys())*(math.ceil(len(model_value.keys())/plot_column))\n model_number =model_key_index+1\n temp_f1_threshold_list = []\n for classes_key, classes_value in model_value.items():\n classes_key_index = list(model_value.keys()).index(classes_key)\n plot_count = plot_count + 1\n f1plt.subplot(plot_row+1,plot_column,plot_count)\n temp_groundTruth =[]\n temp_predictedProbabilities=[]\n temp_groundTruth.append(classes_value[0])\n temp_predictedProbabilities.append(classes_value[1])\n groundTruth = np.array(temp_groundTruth)\n predictedProbabilities = np.array(temp_predictedProbabilities)\n threshold_f1_map = {}\n for k in np.arange(0, 1, 0.01): \n predictions_binarized = []\n for s in predictedProbabilities[0]:\n if s<=k:\n predictions_binarized.append(0)\n else:\n predictions_binarized.append(1) \n threshold_f1_map[k]=f1_score(groundTruth[0], predictions_binarized)\n \n \n def keywithmaxval(d):\n \"\"\" a) create a list of the dict's keys and values; \n b) return the key with the max value\"\"\" \n v=list(d.values())\n k=list(d.keys())\n return k[v.index(max(v))]\n \n threshold_f1_map_sorted = OrderedDict(sorted(threshold_f1_map.items(), key=lambda t: t[0]))\n f1_optimal_threshold = keywithmaxval(threshold_f1_map_sorted)\n temp_f1_threshold_list.append(f1_optimal_threshold)\n \n f1plt.plot(list(threshold_f1_map_sorted.keys()),list(threshold_f1_map_sorted.values()), color[classes_key_index%4],label='Threshold vs F1 Score %s'%classes_key)\n f1plt.text(.01,.9,'Model: %s'% (model_key))\n f1plt.text(.01,.1,'Optimum Threshold = %0.4f '% f1_optimal_threshold+classes_key)\n f1plt.legend(loc='upper right')\n f1plt.xlim([0,1])\n f1plt.ylim([0,1])\n f1plt.ylabel('F1 Score')\n f1plt.xlabel('Threshold')\n f1plt.grid(True) \n f1_threshold_list.append(np.mean(temp_f1_threshold_list)) \n df.loc[model_key] = temp_f1_threshold_list\n if(plot_count%plot_column == 0):\n reset_row = plot_column\n plot_count = plot_count + (plot_column - reset_row)\n\nf1plt.show()\ndf.head()\n\n\n",
"execution_count": 92,
"outputs": [
{
"data": {
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mmjVrlubPn6+srCzdddddXu3rbW8j25m/k3PpHZxpxowZCgkJUUxMjHbu3Kmd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\n+LuW/Zyy+yZff/216datm4m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DxsO2eRF/yDdb79xquu7tMtlvZY1e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\nVCqdUq32mkqlUwvOh7WTQSqVkufllMk4C85nMo48L6dUKtXUmjj2lMS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2ZN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\n++/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rEdioP+Yivn8oztbF5g/fGNgmT+79P2whuyaVlppHw2EzvrjYPBoOm0mlpX63\nFFjkGrYi27ARuYZtOCCBi60X5iKwFWsRO2UyGTNnzqJOD4IoLZ1kwuFHTXHxeBMOP2pKSydZVfN/\n/s+5ndYEue+g1ASxp1ys6Y/H6y7bbsyatcBI3zGx2D2BqPH6vpB7WIvARsxGuNh4sW0ukliaMEOH\nDzWlY0pN0eVFRvfJFF1eZEZ8YoQZOnyoSSxNuKrpKa82fcA7rEW65mbzhFc1xrj7Helu40fpFaUm\nfFPYFH+82IRvCpvSKwbmcSVkGzbqa67dvD94VeP1fbnd9Ibcw/s1bNQfc5G8bJy9A0D/eeSRR/xu\nAS6VRKNyamq08eBBOTU1KolG/W4psMg1bEW2YSNyDQAA/MRaxE7JZFJ1dSuUSqU6vD0aLVFNjaOD\nBzeqpsZRNFpiVc2uXVs7rQly30GpCWJPuVjTH4/XXbY7U129XIWF5Ro5cpK2bNkvabY2b96n0tKJ\nKiwsV3X18qzWeNkTch9rEQCAHxILE0osSqi5pVnp69OSI6WvT+voyaNKLEqcut1FjRvV91er8PxC\njbxipLZs2yJVSJtf2KzSMaUqPL9Q1fdX9/OzRVdYi3StfnW9Wsa3qH51fb/V9PR3JJlMqu6Rus5n\nHqOiqplQo4PPH1TNhBpFRw3M40rINmzU11y7eX/wqsbr++ruvQ+5i/drwB3HnPoWAes5jhOVlEwm\nk4pygDQAAAAAoAOpVEoVFRWSVGGMYWIIazAXAZBLYrF79NhjIxSL/U6NjQ/43Q4An504cUINDWu0\nbNmTSqefar2+qGiyFi++UVVV0yUpazWhUMiznkKhkJcvFQB4gtkIbGTrXKT8qnLtv3Z/65/LninT\nvuf29bhGkowxmrdgnh5+8GE5jtN6/YkTJ9SwskHLHl2mdGW69fqiTUVaPHuxqmJVrGkQKNX3V6uh\nsUGRD0fUnGlW+nNpFW0p0mBnsI6kj6jq1ipJ8qRmaWJpj39HYvGYHnv1McVKYmqsbczuiwMAPuG9\nD0Au6Y+5CGfWAAAAQL8zxujeuXM1UDYKAwCAgcsYo7lz72XdA/QQ30APoDOhUEjxeEyRiNPm+kjE\nUTweUygUymqNlz0BAAD0ReZkRuHdYRVvKFZ4d1iZk5le1Uidf+N1KBRSvCquyKBIm+sjgyKKV8V7\ntaYxxmju3V3/m5FXNdl+PFtrgthTZzVenXnG7dlp3PyOcIYaAAMR730A8C42a/iEAxYBAMBAkkwm\ntaKO01oCAAD7JZNJ1dWtYN0D9FAiMV+JxAI1N+crnd4gyVE6vUFHj4aUSCxQIjHf7xYB+CyTaVE4\n3Kji4gkKhxuVybT4WuP1fQEAAPRUdFRUNRNqdPD5g6qZUKPoqPZnDXFTI0n1q+vVMr5F9avrO7zd\n7aYPNzrbGNIfNdl+PFtrgthTXzYYeVXzXl39jrjd+AEANuG9DwDexWYNn3DAIrxSU1PjdwuA58g1\nbDWQs72uvl4PtbRoXX3HQ37kroGcawAAOlJfv04tLQ+pvn6d360MCKxF7ME30L+LXMNWfc12NFqi\nmhpHBw9uVE2No2i0xNcar+8LuYn3bACAn5oeb1K8Kq6CggLFq+JqerypRzU9+cZrt5s+3OhuY4iX\nNdl+vKzXXMDr2JcNRl6enaar35H+OEON7Vhnw0YDLde89w0MAy3XQG/l+93AQPXeAxYrGhv9bgc5\n7NixY363AHiOXMNWAy3by6urtaqhQRdGIhrU3KwHJN20ebMmlpbqlSNHNKOqSouWLvW7TfTRQMs1\nACBYjDGaN2+xHn54mRzH6XNdb1VXL1dDwypFIhequXmQpAe0efNNKi2dqCNHXlFV1QwtXbrI88cF\naxEbnf4G+iFDmnT48NQB+Q305Bq26mu2m5rqWv9/PB5TPB7ztcbr+0Ju4j0bAJDLEgsTGjp0qJY9\nukzpyrSkU994rU1SYlFCVbGq1tr3bvKIV8UVr4r36LGq769WQ2ODIh+OqDnTLH1O2rzl1MaQI+kj\nqrr11GN5UbM0sTSrj+drTeG7G2x4Hd/tWzq1eeKuK+5S7F9jWrlmpbb+amu7XHpVI7n7HTm98WPI\ny0N0eMThPp2hxnass2GjgZpr3vvsNlBzDfSUY4zxu4escBwnKimZTCYVjfZ+h39fvP+AxfXptG4q\nKtJbgwdzwCIAALDOiRMntKahQU8uW6an0unW6ycXFenGxYs1vaqKb0sAEDipVEoVFRWSVGGM4VSI\nsEYQ5iL9YceOHRo7dpy2bfvF6d/dPtX11okTJ9TQsEbLlj2pdPqp1uuLiiZr8eIbVVU1nXUPfOFm\no1I2a9zUTZ16h66+OqpY7ItauXKttm5NtTnQGQAAAP2L2QhsZOtcxCvlV5Vr/7X7W/9c9kyZ9j23\nz9PHOHHihBpWNrTZGCJJRZuKtHj24taNIV7UhEKhrD6erTW5+joGeQY3deZUXX3F1YpNf3fjR0dn\nxAEAm/DeByDX9MdchM0aWcQBiwAAYCCqLC/Xpv3vDvkry8q0aZ+3Q34A8AoHJMBWQZiL9IdY7B49\n9tgIxWK/U2PjA32u66vy8krt37+p9c9lZZXat29TFz8B9C83G5WyWdOTOgAAAPiD2QhsZOtcxCsj\nPzlSfxz2x9ZvvB72x2E6sO1AvzyWm40hXtVk+/FsrQliT9nYYAQAAICBqT/mInle3AncCYVCisXj\nciKRNtc7kYhi8TgbNQAAgJVaMhk1hsOaUFysxnBYLRlOawkAAHqvunq5CgvLNXLkJG3Zsl/SbG3e\nvE+lpRNVWFiu6urlParzUibTonC4UcXFExQONyqTafH8MYCeqK9fp5aWh1Rfvy4QNT2pAwAAAABk\nR3RUVDUTanTw+YOqmVCj6Kj+29CSOZlReHdYxRuKFd4dVuZk+38z8qom249na00Qe3LbNwAAABAE\nbNbwAQcswkuvv/663y0AniPXsNVAzXZJNCqnpkYbDx6UU1OjEr61yioDNdcAAP8kEvOVSCxQc3O+\n0ukNkhyl0xt09GhIicQCJRLze1TnpWi0RDU1jg4e3KiaGkfRaInnj4G2WIu052ajUjZr3PaEd5Fr\n2Ipsd80Yo7lz75Uxxu9W0APkGgBgg6bHmxSviqugoEDxqriaHm/qt8dyszHEq5psP54fNds2beN1\n7OcNRvAH62zYiFzDRuQacMcZKEPfIJ3W8o6pUxW9+mp9MRbT2pUrldq6VXVN/fdhF3arrKzUpk2b\n/G4D8BS5hq3INmxErmGb/jilJRAEQZqLeKW8vFL797/736Cyskrt29f+v0lu65CbWIu0d+LECTU0\nrNGyZU8qnX6q9fqioslavPhGVVVNl6Ss1YRCIVc9cdbhd5Fr2Ipsd23Hjh0aO3actm37xenPJMgB\n5Bo2YjYCGwVtLmKM0bwF8/Twgw/LcRy/20EOYy0CW5Ft2Ihcw0bkGjbqj7kIZ9bwQV1Tk2LxU99K\nEIvHO92oYYzRvXPn8i1K6NKSJUv8bgHwHLmGrcg2bESuAQD9pbtvl85kWhQON6q4eILC4UZlMi19\nqkNuYi3SXigUUjweUyTS9oCfSMRRPB5TKBTKao3bnvAucg1bke2u1devU0vLQ6qvX+d3K+gBcg0A\n6I1kMqm6R+qUSnV+3I8xRnPv5ngRdI21CGxFtmEjcg0bkWvAHTZrBFgymdSKuq4/oANB+OYPwGvk\nGrYi27ARuQYA9JdkMqm6uhWdzkWi0RLV1Dg6eHCjamocRaMlfarLpu42omS7Jog9ua1Zs+aHgekn\n24/nxYambNb0pG6gY40NW5Ht9qqrl6uwsFwjR07Sli37Jc3W5s37VFo6UYWF5aquXu53i+gGuQYA\n9Eb96nq1jG9R/er6TmvcbOgAWIvAVmQbNiLXXWOjam4i14A7bNboB16dEWNdfb0eamnRuvrOP6AD\nAAAAAADYqLtvl25qqlM8HlNBQYHi8Ziamur6VJdN3W1EyXZNEHvKxZqg9eRmo1I2a3pSBwADRSIx\nX4nEAjU35yud3iDJUTq9QUePhpRILFAiMd/vFgEAgEeq769W4fmFGnnFSG3ZtkWqkDa/sFmlY0pV\neH6hqu+vblPvZkMHAACALdioCsBmbNboB305I8by6mqVFxZq0siR2r9li2ZL2rd5syaWlqq8sFDL\nq6u7vQ8AAAAAAIBcFPRvl3Z7doXudLcRJds1QewpF2uC1pObjUrZrOlJHQAMFKFQSPF4TJGI0+b6\nSMRRPB5TKBTyqTMAAOC1xMKEEosSam5pVvr6tORI6evTOnryqBKLEkosTPR4QwcAAIAt2KgKwGZs\n1ugHfTkjxvxEQgsSCeU3N2tDOi1H0oZ0WqGjR7UgkdD8RML7hpHTVq5c6XcLgOfINWxFtrvm5uxk\nQasBuQYAeMvPb5d2sxHD7dkVOuJmI0o2a4LYU+9q8ngdA7ShCd5gjQ1bke3OZTItCocbVVw8QeFw\nozKZlg7rvNo4Cu+QawCAW6FQSPGquCKDIm2ujwyKKF4VVygUcrWhA3gv1iKwFdmGjch1ez3dqGqM\n0dy7OYYjSMg14JIxZkBcJEUlmWQyafrDNxIJUzZ0qJlYWmpuLCoyGcncWFRkPjtihCkbOtR8I5Ho\n0f1NLiszRmq9TC4r65e+kftuv/12v1sAPEeuYSuy3bXt27ebwfn5ZseOHTlTA3IN+ySTSSPJSIqa\nAHyW5cLFq0t/z0V6KpPJmDlzFplMJtPh7WVlk987FjFlZZP7vaft27eb/PzBXf63f9asBUb6jonF\n7unx/R8/ftzU1jaaoqLr2zy3oqLrTW1tozl+/HhWa4LYU+9qbud17KAn5DbW2LAV2e7clCm3m9ra\nRvPmm2+a2tpGM2VKx6+Vm/U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