LogisticRegression_baselinemodel_overall.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import sklearn\n",
    "from tqdm import tqdm\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_name={\n",
    "    \"y_train\":\"train_y\",\n",
    "    \"y_test\":\"test_y\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def open_pkl(file_name):\n",
    "    all_data=dict()\n",
    "    for each_key in file_name.keys():\n",
    "        name = file_name[each_key]\n",
    "        pickle_file = open('DataSet\\\\'+name+\".pkl\",mode=\"rb\")\n",
    "        data = pickle.load(pickle_file)\n",
    "        pickle_file.close()\n",
    "        all_data[each_key]=data\n",
    "    return all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = open_pkl(y_name)\n",
    "y_train = y_data[\"y_train\"]\n",
    "y_test = y_data[\"y_test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = \"DataSet\\\\train_df_tfidf.csv\"\n",
    "train_df = pd.read_csv(train_path,sep=\",\")\n",
    "\n",
    "test_path = \"DataSet\\\\test_df_tfidf.csv\"\n",
    "test_df = pd.read_csv(test_path,sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "         ur      name       how     know       you       not       hmm  \\\n",
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       "\n",
       "       are      what        do  ...  heheheheh  roadsid  chiranjeevi  \\\n",
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       "\n",
       "[5 rows x 8399 columns]"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    ur  name       how  know       you  not        hmm  are      what   do  \\\n",
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       "\n",
       "   ...  heheheheh  roadsid  chiranjeevi  pawankalyan  drone  salaman  \\\n",
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       "4  ...        0.0      0.0          0.0          0.0    0.0      0.0   \n",
       "\n",
       "   conphyooj  grasshopp  freeeeeeeeeeeeee  dilut  \n",
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       "\n",
       "[5 rows x 8399 columns]"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_np = train_df.iloc[:, :].values\n",
    "test_np = test_df.iloc[:,:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in case I have more versions of dataset for better model building\n",
    "X_train = dict()\n",
    "X_test = dict()\n",
    "\n",
    "X_train[\"train_baseline\"]=train_np\n",
    "X_test[\"test_baseline\"]=test_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "X_test[\"test_baseline\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.Logistic Regression Model\n",
    "## 2.1. build and fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "def lr_model(train_data,y_train):\n",
    "    classifiers = dict()\n",
    "    for key in tqdm(train_data.keys()):\n",
    "        classifier = LogisticRegression(random_state = 0)\n",
    "        classifier.fit(train_data[key],y_train)\n",
    "        classifiers[key]=classifier\n",
    "    \n",
    "    return classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                            | 0/1 [00:00<?, ?it/s]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: 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",
      "  n_iter_i = _check_optimize_result(\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:34<00:00, 34.41s/it]\n"
     ]
    }
   ],
   "source": [
    "classifiers = lr_model(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "def save_data(name,data):\n",
    "    pickle_file = open('DataSet\\\\'+name+\".pkl\",mode='wb')\n",
    "    pickle.dump(data,pickle_file)\n",
    "    pickle_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "save_data(\"LogisticRegression_baseline_overall\",classifiers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2. prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for getting prediction\n",
    "def get_prediction(classifiers, X_test):\n",
    "    y_pred_dict= dict()\n",
    "    list1 = list(classifiers.keys())\n",
    "    list2 = list(X_test.keys())\n",
    "    length = len(list1)\n",
    "    \n",
    "    for i in tqdm(range(length)):\n",
    "        y_pred_dict[list2[i]]=classifiers[list1[i]].predict(X_test[list2[i]])\n",
    "        \n",
    "    return y_pred_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  5.17it/s]\n"
     ]
    }
   ],
   "source": [
    "y_preds = get_prediction(classifiers,X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for getting the score of each prediction\n",
    "def get_scores(classifiers, X_test):\n",
    "    y_score_dict= dict()\n",
    "    list1 = list(classifiers.keys())\n",
    "    list2 = list(X_test.keys())\n",
    "    length = len(list1)\n",
    "    \n",
    "    for i in tqdm(range(length)):\n",
    "        y_score_dict[list2[i]]=classifiers[list1[i]].decision_function(X_test[list2[i]])\n",
    "        \n",
    "    return y_score_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  5.42it/s]\n"
     ]
    }
   ],
   "source": [
    "y_scores = get_scores(classifiers, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test_baseline': array([[  5.87041842,  -9.49915122,   3.05101782,   0.57771498],\n",
       "        [  4.30353789,  -2.35381597,   0.1899078 ,  -2.13962971],\n",
       "        [  1.02289807,  -2.6612374 ,  -1.77357499,   3.41191432],\n",
       "        ...,\n",
       "        [  5.7374296 , -11.82440348,   4.93081824,   1.15615563],\n",
       "        [115.35314217, -47.86533564, -38.41235697, -29.07544955],\n",
       "        [  2.59962972,  -3.17352458,  -1.13751327,   1.71140812]])}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for saving the predictions and scores\n",
    "def save_prediction(name,data):\n",
    "    pickle_file = open('Prediction\\\\'+name+\".pkl\",mode='wb')\n",
    "    pickle.dump(data,pickle_file)\n",
    "    pickle_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_prediction(\"baseline_overall_logisticregression_preds\",y_preds)\n",
    "save_prediction(\"baseline_overall_logisticregression_scores\",y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3. Evaluation: confusion matrix, accuracy, precision, recall, F1 measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "# print evaluation result\n",
    "def get_evaluation(y_test,y_pred):\n",
    "    ev = dict()\n",
    "    ev[\"confusion_matrix\"]=confusion_matrix(y_test,y_pred)\n",
    "    ev[\"accuracy\"] = accuracy_score(y_test,y_pred)\n",
    "    ev[\"precision_macro\"]=precision_score(y_test,y_pred,average=\"macro\")\n",
    "    ev[\"precision_micro\"]=precision_score(y_test,y_pred,average=\"micro\")\n",
    "    ev[\"precision_weighted\"]=precision_score(y_test,y_pred,average=\"weighted\")\n",
    "    ev[\"recall_macro\"]=recall_score(y_test,y_pred,average=\"macro\")\n",
    "    ev[\"recall_micro\"]=recall_score(y_test,y_pred,average=\"micro\")\n",
    "    ev[\"recall_weighted\"]=recall_score(y_test,y_pred,average=\"weighted\")\n",
    "    ev[\"F1_score_macro\"]=f1_score(y_test,y_pred,average=\"macro\")\n",
    "    ev[\"F1_score_micro\"]=f1_score(y_test,y_pred,average=\"micro\")\n",
    "    ev[\"F1_score_weighted\"]=f1_score(y_test,y_pred,average=\"weighted\")\n",
    "    \n",
    "    for key in ev.keys():\n",
    "        if key !=\"confusion_matrix\":\n",
    "            print(\"{a} is: {b}\".format(a=key, b=ev[key]))\n",
    "        else:\n",
    "            print(ev[key])\n",
    "    \n",
    "    return ev\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\"macro\": \"Calculate metrics for each label, and find their unweighted mean. \",\n",
    "#\"micro\": \"Calculate metrics globally by counting the total true positives, false negatives and false positives.\"\n",
    "#\"weighted\":\"Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). \"\n",
    "\n",
    "def get_evaluation_all(y_test, y_pred_dict):\n",
    "    for key in tqdm(y_pred_dict.keys()):\n",
    "        print(\"evaluation on data {0}\".format(key))\n",
    "        get_evaluation(y_test, y_pred_dict[key])\n",
    "        print(\"***************************************\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 22.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluation on data test_baseline\n",
      "[[3593  424  338  322]\n",
      " [ 123  118   15   28]\n",
      " [  91   10  131   18]\n",
      " [  72   24   18  184]]\n",
      "accuracy is: 0.7308041386821564\n",
      "precision_macro is: 0.4313550692178437\n",
      "precision_micro is: 0.7308041386821564\n",
      "precision_weighted is: 0.8268135994013133\n",
      "recall_macro is: 0.5812925296085736\n",
      "recall_micro is: 0.7308041386821564\n",
      "recall_weighted is: 0.7308041386821564\n",
      "F1_score_macro is: 0.4739106210784344\n",
      "F1_score_micro is: 0.7308041386821565\n",
      "F1_score_weighted is: 0.7664120382719579\n",
      "***************************************\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "get_evaluation_all(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4. Evaluation: ROC curve, AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in order to plot ROC curve and calculate AUC, I need to turn y label into 1 hot representation.\n",
    "# transfer y label from {class:[0,1,2,3]} into {class: [1,0,0,0], [0,1,0,0],[0,0,1,0],[0,0,0,1]}\n",
    "import tensorflow as tf\n",
    "y_test1hot = tf.keras.utils.to_categorical(y_test,num_classes=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 1.]\n",
      "[1. 0. 0. 0.]\n",
      "[0. 1. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "emotion = {\"others\":0,\"happy\":1,\"sad\":2,\"angry\":3}\n",
    "print(y_test1hot[13])#3 - 4th\n",
    "print(y_test1hot[0])#0 - 1st\n",
    "print(y_test1hot[6])#1 - 2nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve,auc\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_roc_auc(y_test1hot, y_score):\n",
    "    fpr = dict()\n",
    "    tpr = dict()\n",
    "    roc_auc = dict()\n",
    "    n_class = 4\n",
    "    \n",
    "    # compute roc curve and auc based on each label\n",
    "    for i in range(n_class):\n",
    "        fpr[i],tpr[i],_ = roc_curve(y_test1hot[:,i],y_score[:,i])\n",
    "        roc_auc[i] = auc(fpr[i],tpr[i])\n",
    "    \n",
    "    # compute roc curve and auc based on average type = \"micro\"\n",
    "    fpr[\"micro\"],tpr[\"micro\"],_=roc_curve(y_test1hot.ravel(),y_score.ravel())\n",
    "    roc_auc[\"micro\"]=auc(fpr[\"micro\"],tpr[\"micro\"])\n",
    "    \n",
    "    # compute roc curve and auc based on average type = \"macro\"\n",
    "    # First aggregate all false positive rates\n",
    "    all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_class)]))\n",
    "    # Then interpolate all ROC curves at this points\n",
    "    mean_tpr = np.zeros_like(all_fpr)\n",
    "    for i in range(n_class):\n",
    "        mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])\n",
    "        \n",
    "    # Finally average it and compute AUC\n",
    "    mean_tpr = mean_tpr/n_class\n",
    "    fpr[\"macro\"] = all_fpr\n",
    "    tpr[\"macro\"] = mean_tpr\n",
    "    roc_auc[\"macro\"] = auc(fpr[\"macro\"],tpr[\"macro\"])\n",
    "    \n",
    "    return fpr, tpr, roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_roc_auc_all(y_test1hot, y_scores):\n",
    "    fprs=dict()\n",
    "    tprs=dict()\n",
    "    roc_aucs=dict()\n",
    "    for key in tqdm(y_scores.keys()):\n",
    "        fprs[key],tprs[key],roc_aucs[key]=compute_roc_auc(y_test1hot,y_scores[key])\n",
    "    \n",
    "    return fprs,tprs,roc_aucs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc(fpr, tpr, roc_auc, dataname,modelname,choose = \"all\"):\n",
    "    n_class=4\n",
    "    \n",
    "    # make variable choose as command for choosing which curve to show\n",
    "    # below it's the explaination of choose\n",
    "    ex_choose = {\n",
    "        \"all\": \"show all curves\",\n",
    "        \"macro\": \"only show macro curve\",\n",
    "        \"micro\": \"only show micro curve\",\n",
    "        \"label\": \"only show curves of labels\"\n",
    "        }\n",
    "    \n",
    "    colors = [\"y\", \"g\",\"cornflowerblue\",\"r\"]\n",
    "    label_name = [\"others\", \"happy\",\"sad\",\"angry\"]\n",
    "    plt.figure()\n",
    "    \n",
    "    if (choose !=\"macro\") and (choose !=\"label\"):\n",
    "        # draw the line of micro-average ROC curve\n",
    "        plt.plot(fpr[\"micro\"],tpr[\"micro\"],\n",
    "                 label=\"micro-average ROC (AUC: {0:0.2f})\".format(roc_auc[\"micro\"]),\n",
    "                 color = 'deeppink',linestyle=':',lw=4)\n",
    "    \n",
    "    if (choose !=\"micro\") and (choose !=\"label\"):\n",
    "        # draw the line of macro-average ROC curve\n",
    "        plt.plot(fpr[\"macro\"],tpr[\"macro\"],\n",
    "                 label=\"macro-average ROC (AUC: {0:0.2f})\".format(roc_auc[\"macro\"]),\n",
    "                 color = 'navy',linestyle=':',lw=4)\n",
    "        \n",
    "    if (choose !=\"macro\") and (choose !=\"micro\"):    \n",
    "        # draw line of each label\n",
    "        for i in range(n_class):\n",
    "            plt.plot(fpr[i],tpr[i],color=colors[i],lw=2,\n",
    "                     label=\"ROC of class {0} {1} (AUC: {2:0.2f})\"\n",
    "                     \"\".format(i,label_name[i],roc_auc[i]))\n",
    "    \n",
    "    plt.plot([0,1],[0,1],linestyle=\"--\",color='k',lw=2)\n",
    "    plt.xlim([0.0,1.0])\n",
    "    plt.ylim([0.0,1.1])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC Curve on data {0} by model {1}'.format(dataname,modelname))\n",
    "    \n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.savefig(\"Pictures\\\\{a}\\\\SingleData\\\\ROC Curve by model {b}_{c} on data {d}_overall.png\".format(a=modelname,d=dataname,b=modelname,c=choose))\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 45.57it/s]\n"
     ]
    }
   ],
   "source": [
    "fprs,tprs,roc_aucs=compute_roc_auc_all(y_test1hot, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelname=\"LogisticRegression\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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"
    },
    {
     "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": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "key_name = \"test_baseline\"\n",
    "fpr = fprs[key_name]\n",
    "tpr = tprs[key_name]\n",
    "roc_auc = roc_aucs[key_name]\n",
    "dataname=key_name\n",
    "plot_roc(fpr, tpr, roc_auc, dataname,modelname,choose = \"all\")\n",
    "plot_roc(fpr, tpr, roc_auc, dataname,modelname,choose = \"macro\")\n",
    "plot_roc(fpr, tpr, roc_auc, dataname,modelname,choose = \"micro\")\n",
    "plot_roc(fpr, tpr, roc_auc, dataname,modelname,choose = \"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5. Evaluation: P-R curve, average precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_p_r_curve(y_test1hot, y_score):\n",
    "    n_class = 4\n",
    "    precision = dict()\n",
    "    recall = dict()\n",
    "    average_precision =dict()\n",
    "    \n",
    "    # compute P-R curve on each label\n",
    "    for i in range(n_class):\n",
    "        precision[i],recall[i], _ = precision_recall_curve(y_test1hot[:,i],y_score[:,i])\n",
    "        average_precision[i] = average_precision_score(y_test1hot[:,i],y_score[:,i])\n",
    "        \n",
    "    # compute P-R curve with average = \"micro\"\n",
    "    precision[\"micro\"],recall[\"micro\"],_=precision_recall_curve(y_test1hot.ravel(),y_score.ravel())\n",
    "    average_precision[\"micro\"]=average_precision_score(y_test1hot,y_score,average=\"micro\")\n",
    "    \n",
    "    return precision,recall,average_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_p_r_curve_all(y_test1hot, y_scores):\n",
    "    recalls=dict()\n",
    "    precisions=dict()\n",
    "    average_precisions=dict()\n",
    "    for key in y_scores.keys():\n",
    "        recalls[key],precisions[key],average_precisions[key]=compute_p_r_curve(y_test1hot,y_scores[key])\n",
    "    \n",
    "    return recalls,precisions,average_precisions\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pr(recall, precision, average_precision, dataname,modelname,choose = \"all\"):\n",
    "    n_class=4\n",
    "    \n",
    "    # make variable choose as command for choosing which curve to show\n",
    "    # below it's the explaination of choose\n",
    "    ex_choose = {\n",
    "        \"all\": \"show all curves\",\n",
    "        \"micro\": \"only show micro curve\",\n",
    "        \"label\": \"only show curves of labels\"\n",
    "        }\n",
    "    \n",
    "    colors = [\"y\", \"g\",\"cornflowerblue\",\"r\"]\n",
    "    label_name = [\"others\", \"happy\",\"sad\",\"angry\"]\n",
    "    plt.figure()\n",
    "    \n",
    "    if choose !=\"label\":\n",
    "        # draw the line of micro-average ROC curve\n",
    "        plt.plot(recall[\"micro\"],precision[\"micro\"],\n",
    "                 label=\"micro-average P-R curve (average precision: {0:0.2f})\".format(average_precision[\"micro\"]),\n",
    "                 color = 'deeppink',linestyle=':',lw=4)\n",
    "    \n",
    "        \n",
    "    if choose !=\"micro\":    \n",
    "        # draw line of each label\n",
    "        for i in range(n_class):\n",
    "            plt.plot(recall[i],precision[i],color=colors[i],lw=2,\n",
    "                     label=\"P-R curve of class {0} {1} (average precision: {2:0.2f})\"\n",
    "                     \"\".format(i,label_name[i],average_precision[i]))\n",
    "    \n",
    "    plt.plot([0,1],[1,0],linestyle=\"--\",color='k',lw=2)\n",
    "    plt.xlim([0.0,1.0])\n",
    "    plt.ylim([0.0,1.1])\n",
    "    plt.xlabel('Recall')\n",
    "    plt.ylabel('Precision')\n",
    "    plt.title('P-R Curve on data {0} by model {1}'.format(dataname,modelname))\n",
    "    \n",
    "    plt.legend(loc=\"lower left\")\n",
    "    plt.savefig(\"Pictures\\\\{a}\\\\SingleData\\\\P-R Curve by model {b}_{c} on data {d}_overall.png\".format(a=modelname,d=dataname,b=modelname,c=choose))\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "recalls,precisions,average_precisions = compute_p_r_curve_all(y_test1hot, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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"
    },
    {
     "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": {
      "image/png": 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wiOwUkZ2nTp3i5ptvZsSIEZw6dapIBZ6PTicuwR1i4qkZmQA7dlyz8BqNpnwTF/cvqakXOH36VWeLUupxpiKRXPap3BIqpRYopToqpTrWq1cPHx8fVqxYQfPmzXnllVdITEwsVIHJqcZ/j+RhRqBSpWuRW6PRVABSUy8B4O3dwsmSlH6cqUjCgAY22/7AuYIOuu666wgNDeWee+4hKSmJGTNm0Lx5c3744QeUylUPWWhQ0xUAT4/Gxo6tW69RdI1GU96pXv0WAKKj/3KyJKUfZyqSlcD95uytLkCMUiqiMAfWr1+fr7/+mvXr1xMcHMzp06d54403ClQkD9/sC0BMlTrFlV2j0ZRzMlskmoJxc1TGIvIN0BuoKSJhwHTAHUApNQ9YBdwCHAMSgHFFLaNnz56EhIQwf/58OnbsiIuLoRcvXryIp6cnlStXzpLe19PoTTvRqOM1npVGo6koVKvWn/DwD3Fx8Xa2KKUehykSpdQ9BcQr4NHiluPm5sajj2bNZvLkyfzzzz/Mnj2b0aNHI2IoEJ9Kxn+Gq81pJyaCl1dxxdBoNOUML68mAGRkJDhZktJPuVvZnpCQwNmzZzl//jz3338/3bt3599//wWwKJRY35rWA376yRliajSaUo6n5w2WcGpqfisZNOVOkXh7e7Np0yYWLVpEnTp12Lx5Mx07dmTSpElERUUZicRmwtiVK84RVKPRlGpcXDwtYaUynChJ6afcKRIAFxcXxo4dS2hoKE8++SSurq7Mnz+f5s2bk5IUC0B6pi7JNo6i0Wg0BtbJO+npcU6Uo/RTLhVJJlWqVOGdd95hz5499OvXj1GjRlHvOg8AdjcJNBJdvOhECTUaTWlFxPp63LatMZcv/+FEaUo35VqRZNKiRQvWrFnD22+/TR0/HwDOHj/KfcC548edK5xGoym11Kv3iCW8d+9ALl363onSlF4qhCIBY6Ddw8ODtIwMlFI84+bG10DQvHnMnj2blJQUZ4uo0WhKGYGBH9O1a7hl+8CBOzl0aGyBa9YqGhVGkWRSt5o7IsILne9jOBCXns60adNo3bo1q1evdrZ4Go2mlFGpUj06dtxn2b5w4UvWr3chPPwTrVBMKpwiqVPNMLgVftP9rABWA0FBQRw5coRBgwYxfPhwLly44FQZNRpN6cLXtxU9eybj49PKsu/o0f+wfr0LBw+OdqJkpYMKp0ia1I8BIKx+SwBuBvZu2MCcOXPw9fUlJCQEX19fJ0qo0WhKIy4uHtx44z66dDmVZf/Fi19x4sSLFbp1UuEUia97NSDr6nYPpXjmmWc4cuQI3333HT4+xoD81atXWbFiRYW+QTQaTVY8PRvSu7eiV680y74zZ2axfr0LqanRTpTMeVQ4ReLplktr4/BhAOrWrUvXrl0tu2fOnMmIESMYMGAABw8eLCkRNRpNGUDElY4d92TZt2lTtQr54VnhFEmm4cYsRORudDgwMJDq1auzdu1agoODefLJJ4mJiXGwhBqNpqzg6xtM796KmjXvsOxbv96FjIxUJ0pV8lQ4RSIiJGRkG0z//PNc0z700EMcOXKESZMmkZGRwXvvvUdgYCALFy4kI0ObTNBoNAatWi2nVq07LdsbNnhUKGVS4RQJQIYyLvD+m243dvz5Z55pa9SowSeffEJISAjdunXj4sWLjB8/no0bN5aEqBqNpozQsuUyqlW72bK9YYOHE6UpWSqkIklWhvHGf6/vYd25d2++x7Rr146NGzeyePFiHnroIXr27GmJS0jQZqY1Gg20abMaT8/Glu0TJ553ojQlR4VUJCeSVgKwuYuNy5S4go2yiQijR49mwYIFln3//vsv119/PXPnziUtLS2fozUaTUWgc2er2aUzZ94kLi7/j9TyQIVUJIeTvgQg3c2DC7VMnwPVq19TXt9++y1RUVE89thjdOjQgQ0bNthLTI1GUwYRETp3PmHZ3rmzjROlKRkqnCLJUBkkq8scSfoWgDqXzAuelHRN+b311lusWLGCRo0asXfvXnr16sW9995LeHh4wQdrNJpyiZeXtXvLz6+TEyUpGSqcIklJN4wz/hP7RJb9CVt2XlN+IsLw4cM5ePAgM2bMwNPTk2+++YagoCB++eWXYsur0WjKJkFBnwEQG7vdyZI4ngqnSDzdDK9nijRaN7lq2b/25yMkJF/7lF4vLy9efvllDh06xIgRIxAR2rdvX2x5NRpN2cTFxccSLu9TgSucIgGo6lkVgDaBZy376p87yJFzxR8sb9SoEd9//z2HDh2iXr16AKSlpfHEE09w7NixYuev0WjKBrVr320JX7mS9xKD8kCFVCTRSYY9nOreVVF9+gDgG3eZbzfabxqvv7+/Jfzpp5/ywQcf0LJlS/773/8SHx9vt3I0Gk3pRMRqRSMiYkE+Kcs+FVKRVPcyZmi5u7ojly4B4JV0lajYDP49YX8HV7fffjtjx44lJSWF119/nWbNmrF06dIKaZNHo6lIeHo2ASAy8kcnS+JYKqQiuZx4GTBbJubCwgbhB6hz4Rif/B5HfJJ9zZ9cd911LFq0iM2bN9OhQwfCwsIYNWoUffv2Zd++fQVnoNFoyiQBAXMt4fT08rtwuUIqkkyuJF6BFi0s26+92pmb13zIr5uuGNOB7WxPq2vXrmzbto0FCxZQo0YN1q1bx6ZNm+xahkajKT1Urz7QEv7nH598UpZtKqQi6eLfBTDWlDBpEjxvNWMw8sdXuKt/TfDyAnd3uysTV1dXHnroIY4ePcrrr7/OQw89ZInbt2+fNgap0ZQjRCSL/a2UlEtOlMZxVEhFsjVsKwBHLx8FV1d4/XU4YSxMTHN1J9WtkpEwIwOaNoUhQ2DKFEi13xS+atWq8fzzz+Pq6gpAeHg4Xbt2pUuXLmzfXv7nnWs0FYU2bVZbwqdPz3SiJI6jQioSdxd3AFzF1bqzcWNQikc+OM9/3j9n3X/yJKxaBe+/Dx4e8PjjsHAhfPUVxMbaTaaTJ09SpUoVduzYQefOnXnwwQe5ePGi3fLXaDTOJzz8Q2eL4BAqpCJxdTEUyOO/P05yWnKWuKdu8zPi5pzg0LwV8MsvcN111gQffgjjx8Po0TBokN1k6t69O6GhoUybNg13d3e++OILAgMDef/990m1Y0tIo9GUPP7+T1nCGRn2nxnqbCqkIvlkyCeW8OHIw1nimvu7U6uyC4neVdjcpL/RrRURAbt2wUMPwQMPWBNv3lwoq8GFxdfXlzfffJP9+/czePBgYmJimDJlChMnTrRbGRqNpuRp0uRtSzgmpvz5MqqQiuSBtg9YwlvCtuSIj0kwBry3HkkhOt4c/G7bFhYsMLq1bMzI4+dnDNjb0SdJYGAgv/76KytXriQgIIDHH3/cEqfXnmg0ZQ/bxYmnTs1woiSOwaGKREQGiUioiBwTkedyia8iIj+LyB4ROSAi4xwpT2488usjxKdkXWk+7fbKlvCMpTFcuprO5dh060t87FhjTCWT+fPBxwc+/dRucokIQ4cO5dChQ7Rt29ayf9SoUcyaNYuka7RWrNFonEulSg2cLYLdcZgiERFX4CNgMNACuEdEWmRL9ihwUCnVBugN/E9ESsQ/5YJbra2KNSfWZIm7vpYbdasZVROXpHhhSQzTFsfw47ZEI4GHBxw/Dlu2ZB0/efRRu8uZOasLICQkhO+++44XX3yRli1b8vPPP+sWikZTZjBaJSkp5wpIV/ZwZIukE3BMKXVCKZUCfAsMy5ZGAX5itPt8gctAibgZfLD9g5bwpjM5FwU+M6wy/jVcqeHngncl4waIjLVZ4yECXboY4yfPmY2t1NQCXfYWhw4dOrB27VpatmzJiRMnuO222xgyZAhHjx51WJkajcY+ZC5OzMhIdLIk9seRiqQ+cNZmO8zcZ8tcoDlwDtgHPKGUyrEiT0QeFpGdIrLz0iX7LOhxEeupv73l7Rzxlb1dmH53Fd4cU5U+rYx1JeFR6blnZutit00b6NMn62/YMDh/3i5y9+3bl127dvHuu+9SuXJlfvvtN1q2bMnLL79sl/w1Go1jSEo6BUB8/EHnCuIAHKlIJJd92fthBgK7gXpAW2CuiFTOcZBSC5RSHZVSHWvVqmU3ASd2KNxsqDBTgYRfTs+9K2naNHjySev2unVZfytXQt26sGhRMSU2cHd3Z8qUKRw5coRx48aRmppKcnJywQdqNBqn4e5eG4D09KsFpCx7OFKRhAG2o0r+GC0PW8YBPyiDY8BJoJkDZcrCmOAxhUp3WycvS/i9n3NZhFizJrzzDoSFwV9/Zf11725NN86+cwnq1KnDF198wdatW3nxxRct+9euXcuePXvsWpZGoykeHh51nC2Cw3CkItkBBIhIY3MAfRSwMluaM0A/ABGpAwQBJxwoUxaa12puCec3aH19TTfczDHvg2H5DOHUr5+zW+uffyAgwJrm//6vuGLnoHPnzvj5GQsp4+LiGDt2LO3bt2fy5MlcvnzZ7uVpNJqik+l6F+Dy5TX5pCx7OEyRKKXSgMnAauAQ8J1S6oCITBKRSWaymUA3EdkHrAWmKaUiHSVTdlLTrSvGY5Jj8k07694qlvCpi0WcD7DSRn8+8IAxjjJxIjhgxpVSijvuuAMR4aOPPiIwMJAFCxaQnp7H+I5GoykR3NysvfZnz/7PiZLYH4euI1FKrVJKBSqlmiilZpn75iml5pnhc0qpm5VSrZVSrZRSSxwpT3bq+FqbmrsiduWbtrqfdRrurOVXuRxbhBdzs2ZGyySTvXuNRY1vvQVXrhQ+n0Lg5+fH+++/z65du+jduzdRUVFMnDiRzp07s2VLzsWXGo2m5Mg0leLqWr5MylfIle25sfbk2gLTDLcZK5m2OIalG+PZcTSZtPRCtCy6d4fLl7MqlOefh+rVr0XcAmndujV//fUXS5cuxd/fn5CQEO6//37S0kpkdrVGo8mFzIH2yMgfnCyJfdGKxOSTnZ8UmGZIRy/6ta5k2f5zbzIL1sSz7WghjbBVq2YolBUrsu4/cqQoohYaEeGuu+7i8OHDvPDCC7z33nu4ubkBEB8fr41BajQlTEKCY551Z1PhFcnnt30OGO53T0WfKjD9qB4+PDPMj742CmXRX/FcTSiCQ6rhwyHGZkwmKKjwx14DPj4+zJo1iyFDhlj2Pffcc7Rp04Y///zToWVrNBorLVsut4TT0vIfly1LVHhFckfzOyzhod8MLdQxQfXduaeHj2WhIsCyzQlFM1dSuTLccIN1+99/C39sMUlKSmLNmjUcOnSIAQMGcMcdd3D69OkSK1+jqah4eFjXwcXHH3CiJPalwiuSKp5VqOFVA4D9F/fz1d6vCn3sPT28LeGtR1JYt7+IiwJDQ63hqKiiHVsMPD092bNnD6+//jre3t788MMPNGvWjFdffZXExPJnvkGjKY1kZJQfw6sVXpEAbJ2w1RIevWI0H24rnBczEeHxIb6W7a//KaIpeXO8AoCbb4YStJlVqVIlnn/+eUJDQxk1ahRJSUlMnz6dli1bEh0dXWJyaDQVl/Lz+i0/Z1IMmlZvyoYHNli2H//9cbaHF85veuuGHnQKsBos3n2yiN7PbMdHAgONRYxHjxo/O9nnyg9/f3+++eYb1q1bR+vWrbnxxhupWrWqw8vVaCo6bm5VCk5URtCKxKRHwx4cmWydUdH5s86FHvMY3cs6J/yj34roMXH3brj/fuv2unWGQgkMNFbK//xz0fK7Rnr16sW///7L/PnzLfs2b97M1KlTibWjb3qNRmMQGfmjs0WwG1qR2BBQI4CFwxZatmvNqcXvx34nJT3/VoaXh3DbjdY1JqHhRZhW6+lpmE05c8ZY8d6gATRtasRlZBgLF0to7Yebm5ulNaKU4oknnmDOnDkEBQWxZMkS7ftEo7Ej0dF/O1sEu6EVSTbubnk3tX0MK51RiVEM/mowlV6rVMBR0C/Ymubtn2L590QRu7gaNDBaJ2fOZB0r+eUXaNiwaHnZARHhk08+oXPnzkRERDBmzBh69OjBrl35WwDQaDT54+JiTNLJNCtfHtCKJBte7l6ceuIUb/R7I4vPkuik/AegvSu5cF9P6yyuT36PIyG5CGtLsrNqlTV87pzhxvfQoRJrnQB07NiRzZs3s3DhQmrXrs2mTZvo2BqQSkIAACAASURBVLEjjzzyCFfsbNpFo6ko1K//HwCSk8+Si/ulMomUte6Kjh07qp07d5ZIWWkZabjPdAegtk9twp8Kx83FLd9j/j2ewierjXGSwHpuPDs8h3uVwvPvv9ChQ879MTHGOpQSJCYmhldeeYUPP/wQPz8/jhw5gj19w2g0FYXU1Cts2mSYRurePbrEBt1FJEQp1dEReesWST64ubhR389w6ngx/iL/21ywxc72TawzuI6cK2broX17eO89qJPNj8FTTxUv32ugSpUqvPvuu+zZs4dFixZZlEhKSgrbtm0rcXk0mrKKq6ufJSxScLd5WUArkgI4+ph1vOK5tc8VasD59s7GwHvrhu7FF+CJJ4xpwFdtvKp9/jmEhxc/72ugZcuWDBs2zLL9wQcf0KVLF8aMGUNERIRTZNJoyhK2PttdXT2dKIn90IqkALzcvfjlnl8s200/bMqe8/l7HzwfbZiY33c6laQUO3Ud+vnBrFnW7fh4++RbTNLT06lUqRJLliwhMDCQOXPmkJJSxIkGGk2FIjcv5GUbrUgKwZBAq7HDE1dO0HZ+W15d/2qe6a+vaR1H2Xvaji/VTp2s4VJiuXfatGkcPHiQYcOGERcXx9SpUwkODmb16tXOFk2jKZWIWBVJenoRrWGUUrQiKSSXnr3Ew+0ftmxPXzed5LTcbWv1b2Ntrnp52PHrw3YsolUr2LEDSsFiwRtuuIEff/yR3377jcDAQEJDQxk0aJC2LKzR5IKtU6szZ950oiT2QyuSQlLTuybzh85nzyRrt1ZyesFGGs9E2tHF7aRJ1sWKYLRQSnj2Vn4MGjSIffv28dZbbzFgwAD69u1ricvIKB/THDUaexITs8nZItgFrUiKSGCNQEu4ypsFT9v7cZsdrenWqGGsJenZ03552hkPDw+mTp3K6tWrcXExbq+TJ0/SokULfvjhB706XqMBqlTpAYC7u2M8pJY0WpEUEU83TwY3HWzZDo0MzTVdw1pWH++FcsVbWNzcYP16GDfOui8kxH752wnbfuC5c+cSGhrKHXfcwc0338yhQ4ecKJlG43y8vZsBEBtbMmviHI1WJNfAj6Osxtbe2PhGrmnu7m5d5b6rqOZSCsMLL1jDixfbP387Mnv2bD766COqVavGn3/+SXBwME8//TRXbac0azQVCuPjMimpfDiU04rkGvBw9aBFrRYA/N+e/yMqIadTqqbXWWdunb5kx3ESSwE2YyUrV9o/fzvi6urKf/7zH44cOcLEiRNJT0/nnXfeITAwkDVr1jhbPI2mxBHJXGNWPrp6tSK5Rj4cbHV+FZWYU5HYdu3Ure6aI96unDwJIrB1a8FpnUjNmjWZN28eO3fupFu3bkRFReHv7+9ssTSaEsfXt52zRbArWpFcI+3rtreEr69yfY74lDSFm6k/gu2xwj03sg+6d+0KTz4Jf/zhmPLsRPv27dm4cSNbt26lefPmgGG2/p133iEyMtLJ0mk0jsfaIikfFEqRiMhNIrJGRI6IyAkROSkiJxwtXGkmJinGEt5ydkuO+GMRaaSlw/W1XPHzcpC+XrcOwsKyell87z0YOBDiiuhgq4QRETrYGKT8/vvvefrppwkICOCjjz4irQStHGs0JY2XVxNni2BXCvuG+xx4B+gO3Ah0NP8rLLatkGl/TssRf+CssfL8jCPGRzIRMbwo7tsHX3wBt99ujWvbttSYUSkMrVq1YsCAAURHRzN58mQ6dOjAP//842yxNBqH4OPTyhIuD1PiC6tIYpRSvymlLiqlojJ/DpWslCMiljUltuMhmVxNsC7Ac/iN4u5uTAdevty67/hxWLbMseXakWbNmrF69Wp++OEHGjZsyN69e+nZsyf33nsv4U4yUKnROApXV19LODX1khMlsQ+FVSR/i8gcEekqIu0zfw6VrAyQYTqluZKY08lTYD1rH2ixzckXFhcXw4dJJi1alEy5dkJEuP322zl06BDTp0/H09OTb775hs8//9zZomk0diU93dr17O5e04mS2If8vTRZ6Wz+2zpFUUDfXNJWGDzdDJtanf0754hreb1VkVRyL0Frn7YmUzp3Ntz22k4VLgN4eXnxyiuv8MADD/Dmm2/y7LPPWuLOnz/Pdddd50TpNJriEx9/wBIWKftzngp1BkqpPrn8KrQSAThw0bgZluxdkiOuirdVeRyLKMGB4+xOsL78suTKtjONGjVi3rx5eHkZ/l2io6MJDg7mtttu4/jx406WTqMpPiKF/ZYv3RR21lYVEXlHRHaav/+JSIGGpkRkkIiEisgxEXkujzS9RWS3iBwQkfVFPQFnMqrVKEt474W9WeJcXayK5OTFElQkvr6QbGNM8ty5kivbwezatYukpCR+/vlnWrZsyYsvvkh8GZpQoNFkkp5uWHVQqnzMTixsm+oLIBa4y/xdBRbmd4CIuAIfAYOBFsA9ItIiW5qqwMfAbUqplsDIIknvZGwXJX6x64sc8dfXNBaSbD+aQkJyCVq/9bC6+yWXiQBllT59+hAaGsqYMWNITk5m1qxZNG/enO+++65czHzRVBzc3Ws5WwS7UlhF0kQpNV0pdcL8zQBuKOCYTsAxM30K8C0wLFuae4EflFJnAJRSF4sivLOp4V3DEv7lyC854oebLncB/j3hJEdUrg5eVV/C1K1bly+//JJNmzbRrl07zp49y913382kSZOcLZpGU2gyFyR6eNRzsiT2obCKJFFEumduiMhNQEH20esDZ222w8x9tgQC1URknYiEiMj9uWUkIg9ndqtdulQ6p8odv3Kc9Iysa0ZaN7S2DNYfSCo5YWx9fwwdWnLlliDdunVjx44dzJs3jxo1ajByZJlqzGoqOMnJxqsxJaV8dD0XVpE8AnwkIqdE5DQwFyjoEzC3PpXs/Q9uQAdgCDAQeElEAnMcpNQCpVRHpVTHWrVKV5Ow/w39LWG3mW4sP7g8h0IBOHXRgQsTs3PhgjVcyg06FgdXV1cmTpzIqVOn6N/feh2mTZvGwoULtTMtTaklMbF8TRYp7Kyt3UqpNkAw0Fop1U4ptaeAw8KABjbb/kB29RsG/K6UildKRQIbgDaFE7108N7A97Jsj1w2krUn11q2B9i43U1KKaF+/GrVrOEKMHbg62td3LVv3z5mz57N+PHjLa0Wjaa0Ubly+TIMkq8iEZHR5v9TIvIUMAGYYLOdHzuAABFpLCIewCgg++fxT0APEXETEW+M9SplyutRy9otSX4xmXcHvmvZN3DJQEu4c4C1e8ujpOy02fpxf+yxEiq0dNCqVSuWLFlC3bp12bZtG507d2bChAlcvFimht805ZzMab/u7nUKSFk2KKhFkuml3i+PX54oY17bZGA1hnL4Til1QEQmicgkM80h4HdgL7Ad+Ewptf8az8VpeLh6MKXLFJpUy2mI7dwVa5fW+Ssl1NVi6z2xdeuSKbOUICLcd999hIaGMnXqVNzc3Pj8888JDAxk7ty5zhZPowEgPv4gAKmpFwpIWTbIdzWMUmq++T/jWjJXSq0CVmXbNy/b9hxgzrXkX9r48vYvuemLm7LsC6xnreJj51Op52jfJImJ8OuvRtjW9lYFw8/Pj7feeovx48czZcoUfv/9d0JKoUtiTcXE1dWn4ERliMIuSJwtIpVFxF1E1opIZGa3l8bKuVjrEFDIOeOlZWtCfkuoA1zuZue776zhYdlnW1c8goKCWLVqFT/99BNvvGF1i7xnzx7Onj2bz5EajeNISYlwtgh2pbCztm5WSl0FbsUYIA8Ens3/kIpHV/+ulrCXu7GGxMNNuK6qUc31Hd0aATh2zBouxzO2ioKIcNttt1lsdKWmpnLffffRrFkzZs2aRVJSCU7N1mjIaqgxI8NJa8zsSGEVSeYw8S3AN0qpyw6Sp0xT26e2JTx/53yLdeD4ZGPmVFC9ErCrc8st1vAdd8CKFY4vs4wRFxdH8+bNSUhI4MUXX6RVq1b88kvOBaUajaOoXfteS/jq1ZyO8coahVUkP4vIYQzrv2tFpBagP+Oy4e5qnZb1wfYPOH75OHFJGcQmGork+IUSsKvTtWvW7ddfd3yZZYxq1aqxbNky/vzzT1q0aMHx48cZOnQoQ4YM4ejRo84WT1MBsPVh5OFR14mS2IfCriN5DugKdFRKpQLx5DR3ogF2T9xtCR+9fBTvStYbZldJmUk5fdoaHj++ZMosg/Tr14/du3fz7rvvUrlyZVatWkXfvn1JTS37XQ2a0k1amtUfiZdXQdamSj/59rWISF+l1F8iMsJmn22SHxwlWFmlzXXW9ZQZKgMXm/q6HJdBRobCxcXBhhQ3bzb+r79eK5ICcHd3Z8qUKdxzzz08//zz3HTTTbi7Gy3LtLQ0XF1dc/WAqdEUB+N73MCwb1u2KahF0sv8H5rL71YHylVmiU+xmjXv1qAbAC38rfp635lUx1uqvece4//WW6FSJceWVU6oU6cOX3zxBQ8++KBl3xtvvEGfPn3Yu3dvPkdqNEXH1dW6DC8pKcyJktiHfBWJUmq6+T8ul5/+1M0F2ynAbi6GAhnS0WoFeO6qOHafdGDXyfnzjsu7ApGSksJnn33G+vXradeuHY899hhXruR0qazRXAsuLtaPy5iYDU6UxD4Udh3J66bvkMztaiLymuPEKrvEpVj7PreFbQPghjpuDGxrtbm187gD15O429hh+fhjx5VTzvHw8GD37t08ZpqYmTt3LoGBgXz66aekp5egAU5NuSc5uZy3SGwYrJSKztxQSl3BmAqsyUbT6lb/6JnjJW6uwp3dvC37a1dxoI/mGjWybq9cCSklsBCyHFKtWjU++OADdu3aRc+ePYmMjOThhx+mc+fOREVFOVs8TTkhI6Mgjxyln8K+0VxFxNLZLiJegO58z4VFuxdZwrbrSmwt/1bxdqAiAQgKsoaHDYMZ12ThRmMSHBzMunXr+Oabb6hfvz5+fn5Ur17d2WJpygmRkT85W4RiU9g32hKM9SMPish4YA3wf44Tq+yy6eymXPe728yPy1yg6DD++AMmTLBul1JnYGUJEWHUqFEcPnyYxYsXW2ZyhYaG8u677+opw5oKTWHXkcwGXgOaAy2BmeY+TTZiU6wm3G0H3m1bJN2CHNyYu/56mD7duv38844trwLh6+uLv78/AEopnnjiCZ566inatm3LX3/95WTpNGURb++gghOVcorSx3IIwwnV08A/IpKvGfmKyvRe1hd4/XfqE5kQCcDe09YvVodP/wV46y1ruHFjx5dXARERJk+eTJMmTTh48CD9+vVj5MiRnDlzxtmiacoQJfI+cDCFnbX1ELAcmG/uqg/86CihyjKd6neiU/1Olu1L8Ua3Ug0/a1X/sacErMt8843jy9Bw6623sn//fmbNmoW3tzfLly+nWbNmvPbaa9oYpKZQpKfHOFuEYlPYFsmjwE3AVQCl1FGgdr5HVGD+ut/axXEk6ggAAXWtgyQNapaA8cYuXRxfhgYAT09PXnjhBQ4fPsxdd91FYmIiM2fOJDw83NmiacoAly//7mwRik1h32jJSqmUzAFGMfxElv32mIPwdrdO9b3O1zBdHn7ZuvagQxOPHMfYncz+et2tVWI0aNCApUuXMmnSJEJDQ2nSxPCYqZTi1KlTNNbXQlNOKWyLZL2IvAB4icgAYBnws+PEKtucjrEaTQyqaQyk/bTdOlfc093BtpvOnzc8JQJ8/rljy9LkoE+fPkyaNMmyvWTJEoKCgpg2bRqxsbH5HKmpSPj6tne2CHajsIpkGnAJ2AdMxHCf+6KjhCrrbDhtNXlQ7a1q7IrYRXyS0YCzHStxGP9nMzO7Rw/Hl6fJl0OHDpGWlsbs2bMJCgriq6++KhcDrJri4eZWxdki2I0C32oi4gLsU0p9qpQaqZS60wzrJyEP+jTqQ+VKlS3b7Re052iE4YvE4bWWlpZ1xpZ22OR0Xn/9dbZt20anTp2IiIhg9OjR9OzZk927dxd8sKbc4uVlWMGoXfs+J0tSfApUJEqpDGCPiFxfAvKUCxpUaUD0tGiqelrMk+HhbmiQ+KQMxwvQtq01fPvtsCn3RZKakuPGG29ky5YtfPHFF9SuXZuNGzfSoUMH/vjjD2eLpnESSUnGNHFXV68CUpZ+CtvPUhc4ICJrRWRl5s+RgpV1RIRf7rG2BlJSjXGR5DQ4cs6Bq6Dd3KwD7Zl07w4bNzquTE2hcHFxYdy4cYSGhjJlyhSaN29O7969nS2WxklcubIaqEDrSIAZGP5HXgX+Z/PT5MOPh61Lbe7ocdUSnvNjrONvntjYrJaAvb3zTqspUapWrcq7775LSEgIHh7GDL7IyEj69+/P5kynZJoKQ926EwpOVMrJV5GIiKeITAFGAs2ATUqp9Zm/EpGwDHNroNX31+eHnuX6mlZPaLbTgR2Cry/UqWPdbl9+ZoiUFyrZOB2bM2cOa9eu5aabbuL+++8nIiLCiZJpHE1GRpol7OXVxImS2IeCWiT/B3TEmK01GN0KKRK9GvWyhKMSo3jiVqtVmRlLr/LQx5d5YUk0sYkOGDdZswbCTD8HISH2z19jV15++WX++9//4uHhweLFiwkKCuLtt98mRbsAKJcolWyzVfZdORekSFoopUYrpeYDdwJ6LmkRSM+wtjomdZyEdyXBI9sS0EtXM0hNc0A31803W8O6NVLq8fHx4bXXXuPgwYMMHTqU2NhYnn32Wdq0acMmPVmi3JGYeNISdnMr+2YLC1IkllFhpVRafgk1OTkVfcoS/s+v/8HNVfjwoWp89HA17r7JOmZxIcbBM7l+/dWx+WvsRpMmTVi5ciWrVq0iICCAw4cPl4vBWE1WPD2tk2AzMpLzSVk2KEiRtBGRq+YvFgjODIvI1QKOrfDcUO0GSzgqMYq52+fiIoKHm9DexkxKxBUHjJc88YQ1fOutcOqU/cvQOIzBgwezb98+fvrpJ7p3727Z//3335OYWPY96lV0rlxZYwm7uVXOJ2XZIF9FopRyVUpVNn9+Sik3m3DZP3sHIyJZDDhO+X2KJeznae0XvZrggBbJO++ArRe/StqhZVmjUqVK3HbbbZbtDRs2cOedd9KiRQtWrFihWyplmGPHphScqAxRAvY6KjZ9GvexhMe0GWMJp9u8A1o0cMfuuLhAZRtdX7eu/cvQlCienp4EBwdz6tQpRowYwcCBAzl8+LCzxdJcA5Urd7WEy8MHgVYkDuZC3AVL+L7WVlMIyzYlWMK2Jubtiu7OKld06tSJkJAQ5s6dS9WqVVmzZg2tW7fmmWee4epV3dNclggK+swSLg/Dzw5VJCIySERCReSYiDyXT7obRSRdRO50pDzOYP/F/Zbw1WTrw77hoHWAbcoX0by14moWd7waTW64ubnx6KOPcvToUR5++GHS09P53//+x8yZM50tmqYIpKRctIRdXBzQI1HCOEyRiIgr8BHG+pMWwD0i0iKPdG8Bqx0lizOp62ftUurqb23OtmhgbYUkJCuORaSx97ReM6ApHDVr1mT+/Pns2LGDW265heees36n6cH40k909F8FJypDOLJF0gk4ppQ6oZRKAb4FhuWS7jHge+BiLnFlnhNXTljC72591xJ+Yogfr91bhRfusI5jrN6tXbNqikaHDh349ddfqVGjBgDJycm0a9eOiRMnEhkZ6WTpNHmRnFy+vGc6UpHUB87abIeZ+yyISH3gdmBefhmJyMMislNEdl66dMnugjqSQU0HWcKRCdYH28VFqFPVlcZ1rC2TTJ8lduG336xh7dyqwrBp0yaOHz/OggULCAwM5OOPPyY93cHmeDRFxsOjTsGJyhCOVCS5rfvP/qZ8D5imlMr3TldKLVBKdVRKdaxVq5bdBCwJbAfbp940NUf8lTjr1N/YxAxS0+2gTJSCW26xbo8fX/w8NWWCvn37snfvXvr378+VK1d49NFH6dChA//884+zRdPYkNkiqVXrbidLYh8cqUjCgAY22/7AuWxpOgLfisgpDBMsH4vIcAfKVOLMWD/DEm5Ws1mO+BQb8ygpaZBhjyUlthZkd+2yQ4aaskTz5s35448/+P7772nYsCF79uyhZ8+ePP30084WTWOSuSCxdu2RTpbEPjhSkewAAkSksYh4AKOALD5MlFKNlVKNlFKNgOXAf5RSP+bMquyyPXy7JWw7gyuTOlVdaVjLahX4fLQduiGm2Cx2atOm+PlpyhwiwogRIzh48CAvv/wylSpVor22uVYqUEoRG7sDgKSkswWkLhs4TJGYtrkmY8zGOgR8p5Q6ICKTRGSSo8otbbw36D1L2Mstd09oAfWs4yQfrYorfqE7d1rDUvYti2quHW9vb2bMmMGxY8e49957Lfvnzp3L77//7kTJKi5i80xevbrViZLYD4euI1FKrVJKBSqlmiilZpn75imlcgyuK6UeUEotd6Q8zmD1MeusZlvbW7bc2dVqwLGSvaeUf/CBnTPUlEX8/f0tL7Djx4/z9NNPM3jwYIYNG8aJEycKOFpjT2xXstepM9qJktgPvbLdwYRGhVrCF+Iv5JrGdrZWcCOPXNMUidE2N+eMGXmn01RIGjRowGuvvYavry8rV66kRYsWvPzyyyQkJBR8sKbYpKZaZ29Wr35zPinLDlqROJjKlazrRC4nXs41jaeHtal7NtIO5hLmzrWGy9gsN43j8fDw4NlnnyU0NJTRo0eTnJzMzJkzad68OcuXLy8Xtp9KM7Zm45VysAuJEkIrEgdTw6uGJWyrVGyxfW7rVXPNNU2RsDXk9+yzxc9PUy6pV68eixcvZuPGjbRt25YzZ87wuV5z5HBiYjZYwq6unk6UxH5oReJgjl05ZgkfjszdUmtckvWrZO2+ZNKKu5bkiy+s4aNHi5eXptxz0003sXPnTj755BPef/99y1jK6dOniYmJcbJ05Y/Tp19ztgh2RysSB9O2TltL+KYGN+Wappqv9TII4FLciVbuNiP2b70F0dHFzFBT3nF1dWXSpEkEBgYCxoDwmDFjCAoKYtGiRWTYZYGTBiAh4ZCzRbA7Utb6Qzt27Kh22k5vBVJTUwkLCyMpqfTZqopJjiE60XiR1/Gtg6db7k3ZqFjrg1rDr5j6PTUVzplrP728oHbt4uWnqXBkZGRw8eJFkpON/vxKlSpRrVo1KmkHadeEp6cn/v7+uLu7s26d9Uuxd++Se/+KSIhSqqMj8naQI4ySJSwsDD8/Pxo1apRljnZpID4lnkORxhdIUK0gvN29c0136qJ1kL1R7WJelgMHoGZNI9y+veHkSqMpIi1atODy5cuEhYWRmppKWloaVatWpX79+ri7l33T5yWFUoqoqCjCwsJo3Lixs8VxCOXiDZOUlESNGjVKnRIBSM1ItYSjEqIcX2BGBtiaEddKRHONiAg1atSgVatWXHfddYgIkZGRHDx4UHd1FYHMeiyNPSb2oly0SIBSqUQAfNx9LGG/Sn65prHtXvSpVMzzsPWU16RJ8fLSaDDGT/z9/alRowZnz57Fz88PF/MDRSlVap+90kRudeTpWX6ez3KjSEorR6KOWMJVKlXJNU2czYLE6r7FbEHYrlKuknt5Gs214OXlRUBAQJZ9kZGRxMbG4u/vj4eHHRbTVgBEPFAqhaZN3ys4cRlB93vYCVdXV9q2bUurVq0YOXKkZZVwYpq1m2nsA2Np3Lgxbdu2pU2bNqxduxaA6PgMm3yK8XWXmmo1H+znZ9durUuXLtG5c2fatWtXKJPk69at49Zbb7Vb+bZcvnyZAQMGEBAQwIABA7hy5Uqhjz116hRff/21ZXvRokVMnjzZEWJmYdeuXUyYMMHh5TgaEbF8XSuliIiI4PLly+zfv5+IiIgidXm9/PLL/Pnnn3nGz5s3jy+//LLYMmcnJCSE1q1b07RpUx5//PFcF2CmpKQwbtw4WrduTZs2bVi3bp0l7r///S8NGjTA19c3yzFz585l4cKF+ZadknIRw88fVK7cufgnU0rQisROeHl5sXv3bvbv34+Hhwfz5uX01XU1+Spz5sxh9+7dvPfee0yaNIm0dEW6+ezVqlzMy7Fnj9WJkZ1naq1du5ZmzZqxa9cuevToYde8i8qbb75Jv379OHr0KP369ePNN98s9LHZFUlxKazTqNdff53HHnvMbuVmJy3NDhYRioiIEBQURLVq1UhNTSU8PJwDBw4QXcjp5q+++ir9+/fPM37SpEncf//99hLXwiOPPMKCBQs4evQoR48ezdV45aeffgrAvn37WLNmDU8//bRFSQ4dOpTt27fnOGb8+PF8UIBtu8TE45awq2vuXd1lkXKnSNatE4f8ikKPHj04dsxYiNiqVivL/rQM68PetWtXwsPDLY6tXAR8PI3LcezYMfr370+bNm1o3749x48fz/GFP3nyZBYtWgRAo0aNePXVV+k+YQKzv/ySTmPHwnHjhj116hTBwcGA8SXWq1cvOnTowMCBA4mIiMgh++nTp+nXrx/BwcH069ePM2fOsHv3bqZOncqqVato27ZtDp/gO3bsoFu3brRp04ZOnToRGxubJX779u1069aNdu3a0a1bN0JDDftjBw4coFOnTrRt25bg4GCOHj1KfHw8Q4YMoU2bNrRq1YqlS5fmkPGnn35i7NixAIwdO5Yff8zpeUApxbPPPkurVq1o3bq1JZ/nnnuOf/75h7Zt2/Luu4br43PnzjFo0CACAgKYOtXqfOyPP/6ga9eutG/fnpEjRxIXF5e1vrt3Z9myZXzwwQe0aNGC4OBgRo0alUOW2NhY9u7dSxvTpH9e9dG5c2cOHDhgOa53796EhIQQHx/P+PHjufHGG2nXrh0//fQTYLSmRo4cydChQ7n55puJi4ujX79+tG/fntatW1vSAcycOZNmzZoxYMAA7rnnHt5++23AMOA4aNAgOnToQI8ePTh8OOei2VdeeYUxY8bQt29fAgICLC/ZdevWMWjQIF566SXGjh2Lu7s7s2fPplu3bjRr1oyPP/7Yksfs2bMthvMgTgAAIABJREFUX/eZ/uUfeOABli9fbrkumXX4zDPPWMrNlHP37t106dKF4OBgbr/9dksrtHfv3kybNo1OnToRGBhYYGs5IiKCq1ev0rVrV0SE+++/P9f75+DBg/Tr1w+A2rVrU7VqVTKXHXTp0oW6devmOMbb25tGjRrlqmQyOXJkoiVcXla1A8YDV5Z+HTp0UNk5ePCgJfz33zjkVxA+Pj5KKaVSU1PVbbfdpj7++GNL3I7wHWpH+A41ZOQQtWzZMqWUUitWrFAj7xqlTl5IVScvpKrk1AxL+k6dOqkffvhBKaVUYmKiio+PV3///bcaMmSIJc2jjz6qFi5cqJRSqmHDhuqtt95S6upVpXbsUG0CAtTxFSuUUkq9+eabaubMmSolJUV17dpVXbx4USml1LfffqvGjRuX4zxuvfVWtWjRIqWUUp9//rkaNmyYUkqphQsXqkcffTRH+uTkZNW4cWO1fft2pZRSMTExKjU1NYu8mfuUUmrNmjVqxIgRSimlJk+erJYsWWLJJyEhQS1fvlxNmDDBkn90dHSOMqtUqZJlu2rVqjnSLF++XPXv31+lpaWp8+fPqwYNGqhz587lqMeFCxeqxo0bq+joaJWYmKiuv/56debMGXXp0iXVo0cPFRcXZ6nHGTNmZK1vk7p166qkpCSllFJXrlzJIctff/1lOef86uOdd95RL7/8slJKqXPnzqmAgACllFLPP/+8Wrx4sSX/gIAAFRcXpxYuXKjq16+voqKilFLGvRcTE6OUUurSpUuqSZMmKiMjQ+3YsUO1adNGJSQkqKtXr6qmTZuqOXPmKKWU6tu3rzpy5IhSSqmtW7eqPn365JB/+vTpKjg4WCUkJKhLly4pf39/FR4erv7++2/l7e2tTpw4oZRS6pNPPlHTpk1TISEh6t9//1UdOnRQJ06cUKtWrVJdu3ZV8fHxSillkXfs2LFq2bJlKioqSgUGBqqMjIwsdTh9+nSLnK1bt1br1q1TSin10ksvqSeeeEIppVSvXr3UU089pZRS6tdff1X9+vVTSikVHh6uBg8enONcduzYYUmjlFIbNmzIcj9kMn/+fHXnnXeq1NRUdeLECVWlShW1fPnyLGkyn3lbXnvtNfX222/n2K+U8Z4qyjvF3gA7lYPey+VusL0kF/jYkpiYSNu2xir2Hj168OCDD+aa7tlnn2Xq1KlcvHiR73/daNnv4Wa0emJjYwkPD+f2228HjIVMheHuu++GFKPv9a4BA/huyxaeGz6cpUuXsnTpUkJDQ9m/fz8DBgwAjC6Z3L6qtmzZwg8//ADAmDFjsnyh50ZoaCh169blxhtvBKBy5Zz2xGJiYhg7dixHjx5FREhNNaZEd+3alVmzZhEWFsaIESMICAigdevWPPPMM0ybNo1bb731mrvRNm7cyD333IOrqyt16tShV69e7NixI1f5+vXrRxVzYkKLFi04ffo00dHRHDx4kJtuMqwRpKSk0LVrV8sxd99tdZEaHBzMfffdx/Dhwxk+PKeDz4iICGxdROdVH3fddRcDBgxgxowZfPfdd4wcaXjP++OPP1i5cqXl6zwpKYkzZ84AMGDAAKpXrw4YH4UvvPACGzZswMXFhfDwcC5cuMDGjRsZNmwYXl6GP5yhQ4cCEBcXx+bNmy3lAJYFiNnJPN7Ly4s+ffqwfft2qlatSqdOnSxrI/7880/27t3Lb7/9ZjpviuXo0aP88ccf3HXXXZbyM+XNpHLlynh6ejJhwgSGDBmSY2wtJiaG6OhoevXqBRitUFuZR4wYAUCHDh04deoUYNgRW7VqVY7zULmMh+Q2o2r8+PEcOnSIjh070rBhQ7p164abW8Gvy9q1a+faqivvlDtF4iwyx0hsGTduHLt27cKnhg/vL34fLzcv5syZw4gRI/jfO+/zzOPj+XnNdupUtfYw5najA7i5uWUZyMw+J93HxwfMrqq7Bwxg5IwZjHjwQUSEgIAA9u3bR8uWLdmyZUuRzqugqZ2qENM/X3rpJfr06cOKFSs4deoUvXv3BuDee++lc+fO/PrrrwwcOJDPPvuMvn37EhISwqpVq3j++ee5+eabefnll7PkV6dOHSIiIqhbty4RERHUzmU8KK96zA3b1dqurq6kpaWhlGLAgAF88803uR7j42Od1v3rr7+yYcMGVq5cycyZMzlw4ECWl46Xl1eW65VXfdSvX58aNWqwd+9eli5dyvz58y3n8v333xMUFJRFhm3btmWR46uvvuLSpUuEhITg7u5Oo0aNSEpKyrMuMjIyqFq1ao77NjeyX+PMbdvylVJ8+OGHDBw4MEvapUuXEhkZyZEjR2jQoAHe3lkX5bq5ubF9+3bWrl3Lt99+y9y5c/nrr78KlCmTzOuXee3yw9/fn7CwMMt2WFgY9erVy5HOzc3N0vUJ0K1btxwz1nIjKSnJojArEuVujKQ0sXDhQlZvXM37i98HwMvduMFcXFyYMOkxMjIyWP/3H8QmWh/0ypUr4+/vb+m3TU5OJiEhgYYNG3Lw4EGSk5OJiYmxzPiyoBSYL6sm/v64urkxc+ZMy5dzUFAQly5dsiiS1NTULP3xmXTr1o1vv/0WMF5M3bt3z/ccmzVr9v/tnXl4U1XegN+TltKVtuxL2bdC6EJboDAsRaAgZZAdAUFAYBRZxplBEDcQdQQdVHRmlG/YZWQZwMGNTUAQcaBIqVBqy75DFxq6t0nO90ea26RJ2rIE2nrf58nTJPfm5tyT2/u7555z3h/Xrl3j6FFT6tDMzEybf2adTkejRo0AlH4dgHPnztGiRQtmzZrF4MGDiY+P59q1a3h6evLUU0/xl7/8hZ9//tnmOwcPHsyaNWsAWLNmDU888YTNOj179mTjxo0YDAZSUlI4cOAAnTt3xsfHx6YPxx6RkZEcOnRI6evKyckhKSnJZj2j0cjly5fp3bs3S5YsISMjQ+lLMdOuXTtlO6XVB8CTTz7JkiVL0Ol0BAUFAdC/f38++ugjJSAcP37cbpl1Oh1169alWrVq7Nu3j4sXLwLQvXt3vvzyS/Ly8sjKyuLrr78GTMda8+bN2bx5M2AKBCdOnLC77f/+97/k5eWRlpbG/v37lRaoJf379+ef//yn0sJKSkoiOzub6OhovvzyS1JSUkhISCA+Pt7qGMnKykKn0zFw4EA++OADm8Dm6+uLv7+/0v+xbt06pXVytzRo0AAfHx9++uknpJSsXbvW7vGTk5NDdnY2ALt378bV1ZX27duXuf2kpCQ6dOhQ5noeHm3uvvAVGDWQOJnLd4pzMmtEcXX7eLgw44X5LP/4PXLyra8Y161bx7JlywgODqZbt27cuHGDxo0bM2rUKOU2SseOHa2/qOigB6BOHUaPHs1nn33GqFGjAFMOiv/85z/MnTuXkJAQQkND+fHHH23Ku2zZMlatWkVwcDDr1q3jww8/LHX/3Nzc2LhxIzNnziQkJIR+/frZtJZefPFFXnrpJX73u99ZjXLauHEjHTp0IDQ0lMTERCZMmMAvv/yidMC/9dZbvPLKKzbfOW/ePHbv3k3r1q3ZvXu30nlrydChQwkODiYkJITHHnuMJUuWUL9+fYKDg3F1dSUkJMTqirMkderUYfXq1YwZM4bg4GAiIyPt3rIwGAw89dRTBAUF0bFjR1544QX8/Pys1gkMDESn0ykBzFF9AIwYMYINGzYovxuYWjCFhYUEBwfToUMHXn31VbtlHjduHLGxsURERLB+/XoCAwMB6NSpE4MHDyYkJIRhw4YRERGh3Mpbv349K1asICQkBK1Wa9VBb0nnzp2JiYkhMjKSV1991e5V/JQpU2jfvj1hYWF06NCBP/zhD+j1ekaPHs3o0aOZNGkSY8eO5f333+fkyZNKaykzM5NBgwYRHBxMr1697P4ua9asYc6cOQQHBxMXF2fTSi3JtWvXGDhwoN1l//znP5kyZQqtWrWiZcuWPP744wBs375d2e6tW7cICwujXbt2LF68mHXr1imff/HFFwkICCAnJ4eAgAAWLFigLDt06FCpI9HMVJXMiGaqhLTx9OnTtGvX7hGVyDF6o564G8VXV9o6WqVVAtZ+raZ1XO5vhrBlnYSHq7naKxjvv/8+Pj4+j2wuSVZWFt7e3uTk5NCzZ0+WL19OWFhYuT67YMECvL29ldFU90NOTg6XLl1SWm3t27e3udVVWTl+/DhLly61CjqWnD59mps3Ta2aGjW6EhZmeyHnTJwpbVRbJE7EVWPdBeXI/Av3qXixvFXj46MGkQrIc88990jNudOmTSM0NJSwsDCGDx9e7iDyoPH09KRt27Y0b96cBg0aWAWRRzEX5kGSmprKokWLyrVu9eqNnVyah4va2e5ECg3FwsYG3g2sgoVlS7B6tfs88f9anBeeKmoXrey4u7szfvz4R/b99zMJ0/LWzYPALDG0RKfTce7cORo2bEidOnUUl1dlwjwi0hGWaXXr1Bnh7OI8VCrfr1WJsMyIWN+7vtWytKzig6qu7338DGkWRuH69UH1HalUQnQ6HQaDgcuXL3P69GnuWMpHqwh6ffGMfzWQqJSbfEPxmPyrmVeV51JKsixGarnca0pEKeH8+eLXRSOBVFQqG40bN6ZVq1ZUr16d3NxckpKSOHv2rMN5LZURg6H4FnRVMyargcSJ1POqpzyvUb14IlxuwQOy/VoMKaVJE7VvRKXSIoTAz88PrVZLo0aN0Gg03L59m1OnTlW51ombW/2yV6pkqIHESUgpuZl9EwDPap74uRcPCdXlFAeSOzn3mCDo7FnQ6UzPW7RQ0+mqVAk0Gg0NGjSgQ4cO+Pv74+LiUmVGdZmpVcs5VuxHiRpIHhAlNfIXUi4oy1r6mxLYTJw4kebNm9M/KpzHe4dx6ED5Z+9akZICZnW6mxuUUE44g4qkkd+8eTNarRaNRkPJoeAP4/vvluvXr1eYslQUylLE79ixg82bN9O+fXvFEmAwGLhw4cJ9ZRo8f/48Xbp0oXXr1owePZqCIq1QSQYMGICfn5/N7/bxxx/TqlUrJVukma+++orXX3/d4fdadrT7+HS+5/JXVNRA8oAoqZFf9vdinbRBFk86e/fdd9n9/TFeW7SUV158nvr+Lnf3RdnZUDRjGYCi2c/Kd5VTa363VCSNfIcOHdi6dSs9e/Z8pOUoL0uXLmXq1KlO276zfvPyIqW869S7ZSniBw8ezLx586xyw9+8eZPU1FROnTrFlStX7mm/586dywsvvEBycjL+/v6sWLHC7npz5syxOx/kd7/7HXv27KFp06ZW78fExLB9+3YlD1FJjEbLgFX10hRXuUAiFgqnPO6GHj16kHa1eDRVVkGxMsNglOQWSMIiIrlx4yqudhJZlaqRP30agBlLlrD6yBEQwkprvmTJEjp3Lr7iqYoa+Xbt2tl4p+yRlZXFiBEjCAwMZNy4ccqQ6zfeeINOnTrRoUMHpk2bprwfFRXFH//4R7p160aHDh0UHbgjjfr48eOtZoKPGzeO7du325Rjy5YtDBgwQPk9evToQVhYGGFhYYpdYPTo0VaSwYkTJ7JlyxYMBgNz5syhU6dOBAcHK/6t/fv307t3b8aOHauoVIYMGUJ4eDharZbly5cr21qxYgVt2rQhKiqKqVOnKom8UlJSGD58OJ06daJTp04cOnTIpuyrV6/miSeeYMCAAbRt25aFCxcq+9GuXTumT59OWFgYly9f5t1331XKaXl1vnbtWsUyYB4CbamIt6fht0w4Zj4m+/fvz+zZs7l+/To3btxg6NChTJs2jW7dutGiRQtFSe8IKSV79+5lxAjTiClHKQjAJPL08bHNF9KxY0eaNWtm874QgqioKL766iu729NYzCnz8goutZyVEmdphZ31KEsjzwKc8iiLkhr5t5e+rejj8/X5UkqTNnvVuo3y/M1C+emq/8gRo560uy2HGvmBA6U8elTKo0fl8yNHylVFKvOSWvOQkBB59uxZKWXV1Mib6dWrlzx69KjdZfv27ZM1atSQly9flgaDQUZGRsqDBw9KKYs15lJK+dRTT8nt27cr2zN/9/fffy+1Wq2U0rFGff/+/Ur9ZGRkyGbNmin7aebcuXMyLCxMeZ2dnS1zc3OllFImJSVJ8/G8detWOWHCBKUuAgICZE5Ojvz000/lokWLpJRS5uXlKWr2kgp3y/3KycmRWq1WpqamyqtXr8qmTZvKtLQ0WVBQILt37678jmPGjFHq5OLFizIwMNCmHletWiXr168vU1NTle0ePXpUnj9/Xgoh5OHDh6WUUu7cuVNOnTpVGo1GaTAYZExMjPz+++/lyZMnZZs2bWRKSopVGS0V8fY0/JbHW8ljMiYmRiYkJMiYmBjZp08feerUKRkbGytbtmyplDskJMRmX8xqfTOXLl1SfmN7lEw5YEnTpk2VfTLz2WefyRkzZthdv6AgXf7887dy3z5kYeEdh9/pTFA18uVHvl4xNPJRQ6OUZZYz3F97eS68PI+01Fv8aMfEW6pGvuSoLAunk6XWfNSoUWzatIl58+b9ZjXyYPJDBQQEABAaGsqFCxfo3r07+/btY8mSJeTk5JCeno5Wq1XU6mPGjAFM0sc7d+4o2f7sadSHDBnC888/z61bt9i6dSvDhw+3UY2XVMgXFhYyY8YM4uLicHFxUUSQjz/+OLNmzSI/P58dO3bQs2dPPDw82LVrF/Hx8crVtk6nIzk5GTc3NyuFO5iu7Ldt2wbA5cuXSU5O5saNG/Tq1UtRt48cOVL5zj179pCQkKB8/s6dO2RmZtpciffr10+ZQDhs2DB++OEHhgwZQtOmTYmMjARMqvtdu3YpDrisrCySk5M5ceIEI0aMoHbt2oCtQh7K1vDbOyYDAwPx9vamW7du5OTk0LJlS27evKl8xp7RWJZTIX+v1K1bl2vXrjlYajkZ+dHeinQGTg0kQogBwIeAC/AvKeU7JZaPA+YWvcwCnpNS2tePVnAsNfJSSo5dP8bCFxby68lfadW0lXLbYt5r7zAgZiir/+8jJk+ayLFjx6y2Y+9ghyKNvIWYMU8Iq8mHljrv0aNHM3LkSIYNG1YlNfLlxZ4ePi8vj+nTpxMbG0vjxo1ZsGCBVeetI126o/fHjx/P+vXr2bBhAytXrrQpQ0mF/Pvvv0+9evU4ceIERqNRuVBwd3cnKiqKnTt3snHjRiWgSQdq9v3791v95vv372fPnj0cPnwYT09PoqKiSlXIg8lcfPjw4TK15+VVyL/00kv84Q9/sFp32bJlZR4f9jT8ZZVHCIG7uzstW7akSZMm+Pn5KfualZWFl5eXzffWrl2bjIwM9Ho9rq6uDhXy90rpCvnifhEXl6o1Cg2c2EcihHAB/g48DrQHxgghSnqYzwO9pJTBwCJgOVWAs7dNaW5ff/91TsSdUIKIuT9So9Hw/MzZGI1Gdu7cafVZhxp5Dw8Sfv2V/IICdFlZfFdKOs+WLVvi4uJSZTXy94P5pF67dm2ysrJs7qub+2R++OEHfH19FUuuI436xIkT+eCDDwDQarU239emTRsl2RKY6qJBgwZoNBrWrVtn1WH85JNPsmrVKg4ePKgEDkdq9pLodDr8/f3x9PQkMTGRn376CTC1yr7//ntu376NXq9ny5Ytymeio6P5+OOPldeO8pLs3r2b9PR0cnNz+eKLL5RkX5b079+flStXKjLGq1evcuvWLfr06cOmTZtIKzIwpKenW32uPBr+0o5JFxcX6tatqwSN3NxcEhMTOX36tM12hBD07t1b+c0dpSC4V0pTyOflnVOeazRVzz7hzM72zsAZKeU5KWUBsAGw+tWklD9KKYvGsfITEODE8jwUCg2FZOSZbod4uHootl+DUZJTYOHa8XXhlVdeYcmSJTbbsKuRl5JRffsSPGYM4/76V1uNfAmqskZ+27ZtBAQEcPjwYWJiYmyu1kvDz8+PqVOnEhQUxJAhQ2zyavj7+9OtWzeeffZZqxE9jjTq9erVo127dkyaNMnu93l5edGyZUslH8n06dNZs2YNkZGRJCUlWV3VR0dHc+DAAfr27YtbUWvTkZq9JAMGDECv1xMcHMyrr76q3HJq1KgR8+fPp0uXLvTt25f27dsrwXHZsmXExsYSHBxM+/bt+eSTT+zuQ/fu3Rk/fjyhoaEMHz6ciAhbgWx0dDRjx46la9euBAUFMWLECDIzM9Fqtbz88sv06tWLkJAQ/vSnP1l9rjwa/rs5JgsLC3Fzc2PIkCEkJiZy/vx5JQgDLF68mKVLl9KqVSvS0tKUTKaxsbFWZuYePXowcuRIvvvuOwICApQLvmXLlinJsYKDg60+s2/fPmJiYhyWrXif7334ckXFaRp5IcQIYICUckrR6/FAFynlDAfr/wUINK9fYtk0YBpAkyZNwi9aDn+lYmnk03PTOXfbdPXh5uJGcL1gjEbJzQwD+UX//w1ruiipdcvFuXNgeSWnauKdQlRUFO+9957NibI0jXpOTg5BQUH8/PPPygm6JNu2bePYsWO8+eabTil3WZgV8nq9nqFDhzJ58mSlD64sVq9eTWxsrFXLpaJjMBi4ceMGN27cQEqJRqOhYcOG1K1b12kyyJs3bzJ27FjbhHNFZGbGcuZMKjrd42i1W6hTZ5hTylEalVUjb+9MZzdqCSF6A89Q3F9i/SEpl0spI6SUEZYdlxURbzdv5XmBwTR2/Ha2UQkiANfSDRiM5QzgmZnFQcTVVQ0iFYg9e/YQGBjIzJkzHQYRMCXZsjdk9GGxYMECZbJs8+bN7XZoVyVcXFxo1KgRWq0WX19fjEYjV65csUqx+6C5dOkSf/vb3xwud3Mr7ovx9a0c85/uBme2SLoCC6SU/YtevwQgpfxrifWCgW3A41JK21ymJajoia3yCvM4mXISAH93f1rWbElWnhFdtpFCi8Ea5UpkJSVYdsZ36ADujnOaqKio2KLT6bhy5YoihYTyDRJ5kFi2SKKiHs3IUme2SJw5auso0FoI0Ry4CjwJjLVcQQjRBNgKjC9PEKnoSCm5oLugvG7i2wQAb3cN1VwE12+bIolGlGPYoV4Plp2fjRurQURF5R7w9fWlRo0ayv+clJKkpCR8fHyoX79+pcx9UtFwWiCRUuqFEDOAnZiG/66UUp4SQjxbtPwT4DWgFvCPoh9Z76yI+TAwGA1Ws9iruVQrel+SllncHKnprSn7isgyiDRvDiUSAamoqJQfy/+1zMxM5ZGamkrjxo3x8/N7aC2UnJwzeHq2eijf9bBw6jwSKeU3wDcl3vvE4vkU4NEksXYCGfkZVq/1Rj2uGlfSM40UWPSRpGYaySkQ1PW149kyGq318J6eahBRUXmA1KhRg7Zt23Lp0iVyc3M5e/YsNWrUoEmTJtYTgB8gGo03YJI8qvNIVErFcga7j5sPLsIUKDzcBG4lQrbD9Lo3b4Jl/oUK0vejolKV8PHxoX379jRp0gQXFxfu3LnDqVOnSpmZfu8YjXqMRtOdCldXP6pXf3CTICsKaiB5QLi4uNA5vDOjHxvNvGnzaOTeqHgGsLugQA9/mTWZHhGtGdwvgp5dO9oOFczOhqvFmRQJDKwwI7QqkkZ+zpw5BAYGEhwczNChQxWNyYOitLIfP37cau6ACrz22mvs2bPH4fKylPH3yrFjxwgKCqJVq1bMmjXL7iz+tLQ0evfujbe3tyKBBNOw7UGDBtGzZ0/GjRvHihUrlKHCH3/8MatWrXpg5czPv6w879y50ncF20UNJA8Idw93/r3732zcuxEfTx8r+6rOInnVorcXE38ijg8++IBnn322eAP5+YrZFzDp4b2LhxKXl9+CRr5fv36cPHmS+Ph42rRpw1//+teyP/SAePvtt5k5c6bTtm9vsuHD5F6OnzfeeIO+ffs6XF6WMv5eee6551i+fDnJyckkJyezY8cOm3Xc3d1ZtGiRYhq25C9/+QuJiYlK+ofLly9Tt25dJk+ezLJly7h9+7ZDLfzdoNcXm8BdXW1ddFWBKhdIpv4j3SmPMrG4GAoMC+REgkkZVqCXZGQXL/SqbmphdO3alavm1odeD7/8oqxzBuhbpFK30cgXMWPGDEU38lvTyEdHRytyxMjISLvzA65fv07Pnj2V+RPmVtRzzz1HREQEWq3WSnW+Y8cOAgMD6d69uyIILElmZibx8fGEhISUul9dunSx0s9ERUVx7NgxsrOzmTx5Mp06daJjx46Kgn716tWMHDmS3//+90RHR5OVlUWfPn0ICwsjKCjISlW/aNEiAgMD6devH2PGjFFOkGfPnmXAgAGEh4fTo0cPEhMTbcrvSIdfUknvSF0PsGTJEoKCgggJCWHevHmASRNj1o7MmzdPUcKbJ3BaKuPj4uKIjIxUWpO3ixK0RUVFMXfuXDp37kybNm3KbPVev36dO3fu0LVrV4QQTJgwwa4S3svLi+7du9v0fXh6etK7d2/AZGcICwsjNTUVjUaDp6cnTZo04auvviIhIYGLFy/ec4C3TGgFoNFUd7Bm5abK2X8fFebbWHq9nh/3/ciggYPIzjOSlll8IHlV1yjr7dixo3hi2KVLxRvy8mLcxInMmzePoUOHkpeXp/iISsPd3Z0ffvgBMKlHzB6rjRs3MmrUKAoLC5k5cyb//e9/qVOnDhs3buTll1+2EQ3OmDGDCRMm8PTTT7Ny5UpmzZrFF198wRtvvGF3hnNBQQGjR49m48aNdOrUiTt37tiI6wIDAzlw4ACurq7s2bOH+fPns2XLFj755BNmz57NuHHjKCgowGAw8M0339CwYUO+/vprwDQHoDRWrlxpZT428+9//5v+/fvz8ssvYzAYlCvLt956i5o1a2IwGOjTp4/Sqpk6dSp79+6lVatWdrcHJo2GpUvJ0X49+eSTbNq0iYULF3L9+nWuXbtGeHg48+fP57HHHmPlypVkZGTQuXNn5Ur+8OHDxMfHU7NmTfR6Pdu2baNGjRqkpqYSGRnJ4MGDOXbsGFu2bOH48ePo9XrCwsIIDw+8yA83AAAgAElEQVQHYNq0aXzyySe0bt2a//3vf0yfPp29e20zcMbHx/PTTz+RnZ1Nx44dFaXHkSNHOHnyJM2bN2f58uX4+vpy9OhR8vPz+d3vfkd0dDSJiYl88cUX/O9//8PT09PGm5Wens62bdtITExECGH3luOECRP46KOP6NWrF6+99hoLFy5UXGV6vZ4jR47wzTffsHDhQvbs2cO1a9eYMmWKVa4WMLm8zGZngICAgOILs7skIyODL7/8ktmzZyvvRUREkJSURLt27UhJSeH27ds0bNiQOnXq3NXoLiGsr9XPnPkzrVo5nrhYWalygeT/pjs/7aw9cnNzGdvPNE2mY5eOTJ44k5Q7piDiooFGNV0QwnR//8UXX+TWrVsmsZ6UVvqTzFu3HGvkS+G3qJF/6623cHV1Zdy4cTbLOnXqxOTJkyksLGTIkCGK4n/Tpk0sX74cvV7P9evXSUhIwGg00rx5c1q3bg3AU089ZXVr0kxJJbyj/Ro1ahT9+vVj4cKFbNq0iZEjRwIm1fr27duVq/O8vDwuFV1E9OvXT1GsSymZP38+Bw4cQKPRcPXqVW7evMkPP/yg6OwBRX2flZXFjz/+qHwPmGSf9rCnw/fz87NS0jtS1+/Zs4dJkyYpOdRLKuFr1KiBu7s7U6ZMISYmxqafSafTkZGRQa9evQBTYinLMg8bZtKGhIeHK6LLhg0b2gQRcx2V5F6G7+r1esaMGcOsWbNo0aKF8n79+vVJTExEq9Vy6dIlMjMzuXTpkjJc2F7SK0doNF4YjSbRptGYW8balZMqd2vrUXAn7w7V3avz793/ZsOeDaz/v/Vk5rsyZ/YzDHwsnGfG/R6NxnSQv/vuu5w5c4Y333yTp59+2tSZbpFOtFSNvEU605JixJIa+U2bNpGUlKRo5KWUaLVa4uLiiIuL45dffmHXrl1l7tuD1MifPHmSL7/8Uin72LFj2b59Ox4eHvTv35+9e/fSpk0bpRP1pZde4o033rC7zTVr1vDVV1+xfv16u9/fs2dPDhw4QKNGjRg/fjxr167l/PnzvPfee3z33XfEx8cTExOjlKU8J6GSSnhH+9WoUSNq1apFfHw8GzduVLL+SSnZsmWL8htcunRJMTJY/n7r168nJSWFY8eOERcXR7169UpVwhuNRvz8/JTtxsXFcdqyv82C8irhP/roI2Vb58+fJzo6uszf2tXVlSNHjjB8+HC++OILJStkeTHPOjcr/0vDLE40c69K+GnTptG6dWv++Mc/Wr1vVsJ7eHjQpk0bWrRogZubGzk5OVy5cqVUPb8lUhqVIAJQo0Y3MjIOlPvzlQU1kNwn526fIyndNBLDw9WD0Pqh6HKKgsaHK9h78Gd27rC+otJoNMyePRujwcDOVavAwk5ao3t3+xr5pk1JSEggPz8fnU7nUA4HVV8jv2PHDhYvXsz27duVq+OSXLx4kbp16zJ16lSeeeYZfv75Z+7cuYOXlxe+vr7cvHmTb7/9VtmH8+fPc/asSf//+eef291mu3btFItvafsFJiX8kiVL0Ol0Sirc/v3789FHHyknkePHj9v9Hp1OR926dalWrRr79u3DLCnt3r27ErCysrKU2381atSgefPmbN68GTAFghMn7Kf1caTDt8SRuj46OpqVK1cqtwlL3trKyspCp9MxcOBAPvjgAxstva+vL/7+/kr/x7p165TWyd3SoEEDfHx8+Omnn5BSsnbt2rtWwr/yyivodDrl1pollkp4IQQ1a9ZEq9XSoEEDGjdubHUru/R89daBNzFxPHFxvUhIGHVXZa3oqIHkPkjNSSU9t/ifqX2d9qTekWTmFl9t1PXV2L2KE7du8crYsSz5xz9Mb/j7Q1gYuLra18g3bsyoUaOUbHK/ZY38jBkzyMzMpF+/foSGhlqPfiti//79hIaG0rFjR7Zs2cLs2bMJCQmhY8eOaLVaJk+erOTVcHd3Z/ny5cTExNC9e3eaNm1qd18DAwPR6XTKYAJH+wUwYsQINmzYoNQ/mFowhYWFBAcH06FDB1599VW73zNu3DhiY2OJiIhg/fr1BAYGAqbbdYMHDyYkJIRhw4YRERGhyCLXr1/PihUrCAkJQavVWnXQW+JIh2+JI3X9gAEDGDx4MBEREYSGhtqMhMrMzGTQoEEEBwfTq1cv3n//fZttr1mzhjlz5hAcHExcXFyZScuuXbvGwIED7S775z//yZQpU2jVqhUtW7bk8ccfB2D79u1W223WrBl/+tOfWL16NQEBASQkJHDlyhXeeustEhISCAsLIzQ0lH/961/KZw4dOmQzEs0sg/S2GE158eJFEhISHPblCSHw8gpBiOpWHe0pKaXnl69sOE3a6CwqgrRRSsm1zGtczyoe9RTeIJysPKl0rmsENK7tQMyYmgoWyY4ICoLqVXM0R1Xj/fffx8fH55HNJTEr4XNycujZsyfLly8nLCysXJ8tTYevUszx48dZunQp69atK3U9vV5PYmKicuHk5+dH48aNrTJzWnL69Glu3izO7afVbqVOnfLp/B8ElVUjX2XJyMuwCiIh9ULIK5RWI7Qa1XIQRDIzrYNIu3ZqEKlEPPfccw5PFA+DadOmERoaSlhYGMOHDy93EFEpP6mpqSxatKjM9VxdXWnfvj0BAQFoNBoyMjI4efIkV69eLdd8nFOnhnH+vP1WaWVDbZHcAzezbnL5TvFw3NB6YVxJswgiNV2oZi9xVW4ulOyXUIWMKiqVnoKCAq5cuaL0G1WvXh2tVmtlFjafpw4fbkx+fvFAgYellVdbJBUIg9FgFUQa+jS0CiJ+Xhr7QQTAxY6k0bXKjcBWUfnN4ebmRosWLWjbti0eHh74+vo61NO3aLH4IZfO+ahnsbsgqyCL87fPK69retSkZvX63MgtDiS+ng6CiJQQH2/9Xt26kJMDOh3Uq6fe4lJRqeSYZZCWI7kyMjLIzMxU3nN1tc6mKaUBIexcZFYi1EBSTixzsQsEgbUDccGDGxnFB4y/t/0RWgDYEwveumX7vFEjsDNRUEVFpXIghMCl6O6DlJLLly+Tn59Peno6sbGxDBrUqMT6lTuIgHprq1yk5qQqQQSgXZ12NkGkrq8GX89SqtPHxxQgvLxMLZG6de2vl14Or5eKikqlQAhBixYt8Pb2xmg0MnHiRHr27EeRlg2AgoKbj66ADwg1kJRBak4qFzIuKK+1dbS4Cusg4uoCPp7VFEHgyJEjba2hrq5MfPllmg8cSOjgwYTExPDdkSO2X1ik6ahoVCSN/KuvvkpwcDChoaFER0c7JYfEg+D69etOq4PKSllK+e3bt/POO+888O89f/48Xbp0oXXr1owePZqCggK76w0YMAA/Pz+b361Hjx6EhoYSGhpKw4YNFU/eV199ZSX/tIeXlxdt27alVq1a1K9fn4QEeO45eO89yMqCH3+s/2B28hGiBpIysAwiABqhoUBvPcpCbzDpM8w6ajc3Nz755BPs8e7ChcRt2sQHzz/Ps5b/MKGhEBEBbm73Vd7fgkZ+zpw5xMfHExcXx6BBgxxqVJzB3Vhgly5dytSpU51WFmf91uVFSlnGrG5bylLKDx48WLEKP0jmzp3LCy+8QHJyMv7+/qxYscLuenPmzLE7f+TgwYOKMqZr166KFywmJobt27eXqZsXQuDt7c2vv/7Kn//8Z1xcBEeP2h9/UxmpeoFEiAf6iGjUiYhGxRqJpLQzpGda//M08Lc+Gnr06GGl0iAz02T4TU+H69fhzh26BgVx9dYtaNIEwsOtRm+dOXOGvn37qhp5Bxp5SzFkdna23X4pRyr2Cxcu0K5dO6ZOnYpWqyU6OlrZn6NHjxIcHEzXrl2ZM2eOosgoqXkfP3681czxcePGsX37dpsybNmyRfFNXbhwgR49ehAWFkZYWJhiFRg9erSVlHDixIls2bLFocq9pPIdYMiQIYSHh6PVaq1kkytWrKBNmzZERUUxdepUJbFTSkoKw4cPp1OnTnTq1IlDhw7ZlH316tU88cQTDBgwgLZt27Jw4UKr+ps+fTphYWFcvnyZd999Vymn5dX52rVrCQ4OJiQkhPHjxwPWSvlly5Ypynmzj2z16tVKOe0di+Y6mjVrFt26daNFixaKXNIRUkr27t3LiBEjAJMs0p5yHqBPnz6lChkzMzPZu3ev0iIRQhAVFcVXX31VahnM1KhRg/fee48VKyQvvQQeHuDr24PMzMxytfQrLFLKSvUIDw+XJUlISCh+YRof9cAfR68elUk3bsvzNwutHkajUUoppZeXl5RSysLCQjl48GD5j3/8Q8q8PCmPHlUeT8fEyM3vvSfllSty28aNcsyYMTb7IqWUnTt3llu3bpVSSpmbmyuzs7Plvn37ZExMjLLO888/L1etWiWllLJp06Zy8eLFyrKQkBB59uxZKaWU77zzjly0aJEsKCiQXbt2lbdu3ZJSSrlhwwY5adIkm+8eNGiQXL16tZRSyhUrVsgnnnhCSinlqlWr5PPPP2+zfn5+vmzevLk8cuSIlFJKnU4nCwsLrcprfk9KKXfv3i2HDRsmpZRyxowZ8rPPPlO2k5OTI//zn//IKVOmKNvPyMiwW0fz58+XAQEBUqvVKvtkSWFhodTpdFJKKVNSUmTLli2l0WiU58+fly4uLvL48eNSSilHjhwp161bJ6WUUqvVykOHDkkppZw7d67UarXKvjdq1EimpaVJKaXcv3+/Ui8ZGRmyWbNmyv6ZOXfunAwLC1NeZ2dny9zcXCmllElJSdJ8HG/dulVOmDBBqYOAgACZk5MjP/30U7lo0SIppZR5eXkyPDxcnjt3Tu7bt096enrKc+fOKds2lysnJ0dqtVqZmpoqr169Kps2bSrT0tJkQUGB7N69u/L7jRkzRh48eFBKKeXFixdlYGCgTf2tWrVK1q9fX6ampirbPXr0qDx//rwUQsjDhw9LKaXcuXOnnDp1qjQajdJgMMiYmBj5/fffy5MnT8o2bdrIlJQUqzK+/vrr8t1335VSStmgQQOZl5cnpZTy9u3byveay+noWHz66afliBEjpMFgkKdOnZItW7ZUyh0SEmKzL+bf38ylS5eU39YeJf/XLFmzZo0cPny41XufffaZnDFjhsPtmTGfp4xGg9y3D+Vx4sTjcs6cORKQY8aMkVeuXClzW/cCECuddF6uei2SBxg+pNHIrymJ/Hw1jtquoVQTxY6d+n4amtV1Va6Gc3NzCQ0NJSIigiaNG/PMgAFWyaoA8PVlzscf06JHD56aPJn58+fbFD8zM9NGI+9ITGiJPY08mHxWo0ePttLIh4aG8uabb9pNCHX48GHGjjXp8MePH6/kOHGEPY28a4m5MTqdjpEjR9KhQwdeeOEFRRbZtWtX3n77bRYvXszFixfx8PAgKCiIPXv2MHfuXA4ePKi4pEry1ltvcfnyZcaNG2eTIwWKVezBwcH07dtXUbEDNG/eXNHKm5Xl5iGa3bp1A1DqwIyl5r1Xr16cOXOGW7du8fnnnzN8+HCbfS6pnC8sLGTq1KkEBQUxcuRIEhISAHj88cfZu3cv+fn5fPvtt/Ts2RMPDw927drF2rVrCQ0NpUuXLqSlpZGcnAxgpXwH05V9SEgIkZGRXL58meTkZI4cOUKvXr2oWbMm1apVs9K179mzhxkzZhAaGsrgwYO5c+eOTSvSvM+1atXCw8ODYcOGKcdC06ZNiYyMBEzK+V27dtGxY0fCwsJITEwkOTlZaQHUrl0bsFXOA4o37rPPPrOpPyj9WBwyZAgajYb27dsrvytgI4qEB6ecB5PQc8yYMVbv1a1b96766UrmKElP/xaNJhF3d3c+//xz2rZtyzvvvOMwFUBFpOoFkgdESnYKx64fI6egkJquWqtl9Xw1uLtZV525jyTu66/5aOJE3NLSmLRwIaFjxzJw9myTkLFaNVuNfAnsHfSgauQdMXbsWLZs2WLzviMVO2ClODEryx3VuxnL+gXTiW39+vWsWrWKSZMm2axfUjn//vvvU69ePU6cOEFsbKzS2evu7k5UVBQ7d+60Uc7bU7mXLMv+/fvZs2cPhw8f5sSJE3Ts2LFU5TyYtPOHDx9Wtn316lW7t3PKq5x/6aWXlG2dOXOGZ555plzHxddff83zzz/PsWPHCA8PL7P/yXJ7lr9hWb9d7dq1ycjIULZ/r8r5tLQ0jhw5oiQDM2NWzt8NLVtaCy///OeJnD59mqFDh5Kdnc1LL71EUFCQ3VwsFRE1kNghpzCHy7rreGoa4O8aqLxfzcU0zNejeolq0+tNrZjYWFMfSBGrXn+duNhYvjl0CCxmuSoaeaORnTt3Wm2qRo0aqka+DI28+cocTKN8zHbckt9pT8XuCH9/f0VLDih14IiJEycq+nGtVmuzvE2bNkpyJnN5GjRogEajYd26dVYd5U8++SSrVq3i4MGD9O/fH3Cscre3n/7+/nh6epKYmKiUv3Pnznz//ffcvn0bvV5vFWyjo6OtWnH2ruIBdu/eTXp6Orm5uXzxxReKLdmS/v37s3LlSrKysgBT5sJbt27Rp08fNm3aRFqaKV95SeW8Oetn7969WbJkCRkZGco2zNztsegIIQS9e/dW+lLWrFlz18p5gM2bNzNo0CCbZHOWyvnykpZm3aei19+hadMmbN26lV27dhEYGEhycjIxMTHK/1ZFRg0kFkgpyc03cjtTQ03Xdnhq6inLdIYzXM+P43a+bQc1iYmmQFKSiAiwkzEQTAf3K6+8wpIlS2yWqRr50jXy8+bNo0OHDgQHB7Nr1y67ZXSkYi+NFStWMG3aNLp27YqU0uFtNYB69erRrl07u60RMF21t2zZUhl0MX36dNasWUNkZCRJSUlWV/XR0dEcOHCAvn374lY0as+Ryr0kAwYMQK/XExwczKuvvqrccmrUqBHz58+nS5cu9O3bl/bt2yv7s2zZMmJjYwkODqZ9+/YORxh2796d8ePHExoayvDhw4mIsNU0RUdHM3bsWLp27UpQUBAjRowgMzMTrVbLyy+/TK9evQgJCeFPf/qT1ecMBgNPPfUUQUFBdOzYkRdeeAE/Pz+rde72WASUW5YlWbx4MUuXLqVVq1akpaXxzDPPAKb0yZYm5x49ejBy5Ei+++47AgICrC70NmzYYHNbC2Dfvn02rZSycHGxPrZ+/XUSqammi8d+/fpx4sQJ3nvvPcaNG2eVM+Zec8c7G1XaWEShXnI13XI4pSTfmEGuTEUvi68Ea3vWpplfs+LV7IkYzYSHm0Z/qVQKzIp2gHfeeYfr1687PHnl5OQQFBTEzz//7DDgbNu2jWPHjvHmm286rcylYd4fvV7P0KFDmTx5stL3VharV68mNjbWbv+TSjE3b95k7Nixpd4hMGM+TxmN+Rw4YJtCOzBwLXXqDEMIN0Cg0Vj3G/38888MHz6cJUuWMGLEiLvu51GljU6mwCaIQIOakGm8aBVEGng3oJ5XPXILc8nOvYNMSnIcRFq0UINIJePrr79WJpUePHjQbmsITJ3VgYGBzJw5s9RWy9ChQ2nWrJmTSls2CxYsUPanefPmypBVlQfHpUuX+Nvf/nZXnxHCFQ+PVjbvJyZO4OBBbw4ccOPAgWpcv77aavmnn37KhQsXGDVqFH369LF7a/pR8ZttkRilJCPLyJ1c2/1v4K8hKf0U+YaiURMSPPTgVQDehaa/HvZamLVrQ9OmagBRUVGxwfI8JaWBjIz9JCU9R25ust31PTxa06VLkvLaYDCwYsUK5s+fT1paGi4uLsyYMYMFCxbY3Ba0h9oiKQflDYhGKbmWrudSisEqiHhVF9T11dC0jgvVq2nwq+ZD8A2IuAYR10GbAs10UDvHFEQspyQaWrZA3zEEfZMA9NKA3qi/u4dBT6Gh0LawKioqVYKS5ychXPD370OXLkk0b/623c8Yjbns3y/Yv19w7dqnuLi4MG3aNJKSkpg+fTpSSj788EPatm3L/v37H8JeOKZK2H/d3d1JS0ujVq1adu8bGo0SXY6RfD0UFEqMFr9pdVeo5+eCRmPxueRkGtvJwZzmAdnVINsNcqqBNH8k9xzk2qx+T2jraPGodndDCVVUVCouUkrS0tJsRnuZcXGxP0/MMvlVUtKzJCU9C0CjRjP5+9//ztSpU5k5cyZxcXG0adPmwRf8LqgSt7YKCwu5cuWKzWghKSEz10hhCSWRpsiA4uMhcNGUCDw6nX3lO3DRzu1wjSh/o84oy+cl8qzmeAKiRGKURqQ0/TU/HOHu6k5dr7r3PAFLRUXl/nF3dycgIIBq1ao5XCcn5wwFBddIT9+Jt3dHkpKmotfbPxfVrDkQL68OgIbk5OvUrn2CWrUGo9HU5oMPvmXixF7Urm09Nygg4Hmn3dqqEoGkJNl5Rt77byZX0myldq+MrEHTOg4aYvv3Q+/ejjfctKm1VDEnB65eLUepTTT5I1wu+1amU3CpxDkPDNK5csLft/k928fYurJUVCoCFy68yYUL5cvt/sUX8OGHpmwVkybBkCHFYsjevamcgUQIMQD4EHAB/iWlfKfEclG0fCCQA0yUUtrOQLOgrEByLd3A6xtsb0vZo1b1Ql4d6IqXPsfkc968GcpQQt8PN3Zt5W39PmpUr0E9r3rU8aqDm0v5bL9SSkZsHuG0sv3W+UP4Hx7K9xilEb1Rj0EaMBgNGIr61MzPnfGe5fdJ7v3/vVfTXmwfs50a1e3PjVJxHllZ8Vy+/C7VqzehWrVaSGlASgNgIC/vAjdvrqdmzYFcverGW2/9wMGDpnTgbdrU5JVXutO48fbKGUiEKe1XEtAPuAIcBcZIKRMs1hkIzMQUSLoAH0opu5S23Q4BzeX6p16gsNBIfoGkQG+koNBIQaGksOgvUiKQiCJnlpBGBJIBu5fhnX3bKfurogKwsH8DDrTwLnvFSoyfu/+jLoJKKUgpuX4ug/j9l8i5Yxp52rC1N9eSsyplIOkKLJBS9i96/RKAlPKvFut8CuyXUn5e9PpXIEpKaWf6uIkIIWTpN7ZUVFRUVPKA94C3i55LnNciceaorUbAZYvXVzC1OspapxFgFUiEENOAaUUv8wWcfLBFrbTUBlIfdSEqCGpdFKPWRTFqXRTT1lkbdmYgsTdMqGTzpzzrIKVcDiwHEELEOiuqVjbUuihGrYti1LooRq2LYoQQTruZ48wJiVeAxhavA4CS0v7yrKOioqKiUoFxZiA5CrQWQjQXJgvZk0DJMZbbgQnCRCSgK61/REVFRUWl4uG0W1tSSr0QYgawE9Pw35VSylNCiGeLln8CfINpxNYZTMN/7Tu5rVle9iq/GdS6KEati2LUuihGrYtinFYXlW5CooqKiopKxaLKSBtVVFRUVB4NaiBRUVFRUbkvKmwgEUIMEEL8KoQ4I4SYZ2e5EEIsK1oeL4QIexTlfBiUoy7GFdVBvBDiRyFEyKMo58OgrLqwWK+TEMIghKiyXpny1IUQIkoIESeEOCWE+P5hl/FhUY7/EV8hxJdCiBNFdVGe/thKhxBipRDilhDC7lw7p503pZQV7oGpc/4s0AJwA04A7UusMxD4FtNclEjgf4+63I+wLroB/kXPH/8t14XFensxDeYY8ajL/QiPCz8gAWhS9Lruoy73I6yL+cDioud1gHTA7VGX3Ql10RMIA046WO6U82ZFbZF0Bs5IKc9JKQuADcATJdZ5AlgrTfwE+AkhGjzsgj4EyqwLKeWPUkqzROwnTPNxqiLlOS7A5G/bAtx6mIV7yJSnLsYCW6WUlwCklFW1PspTFxLwKRLFemMKJPbynFZqpJQHMO2bI5xy3qyogcSROuVu16kK3O1+PoPpiqMqUmZdCCEaAUOBTx5iuR4F5Tku2gD+Qoj9QohjQogJD610D5fy1MXHQDtME55/AWZLWc4EQVULp5w3K2qGxAemV6kClHs/hRC9MQWS7k4t0aOjPHXxATBXSmmo4sm8ylMXrkA40AfwAA4LIX6SUibZfLJyU5666A/EAY8BLYHdQoiDUso7zi5cBcMp582KGkhUvUox5dpPIUQw8C/gcSll2kMq28OmPHURAWwoCiK1gYFCCL2U8ouHU8SHRnn/R1KllNlAthDiABCCKb1DVaI8dTEJeEeaOgrOCCHOA4HAkYdTxAqDU86bFfXWlqpXKabMuhBCNAG2AuOr4NWmJWXWhZSyuZSymZSyGfAfYHoVDCJQvv+R/wI9hBCuQghPTPbt0w+5nA+D8tTFJUwtM4QQ9TCZcM891FJWDJxy3qyQLRLpPL1KpaOcdfEaUAv4R9GVuF5WQeNpOeviN0F56kJKeVoIsQOIB4yYspRWuRQM5TwuFgGrhRC/YLq9M1dKWeX08kKIz4EooLYQ4grwOlANnHveVBUpKioqKir3RUW9taWioqKiUklQA4mKioqKyn2hBhIVFRUVlftCDSQqKioqKveFGkhUVFRUVO4LNZCoqNihyBwcJ4Q4WWSN9XvA278ghKhd9DzrQW5bReVhowYSFRX75EopQ6WUHTBJ8J5/1AVSUamoqIFERaVsDlMkthNCtBRC7CiSIB4UQgQWvV9PCLGtKN/FCSFEt6L3vyha95QQYtoj3AcVFadRIWe2q6hUFIQQLpjUGiuK3loOPCulTBZCdAH+gUkEuAz4Xko5tOgz3kXrT5ZSpgshPICjQogtVdiFpvIbRQ0kKir28RBCxAHNgGOYbLHemJKIbbYwC1cv+vsYMAFASmkAdEXvzxJCDC163hhoDaiBRKVKoQYSFRX75EopQ4UQvsBXmPpIVgMZUsrQ8mxACBEF9AW6SilzhBD7AXfnFFdF5dGh9pGoqJSClFIHzAL+AuQC54UQI0HJfx1StOp3wHNF77sIIWoAvsDtoiASiCm1qYpKlUMNJCoqZSClPI4pD/iTwDjgGSHECeAUxSldZwO9i+yyxwAtsANwFULEY7LP/vSwy66i8jBQ7b8qKioqKveF2iJRUVFRUbkv1ECioqKionJfqIFERVDc9mMAAAAwSURBVEVFReW+UAOJioqKisp9oQYSFRUVFZX7Qg0kKioqKir3hRpIVFRUVFTui/8Hc4EXO+9jnBsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "key_name=\"test_baseline\"\n",
    "recall=recalls[key_name]\n",
    "precision=precisions[key_name]\n",
    "average_precision=average_precisions[key_name]\n",
    "dataname=key_name\n",
    "plot_pr(recall, precision, average_precision, dataname,modelname,choose = \"all\")\n",
    "plot_pr(recall, precision, average_precision, dataname,modelname,choose = \"micro\")\n",
    "plot_pr(recall, precision, average_precision, dataname,modelname,choose = \"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. if just want to directly load the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = {\n",
    "    \"model_all\":\"LogisticRegression_baseline_overall\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_data = open_pkl(model_names)\n",
    "classifiers = model_data[\"model_all\"]"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}