BiGRU_self-attention.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",
    "import tensorflow as tf\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "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": [
    "# data with suffix \"_word2vec\" means this data is transformed based on pretrained word2vec lexicon.\n",
    "# data with suffix \"_glove\" means this data is transformed based on pretrained glove lexicon.\n",
    "file_name0={\n",
    "    #\"train0\":\"vec_train_a_no_cor_word2vec\",# data without correction of misspell and shorthand and emoji, emoticon\n",
    "    #\"train1\":\"vec_train_a_no_emo_word2vec\",# data without emoji and emoticon\n",
    "    \"train2\":\"vec_train_a_emo_word2vec\",# data with emoji and emoticon\n",
    "    #\"train3\":\"vec_train_a_no_cor_glove\",# data without correction of misspell and shorthand and emoji, emoticon\n",
    "    #\"train4\":\"vec_train_a_no_emo_glove\",# data without emoji and emoticon\n",
    "    \"train5\":\"vec_train_a_emo_glove\"# data with emoji and emoticon\n",
    "    }\n",
    "file_name1={\n",
    "    #\"test0\":\"vec_test_a_no_cor_word2vec\",\n",
    "    #\"test1\":\"vec_test_a_no_emo_word2vec\",\n",
    "    \"test2\":\"vec_test_a_emo_word2vec\",\n",
    "    #\"test3\":\"vec_test_a_no_cor_glove\",\n",
    "    #\"test4\":\"vec_test_a_no_emo_glove\",\n",
    "    \"test5\":\"vec_test_a_emo_glove\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import data\n",
    "def open_pkl_data(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]=np.array(data)\n",
    "    return all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = open_pkl_data(file_name0)\n",
    "test_data = open_pkl_data(file_name1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([array([[ 0.24609375,  0.02563477,  0.03881836, ..., -0.12695312,\n",
       "         0.04223633, -0.16503906],\n",
       "       [ 0.16992188, -0.19042969,  0.23828125, ..., -0.21777344,\n",
       "         0.09765625, -0.26171875],\n",
       "       [ 0.21777344,  0.07714844,  0.03833008, ..., -0.54296875,\n",
       "         0.35546875, -0.0246582 ],\n",
       "       ...,\n",
       "       [ 0.02114868, -0.21972656,  0.515625  , ...,  0.03369141,\n",
       "         0.4921875 ,  0.32080078],\n",
       "       [ 0.5830078 ,  0.29882812,  0.2475586 , ..., -0.5214844 ,\n",
       "        -0.47753906,  0.5390625 ],\n",
       "       [ 0.4453125 ,  0.45703125,  0.20800781, ..., -0.4658203 ,\n",
       "        -0.8847656 , -0.45703125]], dtype=float32)], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"train2\"][:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.Preprocessing for padding the sentence\n",
    "- because each sentence is different on length, we need to extend those shorter sentence to the same length for further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# increase the length of each text in each row till it matches the max length of all the texts\n",
    "def getlen_max(input_data):\n",
    "    'function for estimating the max length of text in data'\n",
    "    max_no = 0\n",
    "    for text in input_data:\n",
    "        num_word = len(text)\n",
    "        if max_no <= num_word:\n",
    "            max_no = num_word\n",
    "\n",
    "    return max_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_length(input_dict1,input_dict2):\n",
    "    max_no = 0\n",
    "    for key in input_dict1.keys():\n",
    "        comparison = getlen_max(input_dict1[key])\n",
    "        if max_no < comparison:\n",
    "            max_no = comparison\n",
    "    for key in input_dict2.keys():\n",
    "        comparison = getlen_max(input_dict2[key])\n",
    "        if max_no < comparison:\n",
    "            max_no = comparison\n",
    "    return max_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "173\n"
     ]
    }
   ],
   "source": [
    "# coz it doesn't matter how long we set the sentence, so I use the max length directly regardless of from which data resource\n",
    "len_max = compare_length(train_data,test_data)\n",
    "print(len_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extend_text(input_data, len_max,padding=\"pre\",dimension=300):\n",
    "    content=list()\n",
    "    if padding == \"post\":\n",
    "        for text in tqdm(input_data):\n",
    "            add_no = len_max - len(text)\n",
    "            x = np.zeros((add_no,dimension))\n",
    "            new_text = np.vstack((text,x))#padding is post\n",
    "            content.append(new_text)\n",
    "            \n",
    "    elif padding == \"pre\":\n",
    "        for text in tqdm(input_data):\n",
    "            add_no = len_max - len(text)\n",
    "            x = np.zeros((add_no,dimension))\n",
    "            new_text = np.vstack((x,text))#padding is pre\n",
    "            content.append(new_text)\n",
    "\n",
    "    content = np.array(content)\n",
    "    return content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "[[[0. 0. 0.]\n",
      "  [0. 0. 0.]\n",
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      "  ...\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "test = [[[1,2,3],[3,4,5]],[[1,2,3],[4,5,6]]]\n",
    "test = np.array(test)\n",
    "test = extend_text(test,len_max,dimension=3)\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# because of memory error, I have to disclose the for loop of all dict. Instead padding the text of data one by one\n",
    "X_train=dict()\n",
    "X_test=dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 30160/30160 [00:06<00:00, 4516.47it/s]\n"
     ]
    }
   ],
   "source": [
    "key_name = \"train2\"\n",
    "X_train[key_name] = extend_text(train_data[key_name],len_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "key_name = \"train5\"\n",
    "X_train[key_name] = extend_text(train_data[key_name],len_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "key_name = \"test2\"\n",
    "X_test[key_name] = extend_text(test_data[key_name],len_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "key_name = \"test5\"\n",
    "X_test[key_name] = extend_text(test_data[key_name],len_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Preprocessing of y\n",
    "- if we use 1 hot representation, we must change y again\n",
    "- but it only depends on with loss function we apply on our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
    "emotion = {\"others\":0,\"happy\":1,\"sad\":2,\"angry\":3}\n",
    "y_test1hot = tf.keras.utils.to_categorical(y_test,num_classes=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.build BiGRU Model with self-attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1. initialize the model and add layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import keras\n",
    "from keras.layers import GRU\n",
    "from keras.layers import MaxPooling1D\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Bidirectional\n",
    "from keras_self_attention import SeqSelfAttention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(173, 300)\n"
     ]
    }
   ],
   "source": [
    "# firstly we need to make sure our input share the same format\n",
    "input_shape = X_train[\"train2\"][0].shape\n",
    "print(input_shape) # this means 300 length vectors with 162 timesteps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_build():\n",
    "    # initialize RNN model\n",
    "    model = keras.Sequential()\n",
    "    \n",
    "    # add 1st BiLSTM layer, I need to set the input_shape directly, \n",
    "    # which should be 2 dimensions, one for the timesteps, one for the indicators inside\n",
    "    # because sentence can be complex, here I set the LSTM cells number as 50\n",
    "    forward_layer = GRU(units = 50, activation=\"tanh\",dropout=0.2, recurrent_activation=\"sigmoid\", return_sequences = True)\n",
    "    model.add(Bidirectional(layer = forward_layer,merge_mode=\"concat\",input_shape = input_shape))\n",
    "    \n",
    "    # add self-attention layer for apply different weights to add attention focus on the different parts of conversation\n",
    "    model.add(SeqSelfAttention(attention_activation='sigmoid'))\n",
    "    \n",
    "    forward_layer2 = GRU(units = 50, activation=\"tanh\",dropout=0.2, recurrent_activation=\"sigmoid\")\n",
    "    # add 2nd LSTM layer\n",
    "    model.add(Bidirectional(layer = forward_layer2))\n",
    "    #model.add(Dropout(0.2))\n",
    "   \n",
    "    # add output layer, since the data has 4 different categories as label, I set the final neurons number as 4\n",
    "    # and sicne I need the possibilities for each category, I set the final activation function as \"sigmoid\"\n",
    "    model.add(Dense(units=4, activation='sigmoid'))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2. Compilation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigru_model = model_build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compilation\n",
    "# we use stochastic gradient descent as our optimizer, cross entropy as our loss function\n",
    "# it's because I use integer category as label, so I have to use sparse_categorical_crossentropy as my loss function\n",
    "bigru_model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bidirectional (Bidirectional (None, 173, 100)          105600    \n",
      "_________________________________________________________________\n",
      "seq_self_attention (SeqSelfA (None, None, 100)         6465      \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 100)               45600     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 4)                 404       \n",
      "=================================================================\n",
      "Total params: 158,069\n",
      "Trainable params: 158,069\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "bigru_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3. Save Model Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# global parameter\n",
    "modelname=\"BiGRU_self-attention\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_model(name,model,modelname):\n",
    "    model_json=model.to_json()\n",
    "    # specialize model to json\n",
    "    name_path = \"DataSet\\\\{a}_{b}\".format(a=modelname,b=name)\n",
    "    with open(name_path+\".json\",\"w\") as json_file:\n",
    "        json_file.write(model_json)\n",
    "        \n",
    "    # sepcialize weights to HDF5\n",
    "    model.save_weights(name_path+\".h5\")\n",
    "    print(\"Save model to DataSet archive successfully\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.train BiGRU Model with self-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "history=dict() # to store the history of the model training\n",
    "epochs = 100\n",
    "batch = 100\n",
    "vali_split=0.1\n",
    "# though validation_data is not used in propagation, the model will be gradually familier with validation data. There can be information leak.\n",
    "# therefore, normally use validation_split for testing model to avoid overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "272/272 [==============================] - 408s 2s/step - loss: 0.6943 - accuracy: 0.7254 - val_loss: 0.3908 - val_accuracy: 0.8637\n",
      "Epoch 2/100\n",
      "272/272 [==============================] - 414s 2s/step - loss: 0.3595 - accuracy: 0.8765 - val_loss: 0.3453 - val_accuracy: 0.8830\n",
      "Epoch 3/100\n",
      "272/272 [==============================] - 438s 2s/step - loss: 0.3131 - accuracy: 0.8906 - val_loss: 0.3164 - val_accuracy: 0.8916\n",
      "Epoch 4/100\n",
      "272/272 [==============================] - 448s 2s/step - loss: 0.2841 - accuracy: 0.8989 - val_loss: 0.3059 - val_accuracy: 0.8975\n",
      "Epoch 5/100\n",
      "272/272 [==============================] - 441s 2s/step - loss: 0.2659 - accuracy: 0.9057 - val_loss: 0.2977 - val_accuracy: 0.8999\n",
      "Epoch 6/100\n",
      "272/272 [==============================] - 546s 2s/step - loss: 0.2467 - accuracy: 0.9139 - val_loss: 0.2958 - val_accuracy: 0.9002\n",
      "Epoch 7/100\n",
      "272/272 [==============================] - 550s 2s/step - loss: 0.2320 - accuracy: 0.9187 - val_loss: 0.3103 - val_accuracy: 0.8969\n",
      "Epoch 8/100\n",
      "272/272 [==============================] - 553s 2s/step - loss: 0.2208 - accuracy: 0.9231 - val_loss: 0.2855 - val_accuracy: 0.9092\n",
      "Epoch 9/100\n",
      "272/272 [==============================] - 551s 2s/step - loss: 0.2106 - accuracy: 0.9272 - val_loss: 0.2855 - val_accuracy: 0.9029\n",
      "Epoch 10/100\n",
      "272/272 [==============================] - 558s 2s/step - loss: 0.1977 - accuracy: 0.9322 - val_loss: 0.2853 - val_accuracy: 0.9085\n",
      "Epoch 11/100\n",
      "272/272 [==============================] - 557s 2s/step - loss: 0.1900 - accuracy: 0.9341 - val_loss: 0.2819 - val_accuracy: 0.9075\n",
      "Epoch 12/100\n",
      "272/272 [==============================] - 555s 2s/step - loss: 0.1776 - accuracy: 0.9397 - val_loss: 0.2902 - val_accuracy: 0.9092\n",
      "Epoch 13/100\n",
      "272/272 [==============================] - 556s 2s/step - loss: 0.1737 - accuracy: 0.9385 - val_loss: 0.3027 - val_accuracy: 0.9048\n",
      "Epoch 14/100\n",
      "272/272 [==============================] - 554s 2s/step - loss: 0.1617 - accuracy: 0.9462 - val_loss: 0.2947 - val_accuracy: 0.9101\n",
      "Epoch 15/100\n",
      "272/272 [==============================] - 550s 2s/step - loss: 0.1520 - accuracy: 0.9482 - val_loss: 0.2953 - val_accuracy: 0.9072\n",
      "Epoch 16/100\n",
      "272/272 [==============================] - 569s 2s/step - loss: 0.1470 - accuracy: 0.9505 - val_loss: 0.3129 - val_accuracy: 0.9012\n",
      "Epoch 17/100\n",
      "272/272 [==============================] - 558s 2s/step - loss: 0.1420 - accuracy: 0.9517 - val_loss: 0.3125 - val_accuracy: 0.9029\n",
      "Epoch 18/100\n",
      "272/272 [==============================] - 562s 2s/step - loss: 0.1359 - accuracy: 0.9557 - val_loss: 0.3247 - val_accuracy: 0.9078\n",
      "Epoch 19/100\n",
      "272/272 [==============================] - 570s 2s/step - loss: 0.1280 - accuracy: 0.9568 - val_loss: 0.3069 - val_accuracy: 0.9105\n",
      "Epoch 20/100\n",
      "272/272 [==============================] - 576s 2s/step - loss: 0.1227 - accuracy: 0.9579 - val_loss: 0.3111 - val_accuracy: 0.9095\n",
      "Epoch 21/100\n",
      "272/272 [==============================] - 576s 2s/step - loss: 0.1204 - accuracy: 0.9594 - val_loss: 0.3352 - val_accuracy: 0.9045\n",
      "Epoch 22/100\n",
      "272/272 [==============================] - 575s 2s/step - loss: 0.1161 - accuracy: 0.9611 - val_loss: 0.3291 - val_accuracy: 0.9052\n",
      "Epoch 23/100\n",
      "272/272 [==============================] - 576s 2s/step - loss: 0.1079 - accuracy: 0.9636 - val_loss: 0.3436 - val_accuracy: 0.9052\n",
      "Epoch 24/100\n",
      "272/272 [==============================] - 576s 2s/step - loss: 0.1052 - accuracy: 0.9643 - val_loss: 0.3381 - val_accuracy: 0.9042\n",
      "Epoch 25/100\n",
      "272/272 [==============================] - 582s 2s/step - loss: 0.1067 - accuracy: 0.9637 - val_loss: 0.3584 - val_accuracy: 0.8949\n",
      "Epoch 26/100\n",
      "272/272 [==============================] - 581s 2s/step - loss: 0.1002 - accuracy: 0.9659 - val_loss: 0.3399 - val_accuracy: 0.9025\n",
      "Epoch 27/100\n",
      "272/272 [==============================] - 581s 2s/step - loss: 0.0973 - accuracy: 0.9674 - val_loss: 0.3696 - val_accuracy: 0.8999\n",
      "Epoch 28/100\n",
      "272/272 [==============================] - 575s 2s/step - loss: 0.0921 - accuracy: 0.9685 - val_loss: 0.3798 - val_accuracy: 0.8989\n",
      "Epoch 29/100\n",
      "272/272 [==============================] - 575s 2s/step - loss: 0.0904 - accuracy: 0.9695 - val_loss: 0.3598 - val_accuracy: 0.9002\n",
      "Epoch 30/100\n",
      "272/272 [==============================] - 583s 2s/step - loss: 0.0886 - accuracy: 0.9707 - val_loss: 0.3608 - val_accuracy: 0.8975\n",
      "Epoch 31/100\n",
      "272/272 [==============================] - 581s 2s/step - loss: 0.0844 - accuracy: 0.9708 - val_loss: 0.3805 - val_accuracy: 0.9005\n",
      "Epoch 32/100\n",
      "272/272 [==============================] - 578s 2s/step - loss: 0.0817 - accuracy: 0.9730 - val_loss: 0.3796 - val_accuracy: 0.9045\n",
      "Epoch 33/100\n",
      "272/272 [==============================] - 579s 2s/step - loss: 0.0791 - accuracy: 0.9735 - val_loss: 0.3722 - val_accuracy: 0.9065\n",
      "Epoch 34/100\n",
      "272/272 [==============================] - 580s 2s/step - loss: 0.0747 - accuracy: 0.9744 - val_loss: 0.3822 - val_accuracy: 0.8989\n",
      "Epoch 35/100\n",
      "272/272 [==============================] - 573s 2s/step - loss: 0.0704 - accuracy: 0.9764 - val_loss: 0.4153 - val_accuracy: 0.8959\n",
      "Epoch 36/100\n",
      "272/272 [==============================] - 573s 2s/step - loss: 0.0704 - accuracy: 0.9763 - val_loss: 0.4175 - val_accuracy: 0.9035\n",
      "Epoch 37/100\n",
      "272/272 [==============================] - 573s 2s/step - loss: 0.0703 - accuracy: 0.9757 - val_loss: 0.3985 - val_accuracy: 0.8979\n",
      "Epoch 38/100\n",
      "272/272 [==============================] - 574s 2s/step - loss: 0.0671 - accuracy: 0.9762 - val_loss: 0.3816 - val_accuracy: 0.9025\n",
      "Epoch 39/100\n",
      "272/272 [==============================] - 575s 2s/step - loss: 0.0629 - accuracy: 0.9783 - val_loss: 0.4321 - val_accuracy: 0.8972\n",
      "Epoch 40/100\n",
      "272/272 [==============================] - 572s 2s/step - loss: 0.0647 - accuracy: 0.9785 - val_loss: 0.4259 - val_accuracy: 0.8956\n",
      "Epoch 41/100\n",
      "272/272 [==============================] - 573s 2s/step - loss: 0.0607 - accuracy: 0.9786 - val_loss: 0.4166 - val_accuracy: 0.8995\n",
      "Epoch 42/100\n",
      "272/272 [==============================] - 572s 2s/step - loss: 0.0598 - accuracy: 0.9788 - val_loss: 0.4160 - val_accuracy: 0.9058\n",
      "Epoch 43/100\n",
      "272/272 [==============================] - 582s 2s/step - loss: 0.0561 - accuracy: 0.9805 - val_loss: 0.4229 - val_accuracy: 0.9068\n",
      "Epoch 44/100\n",
      "272/272 [==============================] - 582s 2s/step - loss: 0.0552 - accuracy: 0.9805 - val_loss: 0.4330 - val_accuracy: 0.9025\n",
      "Epoch 45/100\n",
      "272/272 [==============================] - 577s 2s/step - loss: 0.0562 - accuracy: 0.9801 - val_loss: 0.4199 - val_accuracy: 0.9022\n",
      "Epoch 46/100\n",
      "272/272 [==============================] - 573s 2s/step - loss: 0.0508 - accuracy: 0.9819 - val_loss: 0.4525 - val_accuracy: 0.9012\n",
      "Epoch 47/100\n",
      "272/272 [==============================] - 580s 2s/step - loss: 0.0518 - accuracy: 0.9816 - val_loss: 0.4458 - val_accuracy: 0.9022\n",
      "Epoch 48/100\n",
      "272/272 [==============================] - 578s 2s/step - loss: 0.0541 - accuracy: 0.9803 - val_loss: 0.4434 - val_accuracy: 0.8959\n",
      "Epoch 49/100\n",
      "272/272 [==============================] - 576s 2s/step - loss: 0.0446 - accuracy: 0.9842 - val_loss: 0.4665 - val_accuracy: 0.9029\n",
      "Epoch 50/100\n",
      "272/272 [==============================] - 580s 2s/step - loss: 0.0469 - accuracy: 0.9835 - val_loss: 0.4567 - val_accuracy: 0.9062\n",
      "Epoch 51/100\n",
      "272/272 [==============================] - 578s 2s/step - loss: 0.0477 - accuracy: 0.9832 - val_loss: 0.4487 - val_accuracy: 0.9019\n",
      "Epoch 52/100\n",
      "272/272 [==============================] - 580s 2s/step - loss: 0.0503 - accuracy: 0.9829 - val_loss: 0.4326 - val_accuracy: 0.9012\n",
      "Epoch 53/100\n",
      "272/272 [==============================] - 576s 2s/step - loss: 0.0455 - accuracy: 0.9832 - val_loss: 0.4580 - val_accuracy: 0.8985\n",
      "Epoch 54/100\n",
      "272/272 [==============================] - 582s 2s/step - loss: 0.0447 - accuracy: 0.9841 - val_loss: 0.4709 - val_accuracy: 0.8939\n",
      "Epoch 55/100\n",
      "272/272 [==============================] - 585s 2s/step - loss: 0.0453 - accuracy: 0.9834 - val_loss: 0.4514 - val_accuracy: 0.8992\n",
      "Epoch 56/100\n",
      "272/272 [==============================] - 583s 2s/step - loss: 0.0417 - accuracy: 0.9855 - val_loss: 0.4630 - val_accuracy: 0.9002\n",
      "Epoch 57/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "272/272 [==============================] - 587s 2s/step - loss: 0.0436 - accuracy: 0.9840 - val_loss: 0.4582 - val_accuracy: 0.9015\n",
      "Epoch 58/100\n",
      "272/272 [==============================] - 586s 2s/step - loss: 0.0398 - accuracy: 0.9852 - val_loss: 0.4861 - val_accuracy: 0.8982\n",
      "Epoch 59/100\n",
      "272/272 [==============================] - 635s 2s/step - loss: 0.0405 - accuracy: 0.9856 - val_loss: 0.4695 - val_accuracy: 0.9025\n",
      "Epoch 60/100\n",
      "272/272 [==============================] - 604s 2s/step - loss: 0.0383 - accuracy: 0.9864 - val_loss: 0.5021 - val_accuracy: 0.8956\n",
      "Epoch 61/100\n",
      "272/272 [==============================] - 620s 2s/step - loss: 0.0419 - accuracy: 0.9846 - val_loss: 0.4604 - val_accuracy: 0.8995\n",
      "Epoch 62/100\n",
      "272/272 [==============================] - 602s 2s/step - loss: 0.0377 - accuracy: 0.9864 - val_loss: 0.4924 - val_accuracy: 0.9015\n",
      "Epoch 63/100\n",
      "272/272 [==============================] - 610s 2s/step - loss: 0.0383 - accuracy: 0.9871 - val_loss: 0.5120 - val_accuracy: 0.8929\n",
      "Epoch 64/100\n",
      "272/272 [==============================] - 610s 2s/step - loss: 0.0398 - accuracy: 0.9854 - val_loss: 0.5104 - val_accuracy: 0.8995\n",
      "Epoch 65/100\n",
      "272/272 [==============================] - 629s 2s/step - loss: 0.1522 - accuracy: 0.9211 - val_loss: 0.4345 - val_accuracy: 0.8186\n",
      "Epoch 66/100\n",
      "272/272 [==============================] - 620s 2s/step - loss: 0.1072 - accuracy: 0.9557 - val_loss: 0.4161 - val_accuracy: 0.8972\n",
      "Epoch 67/100\n",
      "272/272 [==============================] - 615s 2s/step - loss: 0.1109 - accuracy: 0.9612 - val_loss: 0.3702 - val_accuracy: 0.8999\n",
      "Epoch 68/100\n",
      "272/272 [==============================] - 623s 2s/step - loss: 0.0853 - accuracy: 0.9698 - val_loss: 0.3866 - val_accuracy: 0.9015\n",
      "Epoch 69/100\n",
      "272/272 [==============================] - 625s 2s/step - loss: 0.0721 - accuracy: 0.9740 - val_loss: 0.4027 - val_accuracy: 0.9029\n",
      "Epoch 70/100\n",
      "272/272 [==============================] - 611s 2s/step - loss: 0.0626 - accuracy: 0.9776 - val_loss: 0.4146 - val_accuracy: 0.8995\n",
      "Epoch 71/100\n",
      "272/272 [==============================] - 633s 2s/step - loss: 0.0557 - accuracy: 0.9806 - val_loss: 0.4260 - val_accuracy: 0.9038\n",
      "Epoch 72/100\n",
      "272/272 [==============================] - 618s 2s/step - loss: 0.0484 - accuracy: 0.9824 - val_loss: 0.4520 - val_accuracy: 0.9015\n",
      "Epoch 73/100\n",
      "272/272 [==============================] - 615s 2s/step - loss: 0.0476 - accuracy: 0.9833 - val_loss: 0.4618 - val_accuracy: 0.8966\n",
      "Epoch 74/100\n",
      "272/272 [==============================] - 625s 2s/step - loss: 0.0421 - accuracy: 0.9849 - val_loss: 0.4573 - val_accuracy: 0.9009\n",
      "Epoch 75/100\n",
      "272/272 [==============================] - 621s 2s/step - loss: 0.0413 - accuracy: 0.9851 - val_loss: 0.4622 - val_accuracy: 0.8985\n",
      "Epoch 76/100\n",
      "272/272 [==============================] - 625s 2s/step - loss: 0.0389 - accuracy: 0.9854 - val_loss: 0.4772 - val_accuracy: 0.8982\n",
      "Epoch 77/100\n",
      "272/272 [==============================] - 616s 2s/step - loss: 0.0362 - accuracy: 0.9869 - val_loss: 0.4911 - val_accuracy: 0.8989\n",
      "Epoch 78/100\n",
      "272/272 [==============================] - 622s 2s/step - loss: 0.0372 - accuracy: 0.9864 - val_loss: 0.4820 - val_accuracy: 0.8985\n",
      "Epoch 79/100\n",
      "272/272 [==============================] - 626s 2s/step - loss: 0.0362 - accuracy: 0.9869 - val_loss: 0.5003 - val_accuracy: 0.8989\n",
      "Epoch 80/100\n",
      "272/272 [==============================] - 628s 2s/step - loss: 0.0333 - accuracy: 0.9878 - val_loss: 0.5019 - val_accuracy: 0.8985\n",
      "Epoch 81/100\n",
      "272/272 [==============================] - 629s 2s/step - loss: 0.0328 - accuracy: 0.9879 - val_loss: 0.5160 - val_accuracy: 0.8972\n",
      "Epoch 82/100\n",
      "272/272 [==============================] - 627s 2s/step - loss: 0.0314 - accuracy: 0.9882 - val_loss: 0.5021 - val_accuracy: 0.8999\n",
      "Epoch 83/100\n",
      "272/272 [==============================] - 635s 2s/step - loss: 0.0313 - accuracy: 0.9888 - val_loss: 0.5096 - val_accuracy: 0.8992\n",
      "Epoch 84/100\n",
      "272/272 [==============================] - 632s 2s/step - loss: 0.0328 - accuracy: 0.9877 - val_loss: 0.5056 - val_accuracy: 0.9062\n",
      "Epoch 85/100\n",
      "272/272 [==============================] - 630s 2s/step - loss: 0.0304 - accuracy: 0.9891 - val_loss: 0.5195 - val_accuracy: 0.9005\n",
      "Epoch 86/100\n",
      "272/272 [==============================] - 632s 2s/step - loss: 0.0311 - accuracy: 0.9880 - val_loss: 0.5211 - val_accuracy: 0.8999\n",
      "Epoch 87/100\n",
      "272/272 [==============================] - 621s 2s/step - loss: 0.0305 - accuracy: 0.9887 - val_loss: 0.5212 - val_accuracy: 0.9002\n",
      "Epoch 88/100\n",
      "272/272 [==============================] - 637s 2s/step - loss: 0.0292 - accuracy: 0.9892 - val_loss: 0.5249 - val_accuracy: 0.8982\n",
      "Epoch 89/100\n",
      "272/272 [==============================] - 619s 2s/step - loss: 0.0288 - accuracy: 0.9890 - val_loss: 0.5365 - val_accuracy: 0.8989\n",
      "Epoch 90/100\n",
      "272/272 [==============================] - 622s 2s/step - loss: 0.0326 - accuracy: 0.9876 - val_loss: 0.5308 - val_accuracy: 0.8962\n",
      "Epoch 91/100\n",
      "272/272 [==============================] - 648s 2s/step - loss: 0.0320 - accuracy: 0.9884 - val_loss: 0.5205 - val_accuracy: 0.9032\n",
      "Epoch 92/100\n",
      "272/272 [==============================] - 703s 3s/step - loss: 0.0277 - accuracy: 0.9896 - val_loss: 0.5374 - val_accuracy: 0.8979\n",
      "Epoch 93/100\n",
      "272/272 [==============================] - 707s 3s/step - loss: 0.0322 - accuracy: 0.9879 - val_loss: 0.5263 - val_accuracy: 0.9019\n",
      "Epoch 94/100\n",
      "272/272 [==============================] - 727s 3s/step - loss: 0.0302 - accuracy: 0.9883 - val_loss: 0.5654 - val_accuracy: 0.8959\n",
      "Epoch 95/100\n",
      "272/272 [==============================] - 714s 3s/step - loss: 0.0312 - accuracy: 0.9886 - val_loss: 0.5638 - val_accuracy: 0.8942\n",
      "Epoch 96/100\n",
      "272/272 [==============================] - 4660s 17s/step - loss: 0.0301 - accuracy: 0.9886 - val_loss: 0.5346 - val_accuracy: 0.8985\n",
      "Epoch 97/100\n",
      "272/272 [==============================] - 639s 2s/step - loss: 0.0316 - accuracy: 0.9882 - val_loss: 0.5419 - val_accuracy: 0.8985\n",
      "Epoch 98/100\n",
      "272/272 [==============================] - 650s 2s/step - loss: 0.0277 - accuracy: 0.9895 - val_loss: 0.5291 - val_accuracy: 0.9038\n",
      "Epoch 99/100\n",
      "272/272 [==============================] - 648s 2s/step - loss: 0.0403 - accuracy: 0.9858 - val_loss: 0.5168 - val_accuracy: 0.9002\n",
      "Epoch 100/100\n",
      "272/272 [==============================] - 658s 2s/step - loss: 0.0319 - accuracy: 0.9881 - val_loss: 0.5212 - val_accuracy: 0.9038\n",
      "Save model to DataSet archive successfully\n"
     ]
    }
   ],
   "source": [
    "key_name=\"train2\"\n",
    "key_name2=\"test2\"\n",
    "history[key_name]=bigru_model.fit(x = X_train[key_name],y=y_train,validation_split=vali_split, epochs =epochs, batch_size=batch)\n",
    "save_model(key_name,bigru_model,modelname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "272/272 [==============================] - 657s 2s/step - loss: 0.9644 - accuracy: 0.6134 - val_loss: 0.5349 - val_accuracy: 0.8047\n",
      "Epoch 2/100\n",
      "272/272 [==============================] - 648s 2s/step - loss: 0.4822 - accuracy: 0.8218 - val_loss: 0.3964 - val_accuracy: 0.8528\n",
      "Epoch 3/100\n",
      "272/272 [==============================] - 650s 2s/step - loss: 0.3974 - accuracy: 0.8554 - val_loss: 0.3611 - val_accuracy: 0.8704\n",
      "Epoch 4/100\n",
      "272/272 [==============================] - 642s 2s/step - loss: 0.3525 - accuracy: 0.8747 - val_loss: 0.3517 - val_accuracy: 0.8647\n",
      "Epoch 5/100\n",
      "272/272 [==============================] - 639s 2s/step - loss: 0.3265 - accuracy: 0.8807 - val_loss: 0.3215 - val_accuracy: 0.8800\n",
      "Epoch 6/100\n",
      "272/272 [==============================] - 620s 2s/step - loss: 0.3033 - accuracy: 0.8909 - val_loss: 0.3112 - val_accuracy: 0.8889\n",
      "Epoch 7/100\n",
      "272/272 [==============================] - 618s 2s/step - loss: 0.2849 - accuracy: 0.8966 - val_loss: 0.3036 - val_accuracy: 0.8932\n",
      "Epoch 8/100\n",
      "272/272 [==============================] - 618s 2s/step - loss: 0.2763 - accuracy: 0.9001 - val_loss: 0.3142 - val_accuracy: 0.8869\n",
      "Epoch 9/100\n",
      "272/272 [==============================] - 610s 2s/step - loss: 0.2622 - accuracy: 0.9061 - val_loss: 0.3013 - val_accuracy: 0.8936\n",
      "Epoch 10/100\n",
      "272/272 [==============================] - 615s 2s/step - loss: 0.2503 - accuracy: 0.9098 - val_loss: 0.3046 - val_accuracy: 0.8932\n",
      "Epoch 11/100\n",
      "272/272 [==============================] - 625s 2s/step - loss: 0.2408 - accuracy: 0.9146 - val_loss: 0.2976 - val_accuracy: 0.8956\n",
      "Epoch 12/100\n",
      "272/272 [==============================] - 642s 2s/step - loss: 0.2292 - accuracy: 0.9171 - val_loss: 0.2909 - val_accuracy: 0.8989\n",
      "Epoch 13/100\n",
      "272/272 [==============================] - 685s 3s/step - loss: 0.2279 - accuracy: 0.9188 - val_loss: 0.2974 - val_accuracy: 0.8979\n",
      "Epoch 14/100\n",
      "272/272 [==============================] - 751s 3s/step - loss: 0.2168 - accuracy: 0.9220 - val_loss: 0.2833 - val_accuracy: 0.9052\n",
      "Epoch 15/100\n",
      "272/272 [==============================] - 710s 3s/step - loss: 0.2102 - accuracy: 0.9241 - val_loss: 0.3103 - val_accuracy: 0.8919\n",
      "Epoch 16/100\n",
      "272/272 [==============================] - 700s 3s/step - loss: 0.2059 - accuracy: 0.9263 - val_loss: 0.2984 - val_accuracy: 0.8946\n",
      "Epoch 17/100\n",
      "272/272 [==============================] - 735s 3s/step - loss: 0.1962 - accuracy: 0.9282 - val_loss: 0.2885 - val_accuracy: 0.8989\n",
      "Epoch 18/100\n",
      "272/272 [==============================] - 740s 3s/step - loss: 0.1920 - accuracy: 0.9313 - val_loss: 0.2904 - val_accuracy: 0.9062\n",
      "Epoch 19/100\n",
      "272/272 [==============================] - 667s 2s/step - loss: 0.1857 - accuracy: 0.9330 - val_loss: 0.3081 - val_accuracy: 0.8969\n",
      "Epoch 20/100\n",
      "272/272 [==============================] - 687s 3s/step - loss: 0.1816 - accuracy: 0.9338 - val_loss: 0.2961 - val_accuracy: 0.9012\n",
      "Epoch 21/100\n",
      "272/272 [==============================] - 678s 2s/step - loss: 0.1763 - accuracy: 0.9371 - val_loss: 0.3005 - val_accuracy: 0.9009\n",
      "Epoch 22/100\n",
      "272/272 [==============================] - 694s 3s/step - loss: 0.1718 - accuracy: 0.9378 - val_loss: 0.2876 - val_accuracy: 0.9038\n",
      "Epoch 23/100\n",
      "272/272 [==============================] - 640s 2s/step - loss: 0.1637 - accuracy: 0.9417 - val_loss: 0.3258 - val_accuracy: 0.8979\n",
      "Epoch 24/100\n",
      "272/272 [==============================] - 639s 2s/step - loss: 0.1596 - accuracy: 0.9420 - val_loss: 0.2917 - val_accuracy: 0.9038\n",
      "Epoch 25/100\n",
      "272/272 [==============================] - 635s 2s/step - loss: 0.1582 - accuracy: 0.9422 - val_loss: 0.3016 - val_accuracy: 0.9035\n",
      "Epoch 26/100\n",
      "272/272 [==============================] - 639s 2s/step - loss: 0.1515 - accuracy: 0.9463 - val_loss: 0.3033 - val_accuracy: 0.9038\n",
      "Epoch 27/100\n",
      "272/272 [==============================] - 641s 2s/step - loss: 0.1470 - accuracy: 0.9458 - val_loss: 0.3043 - val_accuracy: 0.9009\n",
      "Epoch 28/100\n",
      "272/272 [==============================] - 638s 2s/step - loss: 0.1464 - accuracy: 0.9457 - val_loss: 0.3231 - val_accuracy: 0.9002\n",
      "Epoch 29/100\n",
      "272/272 [==============================] - 635s 2s/step - loss: 0.1375 - accuracy: 0.9505 - val_loss: 0.3059 - val_accuracy: 0.9088\n",
      "Epoch 30/100\n",
      "272/272 [==============================] - 650s 2s/step - loss: 0.1341 - accuracy: 0.9520 - val_loss: 0.3147 - val_accuracy: 0.8985\n",
      "Epoch 31/100\n",
      "272/272 [==============================] - 651s 2s/step - loss: 0.1316 - accuracy: 0.9521 - val_loss: 0.2927 - val_accuracy: 0.9105\n",
      "Epoch 32/100\n",
      "272/272 [==============================] - 655s 2s/step - loss: 0.1301 - accuracy: 0.9518 - val_loss: 0.3119 - val_accuracy: 0.9075\n",
      "Epoch 33/100\n",
      "272/272 [==============================] - 646s 2s/step - loss: 0.1283 - accuracy: 0.9529 - val_loss: 0.3066 - val_accuracy: 0.9065\n",
      "Epoch 34/100\n",
      "272/272 [==============================] - 657s 2s/step - loss: 0.1228 - accuracy: 0.9538 - val_loss: 0.3170 - val_accuracy: 0.9025\n",
      "Epoch 35/100\n",
      "272/272 [==============================] - 654s 2s/step - loss: 0.1179 - accuracy: 0.9565 - val_loss: 0.3307 - val_accuracy: 0.9019\n",
      "Epoch 36/100\n",
      "272/272 [==============================] - 657s 2s/step - loss: 0.1146 - accuracy: 0.9580 - val_loss: 0.3300 - val_accuracy: 0.9015\n",
      "Epoch 37/100\n",
      "272/272 [==============================] - 652s 2s/step - loss: 0.1135 - accuracy: 0.9584 - val_loss: 0.3174 - val_accuracy: 0.9045\n",
      "Epoch 38/100\n",
      "272/272 [==============================] - 654s 2s/step - loss: 0.1129 - accuracy: 0.9569 - val_loss: 0.3190 - val_accuracy: 0.9005\n",
      "Epoch 39/100\n",
      "272/272 [==============================] - 638s 2s/step - loss: 0.1099 - accuracy: 0.9586 - val_loss: 0.3311 - val_accuracy: 0.9022\n",
      "Epoch 40/100\n",
      "272/272 [==============================] - 631s 2s/step - loss: 0.1037 - accuracy: 0.9608 - val_loss: 0.3431 - val_accuracy: 0.9002\n",
      "Epoch 41/100\n",
      "272/272 [==============================] - 643s 2s/step - loss: 0.1022 - accuracy: 0.9615 - val_loss: 0.3198 - val_accuracy: 0.9022\n",
      "Epoch 42/100\n",
      "272/272 [==============================] - 640s 2s/step - loss: 0.1004 - accuracy: 0.9626 - val_loss: 0.3370 - val_accuracy: 0.9015\n",
      "Epoch 43/100\n",
      "272/272 [==============================] - 590s 2s/step - loss: 0.0983 - accuracy: 0.9633 - val_loss: 0.3436 - val_accuracy: 0.9055\n",
      "Epoch 44/100\n",
      "272/272 [==============================] - 584s 2s/step - loss: 0.0950 - accuracy: 0.9649 - val_loss: 0.3688 - val_accuracy: 0.9019\n",
      "Epoch 45/100\n",
      "272/272 [==============================] - 580s 2s/step - loss: 0.0952 - accuracy: 0.9632 - val_loss: 0.3511 - val_accuracy: 0.9035\n",
      "Epoch 46/100\n",
      "272/272 [==============================] - 566s 2s/step - loss: 0.0906 - accuracy: 0.9660 - val_loss: 0.3651 - val_accuracy: 0.9009\n",
      "Epoch 47/100\n",
      "272/272 [==============================] - 576s 2s/step - loss: 0.0868 - accuracy: 0.9675 - val_loss: 0.3556 - val_accuracy: 0.8982\n",
      "Epoch 48/100\n",
      "272/272 [==============================] - 572s 2s/step - loss: 0.0883 - accuracy: 0.9663 - val_loss: 0.3852 - val_accuracy: 0.8946\n",
      "Epoch 49/100\n",
      "272/272 [==============================] - 599s 2s/step - loss: 0.0888 - accuracy: 0.9667 - val_loss: 0.3839 - val_accuracy: 0.8946\n",
      "Epoch 50/100\n",
      "272/272 [==============================] - 631s 2s/step - loss: 0.0861 - accuracy: 0.9671 - val_loss: 0.3797 - val_accuracy: 0.9025\n",
      "Epoch 51/100\n",
      "272/272 [==============================] - 625s 2s/step - loss: 0.0775 - accuracy: 0.9706 - val_loss: 0.4152 - val_accuracy: 0.8949\n",
      "Epoch 52/100\n",
      "272/272 [==============================] - 623s 2s/step - loss: 0.0816 - accuracy: 0.9686 - val_loss: 0.3839 - val_accuracy: 0.9002\n",
      "Epoch 53/100\n",
      "272/272 [==============================] - 624s 2s/step - loss: 0.0784 - accuracy: 0.9694 - val_loss: 0.4167 - val_accuracy: 0.8966\n",
      "Epoch 54/100\n",
      "272/272 [==============================] - 620s 2s/step - loss: 0.0786 - accuracy: 0.9701 - val_loss: 0.4009 - val_accuracy: 0.8999\n",
      "Epoch 55/100\n",
      "272/272 [==============================] - 622s 2s/step - loss: 0.0753 - accuracy: 0.9733 - val_loss: 0.4107 - val_accuracy: 0.8982\n",
      "Epoch 56/100\n",
      "272/272 [==============================] - 619s 2s/step - loss: 0.0791 - accuracy: 0.9705 - val_loss: 0.3885 - val_accuracy: 0.9005\n",
      "Epoch 57/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "272/272 [==============================] - 631s 2s/step - loss: 0.0689 - accuracy: 0.9736 - val_loss: 0.3943 - val_accuracy: 0.8982\n",
      "Epoch 58/100\n",
      "272/272 [==============================] - 623s 2s/step - loss: 0.0724 - accuracy: 0.9730 - val_loss: 0.4082 - val_accuracy: 0.8995\n",
      "Epoch 59/100\n",
      "272/272 [==============================] - 619s 2s/step - loss: 0.0708 - accuracy: 0.9738 - val_loss: 0.3885 - val_accuracy: 0.9005\n",
      "Epoch 60/100\n",
      "272/272 [==============================] - 618s 2s/step - loss: 0.0687 - accuracy: 0.9739 - val_loss: 0.4119 - val_accuracy: 0.8942\n",
      "Epoch 61/100\n",
      "272/272 [==============================] - 622s 2s/step - loss: 0.0697 - accuracy: 0.9733 - val_loss: 0.4029 - val_accuracy: 0.8949\n",
      "Epoch 62/100\n",
      "272/272 [==============================] - 622s 2s/step - loss: 0.0696 - accuracy: 0.9734 - val_loss: 0.4209 - val_accuracy: 0.8985\n",
      "Epoch 63/100\n",
      "272/272 [==============================] - 619s 2s/step - loss: 0.0687 - accuracy: 0.9734 - val_loss: 0.4102 - val_accuracy: 0.8972\n",
      "Epoch 64/100\n",
      "272/272 [==============================] - 619s 2s/step - loss: 0.0653 - accuracy: 0.9750 - val_loss: 0.3961 - val_accuracy: 0.8975\n",
      "Epoch 65/100\n",
      "272/272 [==============================] - 617s 2s/step - loss: 0.0618 - accuracy: 0.9765 - val_loss: 0.4400 - val_accuracy: 0.8972\n",
      "Epoch 66/100\n",
      "272/272 [==============================] - 623s 2s/step - loss: 0.0626 - accuracy: 0.9761 - val_loss: 0.4359 - val_accuracy: 0.8919\n",
      "Epoch 67/100\n",
      "272/272 [==============================] - 626s 2s/step - loss: 0.0629 - accuracy: 0.9760 - val_loss: 0.4551 - val_accuracy: 0.8946\n",
      "Epoch 68/100\n",
      "272/272 [==============================] - 621s 2s/step - loss: 0.0590 - accuracy: 0.9788 - val_loss: 0.4418 - val_accuracy: 0.8972\n",
      "Epoch 69/100\n",
      "272/272 [==============================] - 626s 2s/step - loss: 0.0601 - accuracy: 0.9770 - val_loss: 0.4242 - val_accuracy: 0.8999\n",
      "Epoch 70/100\n",
      "272/272 [==============================] - 618s 2s/step - loss: 0.0597 - accuracy: 0.9771 - val_loss: 0.4201 - val_accuracy: 0.9022\n",
      "Epoch 71/100\n",
      "272/272 [==============================] - 624s 2s/step - loss: 0.0592 - accuracy: 0.9770 - val_loss: 0.4213 - val_accuracy: 0.9055\n",
      "Epoch 72/100\n",
      "272/272 [==============================] - 628s 2s/step - loss: 0.0556 - accuracy: 0.9792 - val_loss: 0.4333 - val_accuracy: 0.9032\n",
      "Epoch 73/100\n",
      "272/272 [==============================] - 623s 2s/step - loss: 0.0565 - accuracy: 0.9789 - val_loss: 0.4350 - val_accuracy: 0.9012\n",
      "Epoch 74/100\n",
      "272/272 [==============================] - 624s 2s/step - loss: 0.0517 - accuracy: 0.9792 - val_loss: 0.4539 - val_accuracy: 0.8952\n",
      "Epoch 75/100\n",
      "272/272 [==============================] - 628s 2s/step - loss: 0.0558 - accuracy: 0.9795 - val_loss: 0.4311 - val_accuracy: 0.8979\n",
      "Epoch 76/100\n",
      "272/272 [==============================] - 627s 2s/step - loss: 0.0534 - accuracy: 0.9799 - val_loss: 0.4685 - val_accuracy: 0.8999\n",
      "Epoch 77/100\n",
      "272/272 [==============================] - 628s 2s/step - loss: 0.0559 - accuracy: 0.9787 - val_loss: 0.4367 - val_accuracy: 0.9025\n",
      "Epoch 78/100\n",
      "272/272 [==============================] - 628s 2s/step - loss: 0.0536 - accuracy: 0.9802 - val_loss: 0.4417 - val_accuracy: 0.9038\n",
      "Epoch 79/100\n",
      "272/272 [==============================] - 624s 2s/step - loss: 0.0502 - accuracy: 0.9808 - val_loss: 0.4449 - val_accuracy: 0.8975\n",
      "Epoch 80/100\n",
      "272/272 [==============================] - 629s 2s/step - loss: 0.0527 - accuracy: 0.9800 - val_loss: 0.4223 - val_accuracy: 0.9009\n",
      "Epoch 81/100\n",
      "272/272 [==============================] - 632s 2s/step - loss: 0.0550 - accuracy: 0.9791 - val_loss: 0.4463 - val_accuracy: 0.9009\n",
      "Epoch 82/100\n",
      "272/272 [==============================] - 628s 2s/step - loss: 0.0519 - accuracy: 0.9800 - val_loss: 0.4376 - val_accuracy: 0.8995\n",
      "Epoch 83/100\n",
      "272/272 [==============================] - 625s 2s/step - loss: 0.0532 - accuracy: 0.9800 - val_loss: 0.4403 - val_accuracy: 0.9038\n",
      "Epoch 84/100\n",
      "272/272 [==============================] - 621s 2s/step - loss: 0.1126 - accuracy: 0.9599 - val_loss: 0.4143 - val_accuracy: 0.8903\n",
      "Epoch 85/100\n",
      "272/272 [==============================] - 625s 2s/step - loss: 0.1007 - accuracy: 0.9616 - val_loss: 0.3866 - val_accuracy: 0.9022\n",
      "Epoch 86/100\n",
      "272/272 [==============================] - 621s 2s/step - loss: 0.1058 - accuracy: 0.9618 - val_loss: 0.3835 - val_accuracy: 0.8972\n",
      "Epoch 87/100\n",
      "272/272 [==============================] - 629s 2s/step - loss: 0.1389 - accuracy: 0.9509 - val_loss: 0.3715 - val_accuracy: 0.8992\n",
      "Epoch 88/100\n",
      "272/272 [==============================] - 620s 2s/step - loss: 0.1108 - accuracy: 0.9592 - val_loss: 0.3817 - val_accuracy: 0.8989\n",
      "Epoch 89/100\n",
      "272/272 [==============================] - 627s 2s/step - loss: 0.0962 - accuracy: 0.9652 - val_loss: 0.3826 - val_accuracy: 0.9029\n",
      "Epoch 90/100\n",
      "272/272 [==============================] - 616s 2s/step - loss: 0.0906 - accuracy: 0.9677 - val_loss: 0.3905 - val_accuracy: 0.9025\n",
      "Epoch 91/100\n",
      "272/272 [==============================] - 626s 2s/step - loss: 0.0835 - accuracy: 0.9694 - val_loss: 0.3933 - val_accuracy: 0.9045\n",
      "Epoch 92/100\n",
      "272/272 [==============================] - 610s 2s/step - loss: 0.0720 - accuracy: 0.9743 - val_loss: 0.4033 - val_accuracy: 0.9015\n",
      "Epoch 93/100\n",
      "272/272 [==============================] - 620s 2s/step - loss: 0.0721 - accuracy: 0.9735 - val_loss: 0.4120 - val_accuracy: 0.9038\n",
      "Epoch 94/100\n",
      "272/272 [==============================] - 628s 2s/step - loss: 0.0630 - accuracy: 0.9778 - val_loss: 0.4209 - val_accuracy: 0.9045\n",
      "Epoch 95/100\n",
      "272/272 [==============================] - 637s 2s/step - loss: 0.0666 - accuracy: 0.9758 - val_loss: 0.4142 - val_accuracy: 0.8992\n",
      "Epoch 96/100\n",
      "272/272 [==============================] - 628s 2s/step - loss: 0.0606 - accuracy: 0.9780 - val_loss: 0.4428 - val_accuracy: 0.8999\n",
      "Epoch 97/100\n",
      "272/272 [==============================] - 632s 2s/step - loss: 0.0599 - accuracy: 0.9782 - val_loss: 0.4105 - val_accuracy: 0.9019\n",
      "Epoch 98/100\n",
      "272/272 [==============================] - 614s 2s/step - loss: 0.0527 - accuracy: 0.9807 - val_loss: 0.4459 - val_accuracy: 0.9015\n",
      "Epoch 99/100\n",
      "272/272 [==============================] - 635s 2s/step - loss: 0.0524 - accuracy: 0.9808 - val_loss: 0.4290 - val_accuracy: 0.9068\n",
      "Epoch 100/100\n",
      "272/272 [==============================] - 639s 2s/step - loss: 0.0531 - accuracy: 0.9804 - val_loss: 0.4338 - val_accuracy: 0.9022\n",
      "Save model to DataSet archive successfully\n"
     ]
    }
   ],
   "source": [
    "key_name=\"train5\"\n",
    "key_name2=\"test5\"\n",
    "history[key_name]=bigru_model.fit(x = X_train[key_name],y=y_train,validation_split=vali_split, epochs =epochs, batch_size=batch)\n",
    "save_model(key_name,bigru_model,modelname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.Load Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = {\n",
    "    #\"train0\":\"train0\",\n",
    "    #\"train1\":\"train1\",\n",
    "    \"train2\":\"train2\",\n",
    "    #\"train3\":\"train3\",\n",
    "    #\"train4\":\"train4\",\n",
    "    \"train5\":\"train5\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_model(names,modelname):\n",
    "    models = dict()\n",
    "    for key in names.keys():\n",
    "        # load json file\n",
    "        load_path = \"DataSet\\\\{a}_{b}\".format(a=modelname,b=names[key])\n",
    "        json_file = open(load_path+\".json\",'r')\n",
    "        model_json = json_file.read()\n",
    "        json_file.close()\n",
    "        model = keras.models.model_from_json(model_json,custom_objects=SeqSelfAttention.get_custom_objects())\n",
    "        # load weights and assign them to the model\n",
    "        model.load_weights(load_path+\".h5\")\n",
    "        print(\"loaded {a} model successfully\".format(a=names[key]))\n",
    "        models[key]=model\n",
    "    \n",
    "    return models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded train2 model successfully\n",
      "loaded train5 model successfully\n"
     ]
    }
   ],
   "source": [
    "models_dict = load_model(model_names,modelname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for getting prediction\n",
    "def get_prediction(classifiers, test_dict):\n",
    "    y_pred_dict= dict()\n",
    "    list1 = list(classifiers.keys())\n",
    "    list2 = list(test_dict.keys())\n",
    "    length = len(list1)\n",
    "    \n",
    "    for i in tqdm(range(length)):\n",
    "        y_pred_dict[list2[i]]=classifiers[list1[i]].predict(test_dict[list2[i]])\n",
    "        \n",
    "    return y_pred_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:27<00:00, 43.78s/it]\n"
     ]
    }
   ],
   "source": [
    "y_scores = get_prediction(models_dict,X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to process the possibilities into category\n",
    "def get_result(y_score):\n",
    "    y_result=list()\n",
    "    for each_pred in y_score:\n",
    "        max_no=0\n",
    "        pos =0\n",
    "        for i in range(4):\n",
    "            if max_no < each_pred[i]:\n",
    "                max_no = each_pred[i]\n",
    "                pos = i\n",
    "        y_result.append(pos)\n",
    "    \n",
    "    y_result = np.array(y_result)\n",
    "    return y_result\n",
    "\n",
    "def get_result_all(y_scores):\n",
    "    y_preds=dict()\n",
    "    for key in y_scores.keys():\n",
    "        y_preds[key]=get_result(y_scores[key])\n",
    "    return y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = get_result_all(y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_preds=\"{0}_preds\".format(modelname)\n",
    "path_scores=\"{0}_scores\".format(modelname)\n",
    "save_prediction(path_preds,y_preds)\n",
    "save_prediction(path_scores,y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8.Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1.plot learning curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_learningCurve(singlehistory,epochs,modelname,dataname):\n",
    "    epoch_range = range(1, epochs +1)\n",
    "    plt.figure()\n",
    "    plt.plot(epoch_range,singlehistory.history['accuracy'],color='b',label='accuracy')\n",
    "    plt.plot(epoch_range,singlehistory.history['val_accuracy'],color='r',label='validation accuracy')\n",
    "    plt.xlim([0.0,epochs+1.0])\n",
    "    plt.ylim([0.0,1.2])\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title('History of {a} Model Accuracy on data {b}'.format(a=modelname,b=dataname))\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.savefig(\"Pictures\\\\{a}\\\\History of {b} Model Accuracy on data {c}.png\".format(a=modelname,b=modelname,c=dataname))\n",
    "    plt.show()\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(epoch_range,singlehistory.history['loss'],color='b',label='loss')\n",
    "    plt.plot(epoch_range,singlehistory.history['val_loss'],color='r',label='validation loss')\n",
    "    plt.xlim([0.0,epochs+1.0])\n",
    "    plt.ylim([0.0,1.2])\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.title('History of {a} Model Loss on data {b}'.format(a=modelname,b=dataname))\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.savefig(\"Pictures\\\\{a}\\\\History of {b} Model Loss on data {c}.png\".format(a=modelname,b=modelname,c=dataname))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learningCurve_all(history,epochs,modelname):\n",
    "    for key in history.keys():\n",
    "        plot_learningCurve(history[key],epochs,modelname,key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": [
    "plot_learningCurve_all(history,epochs,modelname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2. Evaluation: confusion matrix, accuracy, precision, recall, F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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,oneX_test, model):\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",
    "    # and also the basic evaluation from keras\n",
    "    model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "    eval_ = model.evaluate(oneX_test,y_test)\n",
    "    print(\"Loss: {0:.7}\".format(eval_[0]))\n",
    "    print(\"Accuracy: {0:.2%}\".format(eval_[1]))\n",
    "    \n",
    "    return ev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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, X_test,models):\n",
    "    list_test = list(y_pred_dict.keys())\n",
    "    list_train = list(models.keys())\n",
    "    length = len(list_test)\n",
    "    for i in tqdm(range(length)):\n",
    "        print(\"evaluation on data {0}\".format(list_test[i]))\n",
    "        get_evaluation(y_test, y_pred_dict[list_test[i]],X_test[list_test[i]],models[list_train[i]])\n",
    "\n",
    "        print(\"***************************************\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                            | 0/2 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluation on data test2\n",
      "[[4227  138  104  208]\n",
      " [  72  206    2    4]\n",
      " [  25    3  209   13]\n",
      " [  40    0    6  252]]\n",
      "accuracy is: 0.888364494463605\n",
      "precision_macro is: 0.6854147386506988\n",
      "precision_micro is: 0.888364494463605\n",
      "precision_weighted is: 0.9110509883048596\n",
      "recall_macro is: 0.8276935434494166\n",
      "recall_micro is: 0.888364494463605\n",
      "recall_weighted is: 0.888364494463605\n",
      "F1_score_macro is: 0.7425942563582117\n",
      "F1_score_micro is: 0.888364494463605\n",
      "F1_score_weighted is: 0.8959121521130321\n",
      "173/173 [==============================] - 24s 139ms/step - loss: 0.5962 - accuracy: 0.8884\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 50%|██████████████████████████████████████████                                          | 1/2 [00:30<00:30, 30.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.5962203\n",
      "Accuracy: 88.84%\n",
      "***************************************\n",
      "evaluation on data test5\n",
      "[[4172  171  120  214]\n",
      " [  63  218    2    1]\n",
      " [  27    1  210   12]\n",
      " [  36    3    7  252]]\n",
      "accuracy is: 0.8807406062806317\n",
      "precision_macro is: 0.6677391195436009\n",
      "precision_micro is: 0.8807406062806317\n",
      "precision_weighted is: 0.9092521366999223\n",
      "recall_macro is: 0.8363170049797705\n",
      "recall_micro is: 0.8807406062806317\n",
      "recall_weighted is: 0.8807406062806317\n",
      "F1_score_macro is: 0.733858243078821\n",
      "F1_score_micro is: 0.8807406062806317\n",
      "F1_score_weighted is: 0.8899334758535491\n",
      "173/173 [==============================] - 25s 142ms/step - loss: 0.5650 - accuracy: 0.8807\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:00<00:00, 30.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.5649952\n",
      "Accuracy: 88.07%\n",
      "***************************************\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "get_evaluation_all(y_test,y_preds,X_test,models_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.3. Evaluation: ROC curve, AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 48,
   "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": 49,
   "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": 50,
   "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}.png\".format(a=modelname,d=dataname,b=modelname,c=choose))\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 64.69it/s]\n"
     ]
    }
   ],
   "source": [
    "fprs,tprs,roc_aucs=compute_roc_auc_all(y_test1hot, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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34/DBJgCi07L4j7sFyzV1sVgU4eHVefbZrvlxjloNO84BinmnL7K5TQ0swT5YLPD0053p3r2+ES42GYYsB6VIz7Hw0OlELF3qYLEoAgO9+eKLwflx/msrfLnf/jN0ElF5mbscEJHGwA9KqSJvWkQ+BtYopRaZ7kNAb6XUaUdxdunSRekd7soZpWDDKUjPNjJkzWpwZzP7YQ8kwIaTcPiCUUDeEg7XN7Af9v/WwfkMMw1gYk8I8S0abk+8kbnz8mZkKLzczX6cU7fA94fhQCJ0rQMTetovLE+nwj3fg0UZv9p+sGxw0XAAk/6CT/bkF+qvd4en2tsP22SuUWDncewpuP8u+Okn++E1mgpmH/Az8DJsU0p1KUscrtwKtR5wwsYdZ/oVURQi8hTwFEDDhg0rRLgqg1JGDWjzaajrZxSCIzvbD/v0L7A2DrItkJkDY6+B54ux+A36Nv/YTYpXFBtPwdi1+W4vt+IVxbKjEJeS7x57jX1FEZ8OPxzNd6dl248PDKVyINE43nIGLl6yHy5XQdSFfHdmrvF/220lF+pPmz9nCBjhZECNpny5CIzHMOvk/s24XKko7LTPsdu8UUrNBeaC0aIoT6EqHKXsmyrWHIcnfzEKviZB8GBreMGOAhCBJ34u6DeiU9E4LQrCq8M3h/P9MnPsy2Tv2tQsw7RRGLdCYXOUUdDXrGYn3kLu4t5k4fRL88a3nIZ+DY04HCmB84A8WoqIS8f+8K6kLfmWrl3rGR6LDsDI3wDIVIqnQt2tpgIvL3cWLBiUf3HfJYYCBCakp/N/6RnWU2++eT0TJvQxHLvOwY1fATApPZ03bMIBTPn2BQ6espTTHVYMNautpEvYkDJf/9UJ2JVU1N9iMfOYAncPN/Iyp1hAKaNYzc3yxOKRQ1aWB+m5cJ6sohFVAD7J1QGoWbMa/v7eoBSSK1ZTogKO5Oa/Z2VR1Ez05Nz3CeSm5oJAsxY1OHIwscwyuFJRxAG2Vc/6wCkXyVJxPLcKfozOryVvGQqNg4qGi0vNrx1HJ8HZNOfTiM+AWoUKajfJt+XmkVGMogDDJm5bk1+w137ro7CimLvLaH04pSiUczX6pXauLY5x5q8UXOjRF/npR6pXN5/P+PUwewcAFy0WpnWryfqcHJRStG5dkxkzbsm/2DQ97crJocNFmxLpGHx+KCFfUfh6GM/EDXJRfL4vDg4ZysDX16OAorjUyo/ldddzJugUBzIuEJ6SSlBQBtnZ7hwNWs/k3z4BcrmUFIvbS2lkBJ3jQu7HPOHWs8B9VYSS8PBYiX9AfkGelOSLxSL4+HgSFOQL4kbWpTP4ZNVFcj0Q5c6EuOjSJRJ7mYUGCtY+iqtvZ0G2+W/DuJ5mBjueDGYB/deBeNI83RB3QUTo1q0e/n5mxSoj2/otJ6dlsyf2AlLNExEIDPCmTZta+ZFfzIQsCwjEnk6lMT0Jr98WNzehV69G+f0pWbkQY+S3XIvi+zUxuNXxIzp6Hx9/PIGoqN0A9OzZk5nvTKZD49aIvXLGSVypKJYBz4vIYoxO7KSS+icqPdm5cOQinEqFvg2L1owvZBodhbZczATsvEBf94LuVbHwbjHp1q4GZ9Pz3VnFZPzCnaeXHDRIrw0zzovAHyfg0bb2w815Ac7/VtDvGifNL+EVYKa59Vb48UfIscDRi4Zic4ORE9cy89OdRpi/YNXWU9x4YxPD/eo1MKoLuAnJcUm83fojAEQs5OZeJCurM5cunUSpLCxbmpOZFYdkpNDhi084l5yBuCkCAzOYeX4O/53ngdEPqOBFC9nZibi5+9MxJQVv72yysjwQgV4fvYPFkgnAhlowoNkiGnoPwB/oV+iWomyzUKARdXHjF49fWsnK5LLXyJ2iwFdr06o5YVuVP2n3Un8JxsfbAzd3+zUBS66yFuki4Fa4YmKi8jrfjZDGp2cGDfQO5PO7Pkecrm0UT9vabanmaacSVNF4uUOEoTTcgcEtjU72Tz/dRFTUburWrcuUKVMYMmQIYs9iUUrKTVGIyCKgNxAqInHA/wGeAEqpOcBPwK3AESAdKD87QHmTnQt3fmvYyPM48mTRUR2ebnB/S1hyMN/vdBrEHIaBTY0RJnkEFrr2RHLx6d/aFNKyjKZoDR+obmc0CRjK69f74OWH4ddfYBLGzxn8y7FQzyvMi2HDhhOsXHmUjRvjOHw4kQkTevOPf7QrEi4m5iLh4dMBcHcXNmx4nC5d67Iudi0XMy8agSzG71DgFmiR3w/y++lfSD1Qh6ysk+TmppGefpDMzGNkZqbw1NRYtrknsO0irAG8//kCAAMCjcIcjI7uLqEPQOFBN4W7V8SUwc90236B5utvXbPYR+GQbPft7DkzFqW8ATdat65FmKcXj8pDgBvi5oEgZGTkkJJ6CTc3wU0EHx8PqlWzM6oKsFgU4sZlKWRtaVOzDaN6jLqscV6N5OTksG3bNq65xhgwOnToUJKSknj00UcJCAi4bOmU66in8qDSjnq6banRoZzHn0OgSXVD89tiY9YAoEGAMarm7hYFw51Lh21nDJOFjwc0C4ZQOx2/4Jz5pjyxKegtFkVGRjbp6dmcOZNKUJAPDRsWbTFdupTDvff+j7S0bHbvPktSUiaTv3mBvccdmMOuMto0dOfFgWU3F2iubNasWcOIESM4fPgwBw4cIDw83GF4EamSo56qDgcTYNYOo+D+5d6itX2AxoEFFcWSg4atvkPtguFubwpNjc4puodBeFDBlkQetarBLU3KTwmUUIsvjuzsXNLTswkM9LY2aWf8kMKej+x1lHli2H/td6LVvvkmAJrcbbiropJo29CTkQMvX81NoymJEydO8PLLL/PVV8ZAhvDwcM6cOVOiovg7aEXhiFwL3P09rLexr24/C73tDNFtZDN5KcALUrOLKgmAznWMnzOURkmUseAvzIwfUthz3MFwVCvpJQcpA+6+e/EOncWM7f8r9bUD60JLO2W2txtcF5rf7ePm5kdAQGdCQ+8A3FEqi9q1h+LlVeey2HM1mvLg0qVLTJ06lUmTJpGeno6vry+vvfYaL7/8Mr6+xVgbLhNaUTjiTBo83ragovglBnrWL9oKeLC1YT5qEmR/uGtJOFIKf1MJOF/4l522DT15+MZcZn3yB2++8bvh2W43NW44QljdIJSyoFQ2OTkXiLp43nFkx/MPCxf8YT7wXLOig6B83A1F4OZWDU/PGri7BxEaOggvrzr4+jbB07MWnp4h+PqWX61LoylPnn32WebPnw/APffcw5QpU2jUqFGFpK37KPLIWzLAdlRFnl/t2QXDfjcIrqtftnRKaUo61mUA7z66qGxplRF75pSWLWdx6FCC1R0dPZLw8GDOpJ7hmwPfsOvMLuZun1uqdFoFwLUhBf1EPPH2aYSIG11r1qJTaG0yM48RGNiN7OxEgoKuxWLJxN+/IwEB3XB390PEHRG9vqXmyiNvmRGAAwcOMGTIEKZOnUq/foXHwpWM7qP4OyRfgmbzjKUfCi/TIGIz5M5k4z+gaXCZaukjPnqAdvtWFXt+d5v+zBy+uFRxOou9wn/OnK08+2x+S+XBB9uycGH+shbZudlkW4x7DOhyDLp9i7uHhWrVcrjvh9m4u8Om+PgiadWxGYUrwButwMcNRDxQKgdPzxDCwx6inn91PD1r4OMTTlBQTzw9gy/vTWs0VZTU1FQmTZrE7t27+eGHHxARWrVqxY4dO1xiHr26FcXC/fDiauM4MdMwNYX5FwwjwEf9meFdhz1xufCLorjO2cIUpxhKqxDaNvSkR+1krr32v+TkGBN8Jk7swxtvXG83fOPG04iNzR/DbtT+A1BK8UfsH5xJNYbx7pPjELnHGm590gkW7zUmF+05u4d319lM3Ghu/OUCKcDWQo+gTaDxu60utK7ViczMWAICOtO48Xj8/Nrg4WFnATqNRlMApRRLlizh5Zdf5uRJw+S9detWunY1Fg50VR/a1Wt62nzaGNJqj4OPF1iDqLjWQ4FaurMmpTL2N+zbd47IyH8bjm6bqNPtIn16N7Yb9scfo0hOzl/z6LbbIggM9Gbd8XWcSD5h9xpHeJtWnUsWGNcSalWrQU5OIv7+HQkJuYOGNTrRvm43RNzx9KyBiLvjCDUaTRF2797NyJEj+eOPPwDo0qULM2fOpHv37pcl/r9jerp6FYVFwc/HYPo2ZrRqyZ6IeiVeUuxQyL/RER0Tc5Flyw6xa9cZtm8/w9SpA+jbt2iH66lTydR7uyWEXZ7J6/3DgsnNSUKp4pd58HSDh5rUJSLIH4vlEo0bv02NGv3x9i75WWk0Gud59dVXmTJlChaLhdDQUP75z3/y2GOP4eZ2+fredB9FWXATuLUJ3BLOnn9fKDF4ESVhTzmUobWwd+85Xnghf1G/zz/fTd++4Ww7tY0v93yJxSzI953bX0RJfD7oi2KXNADIzDxOdnYimZnRgCI+/hs83KBrMPh5FL3nwMDu1KhxM0pZCA7uT2Bgd9zcrt4sotFUFNWqGcuCjBgxgrfffpvg4MrVX3fVlwIzlucvez1veA0HIW0opZKIi0vGx8eD0ND8NWKUUkRfiCa3xhmoec7wdLPwmf9T/DFtNMeSil84LeW1FPw8/az2yoSEn4mP/xoR4ezZL/Dyqktm5rH8C/IqJTbTOtzcfGjadArBwTfi49MENzf7SzhoNJrLz6ZNm4iPj2fgwIEAvPLKKwwePJi2bYtZU83FXD2K4rvD8O5GWPdggWU19sQZs4HbNiyhoCylcsjIyGbixLX873/7uXQph927nwUgx5LDZ7s+Y+yvY4lPN0cMPZd/nQU4lnTO6h573Vhq+RmrS7qJG4NaDsLP049z5xYTH/81589/UyTtAkoCCA7uj8VyieDgGwkNvQN//2I24dFoNOXK2bNnGTt2LAsWLKBWrVpERUURFBSEr69vpVUScLUoiqMXjb0dwNixrbnRrJvxQ35rokjfQ0md0yWYmTIzc/jnP9dZ3T2ffgm/G7ey+dTmImFr5DTEw9MNXx8PvLzduK3FrTzf7Xlq+tUk0LvgaCGlFH/91YCsrKKrcdat+wQ+Pk3x9g4jKOg6vLzq4e5ezHabGo2mwsjOzmbWrFmMHz+e5ORkvLy8ePzxx/HwqBpFcNWQ8u/S/Yv848MXmLFT5e0LA9i0JsqgHHbuPEO9egHUrOlXwD842Jd27Wqze89piIhiX+vFRXbbmNx/MsO7Di/VssVpaXsKKIn69V8iLOwpqlWLcDoOjUZTcfz222+MHDmS/fuNfatvu+02PvzwQ5o3b+5iyZznylcUr/9Z0P3VQfZ0yx9uZu2kLoVpKTMzh/feW8fEiWuxWBS//faw3ZFK3bqFsTvjTxiSP2diyZ3f0KRWAzrW6Yi7m3PDSLOzL3L48HNcvLiGrKx8bdOrVzru7uW7xotGoyk7ubm5jBgxggMHDtC0aVOmTZtm7ZeoSlz5iiLYmxmP92NPw6IL9Fk7r22VRAkmpaioBFq0mFXAL2/OwrELx9gXv8/qv6XVe1D/L6v7v3f8l/s63OWU2EopEhKWsXfvILvnmzR5TysJjaYSkpmZyaVLlwgKCsLd3Z1Zs2axceNGRo8ejY9P1TQFX/mK4uVudpfAtmtucmJ46//93xrjoNsmqH0WgOnRu/nmWz8+3/15sdctuHMBwzoMK1HcnJxkNm9uQVbWmSLnvL0bEBHxMdWr34C7eyXYZUuj0VhRSrF8+XJefPFF+vbty3/+8x8A+vbtS9++fV0s3d/jilUU9mZTz+ujoJW5Ct1tt8HtNqYmJ+dAzFtwE0tbP0i2JX8f3TUp22F3fpjbmt9mPa7rX5fpt0x32A+hlCItbQ+nTs3l1KnZRc43bTqFBg1eKlE2jUbjGqKionjhhRf4+WdjTtSWLVvIyMgo9+W/K4orUlHYUxJtG3oay5Xm8VPplcTRxKM0m9msgN+LTd+hZctQ65yGGxrdQIvQFvYut0tmZiwbNzYu4h8Q0JW2bZfj5WVnTwuNRlMpSElJ4Z133uHDDz8kOzuboKAgJkyYwPDhw6vMiCZnuHLuxIY8JeFwyY087CxhkpmZw/TpG1m5MpqFi+/kkudFfj7yM8/8+Iw1TLta7dnx9PYyT7E/depjLl78k3PnFhbwD3/XhRMAACAASURBVAm5nebNZ+LjUzHrzGs0mrKRlJREmzZtOHnyJCLC448/zrvvvkutWrVcLdpl54pTFA7nRuRh2ydRiPT0bBo1msb58+nglkvdj6oXCfN056eZMmBKmZREZmYcu3ffRHr6/gL+rVotpHbtB0sdn0ajcQ1BQUHceOONHDhwgFmzZllXeL0SueIUhbU1kZMGX52DRkHQJgT8vYwAtq0JO+amU6dSjA3qWh6AB5ZY/RsENiRX5bB8yHI61e1UarmUsrBhQ12ys88V8G/ZcgFBQTfg69u41HFqNJqKIyEhgTfffJN7772XPn36ADB79mx8fX0v6+J9lZErTlHkMXLi9/mOn+8x9qkuPMLJDs2a1WDwh+f5+Ei+kqiR1ZioZw/j41P6x5WSsp2YmAkkJHxfwN/NzZdrrjmCt3dYqePUaDQVR25uLvPmzeP1118nMTGRDRs2WDcQ8vPzKzmCK4ArRlE43HGucx3j34lhsAfiD/DxkQ+s7pG1/sWUp57H0710jyox8Vd27+5fxN/NzY9evVJctgGJRqNxnvXr1zNixAh27NgBQJ8+fZgxY8ZV9/1eMYrCVkm0jbJZB+lpcwG8EkxOeTz1w1PW43X37eC6Vh1KLUt29sUiSiIoqCcREXPw82tT6vg0Gk3Fcv78eUaPHs3nnxtzoxo0aMDUqVO55557rjolAVeQoshj3vAacNYbWluM5Tv+egdkZX4AG5NTZmYO8fFphNTxZPrG6Szau4g954ytQZ/s9GSZlIRSiujoMVZ3w4bjCA9/56rMXBpNVcXd3Z0VK1bg7e3NmDFjGDt27FVjZrLHFacoAKjtB49Ewvfj4KdCSsJsTcTHp9G372e8+eb1LMh+hRVHCq7zNLHPxFIlqZQiLm46R4+Osvp5etaiSZNJZb8PjUZTYaxevZoePXrg6+tLcHAwX375JU2bNqVJkyauFs3lXBFd9bZDYgHDzOTtUbBPQimrkli+/BC1ak1h795z3H//1wWUxKxbZhE/Jp7a/s5PdMvIiOaPP9wKKAmAyMjvynZDGo2mwoiJiWHw4MH069ePKVOmWP379++vlYRJlW9R2HZiW9dvcjDr+sKFDO54/n149DcQBX5p1nNnRp2jdmBNp9NWSrF5cysyMg4V8O/YcR1BQdeV4W40Gk1FkZGRwfvvv8/7779PZmYmfn5+BAYGlnzhVUiVVxQOZ2EXmnWdlZvFkqOfwmPzi8Tj5eZVKiUBEBc3rYCSCA9/h0aNXi9VHBqNpmJRSvHtt98yevRoYmNjAXjwwQf54IMPqFevnoulq5xUeUWRx0jP87AjHd54rNgwb6x+g8kbJlvdr7R8j+XzE5n24c30bHZNqdLLyUni6NHRVnfPnsl4eBQzE1yj0VQaVq9ezd133w1Au3btmDlzJtdff72LparcXDGKgvc3w554OG+anW65xXpKKcV/d/y3gJJY/+h6QjNb8N59NUo9Iikh4Sf27MkfbtutW5RWEhpNJSYnJ8e6SF/fvn0ZPHgwffv25emnn76iFu8rL8q1M1tEbhaRQyJyRETG2jkfJCLLRWSXiOwTkUfLnJjt3qYA0/K3Px09dzpPLH/C6v7r8b+4tuG1RESElFpJZGWdL6AkwsKeo1q1qrOloUZzNWGxWPjss89o2rQphw4ZZmIRYenSpTz33HNaSThJuSkKEXEHZgO3AK2BISLSulCw54D9Sqn2QG9gqoh4lTnRpH/nH4cY68D/9NNhpu1+1+q98+mddK/fvfCVTnHx4p9s2JDfj9Gu3UoiImY5uEKj0biK7du306tXL4YNG8bx48eZN2+eq0WqspRni6IbcEQpFa2UygIWA3cWCqOAADGq9f5AIpDjbAJFhsVmmyuyeraGGsaWgx989AvUMlobD7V9iMQDZRvVoJSFnTvz7ZgNG46jRo2iS3RoNBrXkpCQwDPPPEOXLl3YsGEDtWvXZsGCBXzwwQclX6yxS3kqinrACRt3nOlnyyygFXAK2AO8oJSyFI5IRJ4Ska0isjU+Pt/ElD/iyQN22mwzOnQqiBAfn8YfkfmzpD3XDjCWDy8DyckbrccdO67TE+k0mkrIihUraN68OR9//DHu7u6MHj2aQ4cOMWzYsCt+hdfypDyfnD3jf+Fdgm4CdgJhQAdglogUqfIrpeYqpboopbrUrFl0COvIgYFQz6Yz+ZObAYiOSQDfTMNvc1eWLzzDPfcUtn6VzPnzy9ixI39ehJ4jodFUTiIiIkhPT6dfv37s2rWLqVOnEhQU5GqxqjzlqSjigAY27voYLQdbHgW+UQZHgGNAyzKlZrvon8mi+Gn5jp9vpmvXsFJ3XkdHv8HevfkWs4gIbefUaCoLJ0+e5O2330aZc6aaNm3Kjh07WLVqFa1bl75SqLFPeSqKLUBzEQk3O6gfAJYVCnMc6AcgIrWBFkB0mVIrtM/EgfgDTN/yofX0td0bM3BgRKmiVEpx/Hi+iSkychlhYU84uEKj0VQEWVlZfPDBB7Ro0YLx48fzxRf5oxxbtWqlF+G8zJTb2DClVI6IPA/8ArgD/1VK7RORZ8zzc4CJwAIR2YNhqnpVKXX+byVsLtdhu1x49Mhoar5Uj2rVPEsVVVRUfhzduh2kWrUWf0s0jUbz9/n555954YUXiIqKAmDQoEH07NnTxVJd2ZTrIGKl1E/AT4X85tgcnwIG/O2ELAW7PhbvXcy64+sA6NO4D+HB4aWO8sSJDzl9+j9Wt1YSGo1riY6OZtSoUSxbZhgmWrRowYwZMxgw4O8XIRrHOG16EpHKuxh7W5u1m/aeZ8jSIVbn1AFTSx1dVlZ8geU5evQo3LWi0Wgqmq+//pply5bh7+/P5MmT2b17t1YSFUSJLQoRuRb4D8Y8h4Yi0h54Wik1vLyFc5rD+crgpn/fCbWM49UPr6Zj3Y6likopxYYNtazurl0P4O1d97KIqdFonEcpRUxMDOHhhkXgxRdfJD4+ntGjR1O3rv4mKxJnWhQfYgxjTQBQSu0CKtcKWuZEu2N+jVhZa4PV+9E+u4iKSihVVGfPLrQeh4QMxM+vbIOwNBpN2dm/fz833ngjnTt35vx5o9vSy8uLyZMnayXhApwyPSmlThTyyi0HWf427a6rn++Y+Aaenu40b17D6euVUhw8ONTq1hsPaTQVS1JSEqNGjaJdu3asXr0aEWHfvn2uFuuqxxlFccI0PykR8RKRl4ED5SxXieQt3zHiowesfqltjP2u2dcacj0YN65nqYbJnTiRv7tV48bjMZar0mg05Y3FYmH+/PlEREQwbdo0LBYLzzzzDFFRUdxwww2uFu+qx5lRT88A0zGW34gDVgIu75/IW76j3b5VAKT37g9BxnH/Ntfjld6cRx91vn8iMXEl0dGvWN2NGr15GaXVaDSOeOqpp/jkk08AuO6665g5cyYdO5auf1FTfjjTomihlPqHUqq2UqqWUuohjPWZKhWy8nvr8dRnhvPpp4NKdX1S0nrrcdeu+xHR68JoNBXFI488Qt26dfn888/5888/tZKoZDjTopgJdHLCz6VEJURZj1vXbI27m/Nmo8zMOGJjJwBQv/5o/PwqnR7UaK4YcnJymDNnDgcOHGD27NkA9OzZk2PHjuHt7e1i6TT2KFZRiEgP4FqgpoiMtjkViDHTulKRYzFWJ+9Yp2OplERW1jk2bsxfkqpmzbsvu2wajcZg7dq1PP/88+zZY/QnPvnkk3To0AFAK4lKjCP7ihfG3AkPIMDmlwzcU/6ilYxtR3aeovB0d36Zjqysc2zYUNvqbtx4AkFB114+ATUaDQBxcXEMGTKEG264gT179hAeHs53331H+/btXS2axgmKbVEopf4A/hCRBUqp2AqUyWnyOrIZcDNHEg8D4OHm/KoksbH5C/41aPAqjRvrDmyN5nIzefJk3n77bdLS0vDx8eG1115jzJgx+Pr6ulo0jZM4U6qmi8hkoA3gk+eplOpbblKVkj82DWDinHFQGw7FH8JiUbi5lTwsNjMzBoCAgC40bfpeOUup0VydHDt2jLS0NO6++26mTp1Ko0aNXC2SppQ4M7RnIXAQCAfeBmIwlhCvNAxPTeNQkqHDElZHsHfvuRKvSUnZTkKCsbhYgwavlqt8Gs3VxJEjR9i0aZPVPXHiRFatWsXXX3+tlUQVxRlFEaKU+gTIVkr9oZR6DOheznKVivMWC/inGo6jTQkNreYwfGZmLNu2dba6fX1Lv7qsRqMpSFpaGuPGjaNNmzY89NBDXLp0CYCQkBBuvPFGF0un+Ts4Y3rKNv9Pi8htGLvU1XcQvsJJUApC8td0CglxbPs8duz/rMeRkd8RENDZQWiNRuMIpRRfffUVL7/8MnFxcYAxaS4jI0OPZLpCcEZRvCMiQcBLGPMnAoEXy1WqUnLbHS1YlR1Khvcp6gTUwdu7+NuKinqWs2c/BSAwsAehoXcWG1aj0Thmz549jBw5kjVr1gDQuXNnZs6cSY8ePVwrmOayUqLpSSn1g1IqSSm1VynVRynVGUisANmKJW+dpzy+//4BGjTyB+D3VY8Xe11OTgqnTln3TaJNm2/KR0CN5iogJyeHgQMHsmbNGkJCQpg7dy6bNm3SSuIKxNGEO3fgPow1nn5WSu0VkYHAOMAXcNkc+z3HswvMoQC4kHEBKH54bEbGMTZtamJ1X3fdeTw9Q8pPSI3mCsRisZCdnY23tzceHh689957rF+/ngkTJlCjhvMrNWuqFo5aFJ8ATwAhwAwRmQ9MAT5QSrl8IRbrHIpbb+XYhWPEp8cDxSuK06c/sR43bjxBKwmNppRs3ryZ7t27M2HCBKvfkCFDmDVrllYSVziO+ii6AO2UUhYR8QHOA82UUmcqRjQnmbmQWZsnWp0NgxraDZaTY3R216nzqJ5Yp9GUgrNnz/Laa68xf76x5fD58+d56623dEf1VYSjFkWWUsoCoJTKBKIqnZIACPNn6YGlAAxqOQg3O6u+pqTstPZN+Pg0KXJeo9EUJTs7m+nTpxMREcH8+fPx9PTk1VdfZffu3VpJXGU4alG0FJHd5rEATU23AEop1a7cpXOC8/d9T2xnY4WRZsHN7IaJinraeqwX/dNoSiYxMZHrr7/eurvcLbfcwrRp04iIiHCxZBpX4EhRVIm1tmsu3wPmNIg76j9oN0xKymYAGjR4WS8hrtE4QXBwMA0aNCAjI4Np06YxcODAUu0WqbmycLQoYKVcCLAIfmnWw06N2hQ5nZWVv5xHaOjgChFJo6lqZGZmMmXKFG6//Xbat2+PiLBgwQKCgoLw8fEpOQLNFY3zS61WVuqeth76+XkVOW2xZFqPAwMr1cojGo3LUUqxfPlyRo0aRXR0NKtWrWLNmjWICLVr1y45As1VQdVXFLcvdyqYt3cD3XTWaGyIiorixRdfZMWKFQBERkYyfvx4/Z1oiuDUxtAi4isiLcpbmLLQukk9ACZ2nFBCSI1GA5CamsrYsWOJjIxkxYoVBAUFMX36dHbs2EGfPn1cLZ6mElKiohCR24GdwM+mu4OILCtvwZxlf8IBAO677n4XS6LRVA0SExOZOXMm2dnZPPbYY0RFRTFy5Eg8PKq+gUFTPjiTM8YD3YA1AEqpnSLSuNwkKiUBXgGkZKVQs1pNV4ui0VRa9u/fT4sWLXB3d6dhw4bMmTOHFi1a0K1bN1eLpqkCOGN6ylFKJZW7JGUkJctYINDXU2+rqNEUJjExkeeff562bdvyySf5y9gMHTpUKwmN0zijKPaKyIOAu4g0F5GZwAZnIheRm0XkkIgcEZGxxYTpLSI7RWSfiPxRCtkL4Onmadc/Pv5/5pEqa9QaTZUjNzeXuXPnEhERwezZsxERTp065WqxNFUUZ0xPI4DXgUvAl8AvwDslXWSuPjsb6A/EAVtEZJlSar9NmOrAR8DNSqnjIlKr9Ldg4O7mbtf/6NGXAcjNTbN7XqO50vjrr78YMWIE27ZtA6B3797MmDGDtm3bulgyTVXFGUXRQin1OoayKA3dgCNKqWgAEVkM3AnstwnzIPCNUuo4gFKq5M2u7VA9NZDThxOp27zgCpaXLp20HkdGfl+WqDWaKsWqVasYMGAAAPXr12fq1Knce++9esir5m/hjOnpXyJyUEQmikjRqc/FUw84YeOOM/1siQCCRWSNiGwTkYftRSQiT4nIVhHZGh8fX2Qviove6RyKK9qNkpy80XocGKg3U9Fc+fTp04cuXbrw+uuvc/DgQe677z6tJDR/G2d2uOsD9AbigbkiskdE3nAibnu5s3BHgQfGSk23ATcBb4pIkVXHlFJzlVJdlFJdlFeIdS+KH31NvZPpQzU7s7LzCAm5A7di9qnQaKoyv/76K927d+f0aWOFAg8PDzZu3Mg777yDn5+fi6XTXCk4NeFOKXVGKTUDeAZjTsVbTlwWBzSwcdcHCvemxWHsnpemlDoPrAXaO4o0I8tiPR4YZk4OOleLatUKdmbn5KSwb989ABjdJRrNlUNsbCx33303/fv3Z9OmTUydOtV6zt1d53fN5cWZCXetRGS8iOwFZmGMeKrvRNxbgOYiEi4iXsADQOGJet8DvUTEQ0SqAdcAB5wV/uFhRudcgLcXNWoUHB67bl2g9Tgw8Fpno9RoKjUZGRm8/fbbtGzZkm+++YZq1arx7rvvMmnSJFeLprmCccYeMx9YBAxQSjk9vk4plSMiz2OMknIH/quU2iciz5jn5yilDojIz8BuwAL8Rym119k07h3cgs++hl69wwkLC7D6Z2bmd42EhQ2nYcOXnY1So6m0rFmzhkcffZSYmBgAHnjgASZPnkz9+s7U2zSaslOiolBKlXnJVaXUT8BPhfzmFHJPBiaXJf5cs4XtXqj/4ejR0dbj5s1nlCVqjabS4e/vT2xsLG3btmXmzJnccMMNrhZJc5VQrOlJRL4y//eIyG6b3x6bne9cypHEIwDkqtwC/llZ8QCEhAzU/ROaKktycjILFiywurt06cKvv/7K9u3btZLQVCiOWhQvmP8DK0KQshDkEwTAubT86RcWSw5JScYE73r1XrB7nUZTmVFKsXDhQsaMGcOZM2cICwuzzo3o27evi6XTXI0U26JQSuXtCDRcKRVr+wOGV4x4jsm1GC2JjnU6Wv0OHhxqPfbza13hMmk0f4cdO3bQq1cvhg4dypkzZ7jmmmuoWVMveKlxLc4Mj+1vx++Wyy1IWcj9Yg8A7mn5pqdz5xYbfu4BeHuHuUQujaa0JCQk8Oyzz9KlSxfWr19PrVq1mD9/Phs2bKBjx44lR6DRlCOO+iieFZE9QItCfRTHMEYpuZzRG6MBOHToAgApKdut57p1i3KJTBpNWZg6dSpz5sxBRBg1ahRRUVE88sgjuLk5NdVJoylXHPVRfAmsAP4J2K78mqKUSixXqZzkUqjRaS3mhisHDz5iPeflpff71VRukpOTCQw05vuMHTuW6Oho3nrrLVq31iZTTeXCUXVFKaVigOeAFJsfIlLDwXUVh1cWADmBKVgsOaSlGaaoJk3e0+vbaCotp0+fZujQobRr14709HQAAgMDWbx4sVYSmkqJI0Xxpfm/Ddhq/m+zcbueMGP+3zWNuxAbm79ndq1a/3CVRBpNsWRlZTF58mQiIiL44osvOHPmDBs3biz5Qo3GxTga9TTQ/A9XSjUx//N+TSpOxOK5savRyde0bgNyc42d7ry8wvDx0TNVNZWLX375hXbt2vHKK6+QmprKnXfeyf79+/VwV02VwJm1nq4TET/z+CER+ZeINCx/0Upmy+nNANT2z9/vqEEDvVyHpnIxYsQIbr75Zg4dOkRERAQrVqzgu+++o0mTSlHf0mhKxJkhFf8G0kWkPfAKEAt8Xq5SOUnrmoY918fDB4sl08XSaDT26dOnD/7+/nzwwQfs2bOHm2++2dUiaTSlwplFAXOUUkpE7gSmK6U+EZFh5S2YM+RYcgAI9gnm1JE5JYTWaMofpRTffPMNR44c4dVXXwXgrrvuolevXnrinKbK4oyiSBGR14ChGEuCuwOeJVxTIeSt8eTh5oGbmy8WSwbBwdrmq3EN+/fvZ+TIkfz222+4u7szaNAgWrRogYhoJaGp0jhjerofuAQ8ppQ6g7GdaZlWe73cHD5/GAB3t/yF/3x9m7tKHM1VSnJyMi+99BLt27fnt99+Izg4mBkzZtCsWTNXi6bRXBac2Qr1DLAQCBKRgUCmUuqzcpesGGqdi7Yep2QbI518PXyLC67RlBtKKT799FMiIiL417/+RW5uLk8//TRRUVEMHz5c7zSnuWIo0fQkIvdhtCDWYOyDPVNExiilvi5n2ezim2kohx/96gLGuoURIRGcdYUwmgolOzubuLg4MjMrz8CF8PBwFixYgLe3NzVq1MDLy4v4+Hji4+NdLZrmKsXHx4f69evj6Xn5egic6aN4HeiqlDoHICI1gV8BlyiKPAZe15g8RaFnYV8dxMXFERAQQOPGjV32zrOzs8nNzcXHxweAJk2akJKSQkhIiM6HGpejlCIhIYG4uDjCw8MvW7zO9FG45SkJkwQnrytf/NIAaHYx0sWCaCqKzMxMlxXISinOnTvH3r17OXbsGEopALy9vQkNDdVKQlMpEBFCQkIue6vbmRbFzyLyC8a+2WB0bv/kIHyF0KZxOPvYTb0mjV0tiqYCcUWBnJKSwvHjx8nIyADA3d2d3NxcPDyc+Xw0moqlPL4RZ/bMHiMig4GeGH0Uc5VS3152SUrJgJuasG8j3N65t6tF0VyhZGVlERcXR2KisViyl5cXDRo0oHr16roFobmqcLQfRXMR+V5E9gL3AlOVUqMqg5KA/N3tbIfGajSXC4vFwoEDB0hMTERECAsLIzIykuDgYIdKYtmyZbz33nsVKGnlZMGCBdSsWZMOHTrQsmVLPvzwwwLn586dS8uWLWnZsiXdunVj3bp11nPZ2dmMHTuW5s2bExkZSbdu3VixYoXddO655x6io/NHQu7YsQMR4ZdffrH6xcTEEBlZ0EQ9fvx4pkyZYnVPmTKFli1bEhkZSfv27fnsM8cDO5VSjBw5kmbNmtGuXTu2b99uN9zq1avp1KkTkZGRDBs2jJwcY5JwUlISt99+O+3bt6dNmzbMnz8fMCon119/vTVcZcFRX8N/gR+AuzFWjJ1ZIRI5Sd6sbA83D5RSWCwZLpZIU+HUnFXwVxyf7S0YbvTqYoPm9T24ublRu3ZtqlevTmRkJGFhYU5tInTHHXcwduzYEsPlpWWxWJwKWx6Ud2F0//33s3PnTtavX8+kSZM4ceIEAD/88AMff/wx69at4+DBg8yZM4cHH3yQM2fOAPDmm29y+vRp9u7dy969e1m+fDkpKSlF4t+3bx+5ubkF1sxatGgRPXv2ZNGiRUXCF8ecOXNYtWoVmzdvZu/evaxdu9aaD4pjxYoVHD58mMOHDzN37lyeffbZImEsFgvDhg1j8eLF7N27l0aNGvHpp58CMHv2bFq3bs2uXbtYs2YNL730EllZWXh5edGvXz+WLFnitPwVgaOcH6CUmqeUOqSUmgI0riCZnOKjrR8B4C7uxMZOsjnj+n52TdUjMzOTw4cPc+5c/riN2rVr06xZM7y9vYmJiaFly5Y88cQTREZG8o9//INff/2V6667jubNm7N5s7FA5YIFC3j++ecBOHv2LHfddRft27enffv2bNiwgZiYGFq1asXw4cPp1KkTJ06cYMyYMURGRtK2bdtiC4jNmzdz7bXX0rFjR6699loOHToEwDXXXMO+ffus4Xr37s22bdtIS0vjscceo2vXrnTs2JHvv//eKt+9997L7bffzoABA0hNTaVfv3506tSJtm3bWsMBTJw4kZYtW9K/f3+GDBlirYEfPXqUm2++mc6dO9OrVy8OHjzo8NmGhITQrFkzTp82Rim+//77TJ48mdDQUAA6derEsGHDmD17Nunp6cybN4+ZM2fi7e1tfQ/33XdfkXgXLlzInXfeaXUrpfj6669ZsGABK1eudLpD99133+Wjjz6ybiIVFBTEsGGOVyn6/vvvefjhhxERunfvzsWLF633l0dCQgLe3t5EREQA0L9/f5YuXQoY/QgpKSkopUhNTaVGjRrWPq9BgwaxcOFCp2SvKByVqj4i0lFEOolIJ8C3kLtSUMe/NjExb1rd7u4+LpRGU9XIzc3l5MmT7Nu3j6SkJM6ePWut5Rc2MR05coQXXniB3bt3c/DgQb788kvWrVvHlClTePfdd4vEPXLkSG644QZ27drF9u3badOmDQCHDh3i4YcfZseOHWzdupWdO3eya9cufv31V8aMGVOkwAFo2bIla9euZceOHUyYMIFx48YB8MADD/DVV18BxoZIp06donPnzkyaNIm+ffuyZcsWfv/9d8aMGUNamjFS8K+//uLTTz9l9erV+Pj48O2337J9+3Z+//13XnrpJZRSbN26laVLl7Jjxw6++eYbtm7N34LmqaeeYubMmWzbto0pU6YwfPhwh8/4+PHjZGZm0q5dO8BoCXTu3LlAmC5durBv3z6OHDlCw4YNrYW2I9avX18gnvXr1xMeHk7Tpk3p3bs3P/1U8piblJQUUlJSaNq0qd3zb731FsuWLSvif/LkSRo0aGB1169fn5MnTxYIExoaSnZ2tvXZff3119ZW1fPPP8+BAwcICwujbdu2TJ8+3dpijYyMZMuWLSXKXpE46sw+DfzLxn3Gxq0Aly6q5KHcyZFcumSd4ajp16XLLleKpKlCKKW4cOECcXFxZGUZOyWGhIRQr169Yk1M4eHhtG3bFoA2bdrQr18/RIS2bdsSExNTJPzq1auttm53d3eCgoK4cOECjRo1onv37gCsW7eOIUOG4O7uTu3atbnhhhvYsmULd9xxR4G4kpKSGDZsGIcPH0ZEyM7OBuC+++6jf//+vP3223z11Vfce++9AKxcuZJly5ZZWwGZmZkcP34cMGq2NWrUsD6HcePGsXbtWtzc3Dh5kVdVigAAIABJREFU8iRnz55l3bp13Hnnnfj6Gqse3H777QCkpqayYcMGazoAly5dsvu8lixZwu+//86hQ4eYN2+ede5Jce+jtAMETp8+XWANrUWLFvHAAw8AhgL9/PPPGTx4cLHxikiJ6U6YMMGuvz3TVOF4RITFixczatQoLl26xIABA6ythl9++YUOHTqwevVqjh49Sv/+/enVqxeBgYG4u7vj5eVFSkoKAQEBjh9CBVGsolBK9alIQUpLjhid2WfOfwZmf7a/fzsXSqSpcOKfdy7cw5HGzyQnJ4ejUVFWu3e1atVo2LAh/v7+DqPJM4WA0YeR53ZzcyuVvd/Pz896XJwtfPbs2cybNw+An376iTfffJM+ffrw7bffEhMTQ+/evQGoV68eISEh7N69myVLlvDxxx9b4126dCktWrQoEO+mTZsKpL9w4ULi4+PZtm0bnp6eNG7cmMzMzGLlslgsVK9enZ07d5Z4n/fffz+zZs3ir7/+4rbbbuOWW26hTp06tG7dmm3bthXYtGn79u20bt2aZs2acfz4cacKSV9fX6t5KTc3l6VLl7Js2TImTZpknXiWNxnywoULBa5NTEwkPDycwMBA/Pz8iI6OLtX+IPXr17e2DsCYDBoWFlYkXI8ePfjzzz8BQ3lHRUUBMH/+fMaOHYuI0KxZM8LDwzl48CDdunUDDOXrSLFWNFXeoJ/stgmApk3/VUJIjcbAdh5Eo0aNaNWqVYlKoiz069ePf//734BRkCUnJxcJc/3117NkyRJyc3OJj49n7dq1dOvWjeeee46dO3eyc+dOwsLCSEpKol69eoDRz2DLAw88wAcffEBSUpK1xXPTTTcxc+ZMa4G/Y8cOuzImJSVRq1YtPD09+f3334mNjQWgZ8+eLF++nMzMTFJTU/nxxx8BY2/v8PBw/ve//wGGQtq1y3FLvkePHgwdOpTp06cD8Morr/Dqq6+SkJAAwM6dO1mwYAHDhw+nWrVqPP7444wcOdLa0jt9+jRffPFFkXhbtWrFkSNHAPj1119p3749J06cICYmhtjYWO6++26+++47/P39qVu37v+3d+7xOdb/H39+DDFkY8ghzRh2vDfMHHdCzr6mSaLIIfxQKbXkK37yLaEcSvWlUBH9iJJTqDmMsGGbmTJDckg2YWxjtvfvj/veZfeON7bdG9fz8bgeu6/r+lyfz/v63Peu9/U5vd78/PPPgNFJbNmyhQ4dOgAwadIkxo4dq30/165dY9GiRQXeU58+ffjqq68QEfbt20f16tWpW7durnRZY143b97k/fffZ/To0QA0bNhQs+fixYv8/vvvmqNKSkqiVq1aRSrBcb+UeUdhY2rtPfbY89Y1RKfUIiIkJiZqXSRKKZycnHB3d6dWrVrFtiZi/vz5hIWF4eHhQcuWLc0GnbMIDg7G09MTg8FAUFAQs2bN4rHHHsuV7o033mDSpEm0b9+ejIwMs3MhISGsWrXKbMB3ypQppKen4+npibu7O1OmTMmZJQCDBg0iMjKSVq1asWLFCpo3bw6Aj48Pffr0wWAw0K9fP1q1akX16tUBYyvkiy++0KZ2Zh8Az4/Q0FCWLl1KcnIyffr0YdiwYbRr147mzZszcuRIli9frj1oZ8yYQa1atXB1dcXd3Z2+ffvmKdPes2dPduzYARi7nYKDg83OP/XUU3zzzTcAfPXVV8yYMQMvLy+CgoKYOnWqNi4xZswYAgMD8fHxwd3dHX9/f2xtbYH8xyh69OiBk5MTTZo0YeTIkXzyySdm586fPw/A7NmzcXFxwdPTk969e2utqClTprB37148PDzo1KkT77//vja4HxYWRo8ePQqt05JEFTYNrLTRSimJBNQ0qKwUm/wEd/cfcXDoZW3TdIqZY8eO4eLiclfX3LhxgzNnznDjxg3s7e3zHbTUyc3169epWrUqKSkp+Pn5sWjRIlq0KDXzWEhNTSUwMJA9e/Y8UEq9/fr147333svVbXg35PW/opQ6KCKt7iU/S9RjFTAIcBKR6aZ42Y+JyIF7KbAoqWRTDsigQoWa1jZFp5SRnp7OuXPnSExMBKBChQrY2dlZ2aqyxYsvvkhcXBxpaWkMGTKkVDkJMI5R/O///i/nzp2jYcOG1janSLh165YW8Ko0YYlYzSdAJsZZTtOBZOA7wKcY7bKIFrWqA5etbYZOKSJLvO/8+fNkZGSglKJOnTrUrVv3gXrrLAmyum1KM127drW2CUVKxYoVef750teNbskYha+IjAXSAETkH6CiJZkrpboppX5XSp1QSuW7XFUp5aOUylBKhVhktYnyut6OTg7S0tL4888/ycjI4NFHH8XNzY0GDRroTkJH5z6wpEWRboqTLaDFoyhUd8B0zUKgC3AWiFBKrReRuDzSvQ/8lDuXgsmkbI2v6BQP6enplC9fHqUUlStXpn79+lSuXJnq1avr4n06OkWAJS2KBcA6oLZS6j9AOJB7GWpuWgMnROSkiNwCVgH/yiPdeIxdWX/nca5A/k5Nv9tLdB4gMjMzuXDhAkeOHOHq1ava8bp16+oKrzo6RYglMuMrlFIHgU4YZcb7isgxC/KuD/yZbf8s4Js9gVKqPhCMcfwj3zEPpdSLwIsA2Rf+96hXE+OQic7DxpUrV/jzzz+1Ka/Jycn6YLWOTjFRaIvCNMspBfgRWA/cMB0r9NI8juXsK5oHhIpIRh5p71wkskhEWuWc2lX5rxQLzNB5kMgS7ztx4oS2erVp06Zmujs6pYvTp09TuXJlvLy8cHV15fnnn9ckSMAoY9K6dWtNdjznYrevvvoKd3d33NzccHV1NZMHz868efPM5MFv376Ng4MDkyZNMkvn6OiozYYD2LFjB7163Zlev3nzZlq1aoWLiwvNmzdn4sSJhd7jl19+ibOzM87OzppCbE7++OMPOnXqhKenJwEBAZw9e1Y7Z2Njg5eXF15eXmbyLc888wzx8fGFll/siEiBG3AEiDH9jQduA0ctuK4t8FO2/UnApBxpTgGnTdt1jN1PfQvKtyWIgDANCZ3vIGFhyJUre0XnwefgwYMSGRkpERERcujQIYFpZlt+/Pe/kWbpRo5cX4JWW87t27etVnZmZqZkZGQUS96nTp0SNzc3ETHeY2BgoCxfvlxERC5cuCCPP/64HDx4UERELl26JC1atJANGzaIiMimTZvE29tbzp07JyIiqampsmjRolxlpKeni4eHh6Snp2vHNm7cKO3atRMnJyfJzMzUjj/xxBNy6dIlbT8sLEx69uwpIiJHjhwRJycnOXbsmJbvwoULC7y/pKQkadSokSQlJcnly5elUaNGcvny5VzpQkJCZNmyZSIi8vPPP8vgwYO1c1WqVMkz7x07dsiIESMKLD8v4uLich0DIqWQ53Z+W6EtChHxEBFP019njGMP4YVdB0QAzkqpRkqpisAzGFsk2fNuJCKOIuIIrAH+R0S+tyBvAGyVPpPlYeKRRx6hQoUKODg45ApEU9xYKjOenxx4RkYGEydOxMPDA09PTz76yBjexdHRkenTp9OhQwdWr17NypUr8fDwwN3dndDQ0DxtyU8aPDQ01GyF8LRp0/jggw8A4wphHx8fPD09mTp1qnZPOSXPx4wZQ6tWrXBzc9PSgVFvqnnz5nTo0IGXXnpJewPPT848P2xsbGjdurWmtLpw4UKGDh2qrdFwcHBg1qxZWvCn9957jzlz5mg6SpUqVWLkyJG58s0KEJQ9PO3KlSt5+eWXadiwIfv27SvQrixmzZrF5MmTtRXq5cuXL1Qd96efftKEFu3t7enSpQtbtmzJlS4uLo5OnToBEBgYaNGK9o4dO7J9+3arBzK6awkPETmEBWsoROQ2MA7jbKZjwP+JyFGl1Gil1Oi7tjQPmlZ+pPBEOmWWmJgY+vfvr2nwlCtXDjc3NxwdHa2ig2OJzHh+cuCLFi3i1KlTHD58mJiYGAYNGqTlW6lSJcLDw/Hz8yM0NJRffvmFqKgoIiIi+P773O9N+UmDP/PMM2bxLLLUZLdu3Up8fDwHDhwgKiqKgwcPsmvXLsBc8vyJJ57gP//5D5GRkcTExLBz505iYmJIS0tj1KhRbN68mfDwcC5duqSVUZCceV6kpaWxf/9+unXrBhQsOQ4QGxub63xe5JQcT01N5eeff6ZXr14MHDjQ4kBGBZW3fv163n777VzHLZEcBzAYDFo8inXr1pGcnKzpXaWlpdGqVSvatGlj9p2XK1eOJk2aFKqnVdxYsjL71Wy75YAWwKV8kpshIpuATTmOfZZP2qGW5JmdR+qVDglenaLln3/+4e233+aTTz4hMzOTZs2aMWPGDACrroewRGY8Pznw7du3M3r0aO2NN0vmG4wqqwAREREEBARoukaDBg1i165d9O3b18wOyUca3NvbW1tseOnSJezt7WnYsCELFixg69ateHt7A8YWSXx8PA0bNjSTPAejc1m0aBG3b9/mwoULxMXFkZmZiZOTE40aNQJg4MCB2jhCfnLmOeUjEhIS8PLyIj4+npCQEC02heQj830vkuPZy9ywYQOBgYHY2try1FNP8c477zB37lxsbGzuubw+ffrkkn/PugdL8pszZw7jxo1j2bJl+Pn5Ub9+fe33cObMGerVq8fJkycJCgrCw8NDk5upXbu2FmfEWliyjiL70/g2sBHjdFar81itKtzM/+VFp4yRkZHBkiVLeOutt0hMTKRcuXKMHz+e1157Lc/0IlPzPJ6TF19syYsv3v8/mSUy4/nJgef3QIQ7suN5PXDAKA0+atQowBgf4fLly3lKg4NRIHDNmjX89ddfWmwGEWHSpElaHlmcPn3aTHL81KlTzJkzh4iICOzt7Rk6dGiBkuNZeeclZ56Txo0bExUVxYULFwgICGD9+vX06dMHNzc3IiMjzR7ABw8exNXVFTA65JyS5HmRXXIcjN1Oe/bswdHRETAqsoaFhdG5c2dNdjxLhO/y5cva56zyDAZDgeVlp0GDBpo4IRglx7O+9+zUq1ePtWvXAkZn/d1332lCi1lda05OTgQEBHD48GHNUaSlpWlxQaxFgV1PpsVwVUXkf03bf0RkhYhYFmOwmLl5w+pyUzpFRGRkJL6+vrz44oskJibi5+fH4cOHWbBgAfb29tY2z2LykwN/8skn+eyzzzSHcvlybukZX19fdu7cSWJiIhkZGaxcuRJ/f398fX01yfE+ffrkKw0Oxlkyq1atYs2aNYSEGIUOunbtypIlS7h+/Tpg7CrJHvI1i2vXrlGlShWqV6/OxYsX2bx5M2DsTjt58qTWasrevWWpnHkWdevWZebMmbz33nsAjB07lmXLlmnxLZKSkggNDeWNN94AjBLgb7zxhhZP++bNmyxYsCBXvtklx69du0Z4eDhnzpzh9OnTnD59moULF2rdTwEBAXz99deA8eVk+fLlBAYaw++8/vrrvPvuu1rciMzMTD78sOAQBl27dmXr1q38888//PPPP2zdujVPaZHExEQteuJ7773HsGHDAGMLOmuad2JiInv27NEcJcDx48e16IjWIl9HoZQqL8Zpq6VLCcyEQ+U7D4/KlZtY0RKdoiDrDbl+/fqsXLmSHTt2aN0TZYn85MBHjBhBw4YNNUnxvHSU6taty3vvvUdgYCAGg4EWLVqYxYTOIj9pcDC+EScnJ1O/fn1NtvvJJ5/k2WefpW3btnh4eBASEqIFbcqOwWDA29sbNzc3hg0bRvv27QHj2/onn3xCt27d6NChA3Xq1NHehC2VM89O3759SUlJYffu3dStW5fly5czcuRImjdvTrt27Rg2bJgWUa9Hjx6MHTuWzp074+bmRsuWLfMc2O3evbs27rJ27VqCgoLMWoD/+te/WL9+PTdv3mTKlCmcOHFCu98mTZowePBgADw9PZk3bx4DBw7ExcUFd3d3LTRtfmMUNWrUYMqUKfj4+ODj48Pbb7+tdS1mlynfsWMHzZo1o2nTply8eJHJkycDRqXXVq1aYTAYCAwM5M0339QcxcWLF6lcuXKesS5KknxlxpVSh0SkhVLqA8AZWA1oHT0isrZkTDQnS2a89qwa/J/PZSpXdsbX97g1TNG5D9LT09m1a5c2CwSMc9GfeuqpfIMI3YvMuE7RkCU5LiKMHTsWZ2dnJkyYYG2zzAgODmbWrFk4Oztb25QiY+7cuTz66KMMHz78rq4raplxS2Y91QCSMK6e7gX0Nv21KuWU0fRy5UpPuEAdy/jll1/w8vKiS5cuHDp0SDs+ZMiQYok0p3P/LF68GC8vL9zc3Lh69Wqu8Y7SwMyZM7W3/wcFOzs7hgwZYm0zChzMrm2a8RSLcUV19pE4q6vxlUspcDG3TinkzJkzvPbaa6xZswYwDnCmpOir68sCEyZMKHUtiJw0a9as1MVxuF9eeOEFa5sAFOwobICqWCbFUeIk37hlbRN0LCQtLY3Zs2fz3nvvkZqaiq2tLZMnT+bVV18tVQHkdXR08qYgR3FBRKaXmCV3SaroyqBlhUmTJjFv3jzAuGZg9uzZujaTjk4ZoqAxilL9JFZWb9PoFET2mSmvv/46bdu2JSwsjFWrVulOQkenjFGQo+hUwDmrY2Oje4rSSHJyMm+88Qbt2rXTpofWq1ePvXv35rkISUdHp/STr6MQkVIdjPpmZeNM3Vu3/rKyJTpgXKG7YsUKmjVrxuzZs4mMjGTnzp3WNqtIyZKCdnd3p3fv3ly5ckU7d/ToUYKCgmjatCnOzs688847Ziua70W6OoubN2/SuXNnvLy8zBa75cfp06eLTTTx5s2bDBgwgCZNmuDr66stwrOEK1eumIkW5pT3Li4uXLiQq5yXX36Z+vXrawvgwCiimFPCPLskedZq98aNG+Pq6kqPHj20hXn5cerUKXx9fXF2dmbAgAHcupX32GpoaCju7u64u7ubfcfDhw/HYDDg6elJSEiItmhyw4YNZqKNxc1diwKWFlrXNi55r1s3t5KkTskSFRWFn58fgwcP5sKFC7Ru3Zr9+/cXKrtQ1qhcuTJRUVHExsZSo0YNFi5cCBgF6Pr06cObb77J8ePHiY6OZu/evdpDMTY2lnHjxrF8+XKOHTtGbGwsTk5OFpd7+PBh0tPTiYqK0nShrMUXX3yBvb09J06cYMKECfkq3OZFTkdxv1iqqPrhhx+aKc5mZmaybt06Hn/8cW2RXmGICMHBwQQEBJCQkEBcXBzvvvsuFy9eLPC60NBQJkyYQHx8PPb29nzxxRe50mzcuJFDhw4RFRXF/v37mT17tiaEOXfuXKKjo4mJiaFhw4Z8/PHHAPTs2ZP169eX2KzBMusoMtPPA/DII/WsbMnDzeTJk2nZsiXh4eHUrl2bJUuW8Ouvv+LjU6jA8H2xY4cqls1S2rZtqymEfvPNN7Rv354nn3wSAFtbWz7++GNNKttS6erLly/Tt29fPD09adOmDTExMfz9998MHjyYqKgovLy8SEhIMLvmxIkTdO7cWVvJnfP86dOn6dixIy1atKBFixbs3bsXML5l+/n5aS2k3bt3k5GRwdChQ3F3d8fDw4O5c+fmsvGHH37Q5vWHhITw888/56kF9eGHH2pvyFkTGd58801NHPD1118HjAv5QkJCaN68OYMGDdLyOnjwIP7+/rRs2ZKuXbtq6yMCAgJ466238Pf3Z/78+axevRp3d3cMBgN+fn55flffffedplYLEBYWhru7O2PGjLFYVTYsLIwKFSowevQd4WsvLy86duyY7zUiwi+//KJJqQwZMiRPNeC4uDj8/f0pX748VapUwWAwaDLljz76qJZXamqqphemlCIgIIANGzZYZP/9UmYdRQcHY4VVqeJhZUsebho0aIBSildeeYXjx4/zwgsvUK5cmf1ZWURGRgY///yzJmSXl1R248aNuX79OteuXbNYKnvq1Kl4e3sTExPDu+++y/PPP0/t2rX5/PPP6dixI1FRUZpQXBaDBg1i7NixWismp9RD7dq12bZtG4cOHeLbb7/lpZdeAozOrWvXrkRFRREdHY2XlxdRUVGcO3eO2NhYjhw5kucc/uyS2uXLl6d69eqaVHYWBw8eZOnSpezfv599+/axePFiDh8+zMyZMzVxwNmzZwPG1tK8efOIi4vj5MmT7Nmzh/T0dMaPH8+aNWs4ePAgw4YN0+QuwNgy2blzJ6+99hrTp0/np59+Ijo6WpPKyM6pU6ewt7c3k/NYuXIlAwcOJDg4mA0bNphF28uPwr5DLy+vXMeSkpKws7PTFGILkh/fvHkzKSkpJCYmEhYWxp9/3oki/cILL/DYY4/x22+/MX78eO14q1at2L17d6G2FwWWqMeWSrIeRY8+2s6qdjxs7N27l4SEBJ577jkAXnzxRQIDA830hkqCgICSn8yQmpqKl5cXp0+fpmXLlnTp0gUoWBn2buSyw8PDtXgFQUFBJCUlcfXq1XzTJycnc+7cOYKDgwHyXJOSnp7OuHHjiIqKwsbGRutT9/HxYdiwYaSnp9O3b1+8vLxwcnLi5MmTjB8/np49e2otpOxYIqkdHh5OcHCwpkzbr18/du/enadEd+vWrWnQoAGAVrd2dnbExsZq9ZuRkWHmALN3v7Vv356hQ4fy9NNP069fv1z5X7hwQZNtB7h16xabNm1i7ty5VKtWDV9fX7Zu3UrPnj3v6zvMEjXMjqXy408++SQRERG0a9eOWrVq0bZtW7MATEuXLiUjI4Px48fz7bffag48S368JCizr37lSvXk3QePCxcu8Pzzz9O+fXvGjBmjvRnZ2NiUuJOwFlljFH/88Qe3bt3SxiiypLKzc/LkSapWrUq1atU06erCsPTBUlD6nMydO5c6deoQHR1NZGSkNpjq5+fHrl27qF+/Ps899xxfffUV9vb2REdHExAQwMKFCxkxYkSu/Bo0aKC97d6+fZurV6+axdaw1K4ssr/p29jYcPv2bUQENzc3TTH3yJEjbN26VUuXXRr9s88+Y8aMGfz55594eXnlat3klB/fsmULV69excPDA0dHR8LDw7Xupyz58ewkJydjZ2dn8XeYHQcHB65cuaKNpZw9e1aTE8/J5MmTiYqKYtu2bYhILr0qGxsbBgwYoL1IQMnKj5dZR6EyC0+jc//cunWLOXPm0KxZM77++msqVqzIK6+8gp2dnbVNsxrVq1dnwYIFzJkzh/T0dAYNGkR4eDjbt28HjC2Pl156SZPKtlS62s/PjxUrVgDGGUEODg5aH3VePProozRo0EDr975582auwc2rV69St25dypUrx9dff61NWf7jjz+oXbs2I0eOZPjw4Rw6dEiTwc4K9JNdhyuLPn368OWXXwKwZs0agoKCcjkzPz8/vv/+e1JSUrhx4wbr1q2jY8eOVKtWLU/V2pw0a9aMS5cu8euvvwLGVlFWxLucJCQk4Ovry/Tp03FwcDDrsgFo2rSp2cyslStX8vnnn2vy46dOnWLr1q2kpKTg5+fH+vXrNRvXrl2LwWDAxsaGoKAgbt68yeLFi7W8IiIiCpzZp5QiMDBQk6z58ssv81QDzsjI0BxcTEwMMTExPPnkk4iIJp0uIvz4449mL2XHjx8vuZDA9xps21pbSxABeX0FEhaGZGTcCaauU7T89NNP0qxZM8Eo2SK9e/eWEydOWM2evALGlyRVqlQx2+/Vq5d89dVXIiISExMj/v7+0rRpU2ncuLFMmzZNMjMztbQ//vijtGjRQpo3by4uLi4yceLEXPknJSVJnz59xMPDQ3x9fSU6OlpERMLCwqRnz5552nT8+HEJDAwUDw8PadGihSQkJMipU6fEzc1NO5+V35tvvqndw7Jly8TNzU28vLykQ4cOcvLkSYmKihJvb28xGAxiMBhk06ZNucpLTU2VkJAQady4sfj4+EhCQkKedn3wwQfi5uYmbm5uMnfuXO34wIEDxc3NTSZOnJjrvsaOHStLly4VEZHDhw9Lx44dxdPTU1xdXWXRokUiIuLv7y8RERHaNcHBweLu7i5ubm7y0ksvmdV5FkFBQRIfHy83btwQe3t7uXr1qtn54OBgWbVqlYiIfPbZZ+Lp6SkGg0G6dOlidn/nzp2T/v37i5OTk7i6ukqPHj3k+PHjIiJiMBjyrIeEhATx8fGRxo0bS0hIiKSlpYmISEREhAwfPlyrUxcXF3FxcRFfX185fPiwiIhkZGRIu3bttPt79tlnzWzv2bOnxMTE5FluXv8rQKTc43M3X5nx0kqWzPikldD1MfDzS6dcuTI71FJqERH8/PwIDw/H2dmZ+fPn0717d6vapMuM69wL69at4+DBg1o43QeBixcv8uyzz/Lzzz/neb6oZcbL7BM2a4zCGIRPpyhITU3l2rVr1KlTB6WUFmv5lVdeMetL1tEpSwQHB+cauyjrnDlzhg8++KDEyiuzjuJWJtSpNeiug7Dr5EZEWLduHa+++iru7u7a3Gxvb2+8vb2tbJ2Ozv2T18B8Waa41ynlpMw6iscqoU99KgJ+++03XnrpJbZt2wYYA6VcuXLloR6s1tHRMafMznp6rBJUr57/qkidgrl27RoTJ07Ew8ODbdu2YW9vz8KFC4mMjNSdhI6OjhlltkVho6Bq1dyrIXUKJy0tDQ8PD86cOYNSilGjRjFjxgwcHBysbZqOjk4ppEw7Cp17o1KlSgwcOJBdu3bx0UcfWSQvoaOj8/BSZrueKpZZy0uexMRERo0aZSZfPH36dMLDw3UncRfoMuOwa9cuWrRoQfny5bWFZCVZ/t2SmpqKv7+/ttAQjKvVK1WqZCaPsmzZMsaNG2d2bUBAgLbi/vr164waNYrGjRvj5uaGn58f+/fvL7Dsy5cv06VLF5ydnenSpUuuVd9ZzJ8/H3d3d9zc3DQBRTDKntevXx8vLy+8vLzYtGkTAEeOHGHo0KF3VQ/3S5l93FaxAa7dtLYZpZrbt2+zcOFCmjZtyqJFi3jzzTe1f5iKFSs+8OJ9RY0uMw4NGzZk2bJlPPvss1a1w1LhTV2QAAAgAElEQVSWLFlCv379sLG5M41+5cqV+Pj4sG7dOovzGTFiBDVq1CA+Pp6jR4+ybNkyLU5FfsycOZNOnToRHx9Pp06dNDXh7MTGxrJ48WIOHDhAdHQ0GzZsID4+Xjs/YcIETcqkR48eAHh4eHD27FnOnDljsf33S5nteiqnoMqFx6GBtS0pnezevZvx48cTHR0NQOfOnVmwYIHZP0xZRv1v8fQ9ylTLFqC2bduWmJgYIH+Z8YCAAMaOHXtXMuPDhg3j5MmT2NrasmjRIh577DEGDx7MpUuX8PLy4rvvvjNTkD1x4gSjR4/m0qVL2NjYsHr1arPv+PTp0zz33HPcuGEM9PXxxx/Trl07Lly4wIABA7h27Rq3b9/m008/pV27dgwfPpzIyEiUUgwbNowJEyaY2ejo6AhQ6EtGRkYGI0eOZO/evdSvX58ffviBypUrs3jxYhYtWsStW7do0qQJX3/9Nba2tgwdOpRKlSpx9OhRLl68yIcffkivXr1YtmwZ69at4+bNm5w6dYpnn32WqVOnMmXKFBwcHHj55ZcBo1ZSnTp1NHXcLFasWME333yj7SckJHD9+nVmz57Nu+++a9GbeUJCAvv372fFihXafTs5ORXq7H/44Qd27NgBGCXGAwICeP/9983SHDt2jDZt2mBrawuAv78/69at0+Rf8qN3796sWrWq0HRFRZl9pbySUBebGvrsnJz8888/DBo0CD8/P6Kjo3niiSf47rvv2Lp1q76quYh4mGXGLSU+Pp6xY8dy9OhR7OzsNDG7fv36ERERQXR0NC4uLmaBfE6fPs3OnTvZuHEjo0eP1sT8Dhw4wIoVK4iKimL16tVERkYyfPhwTXMqMzOTVatWMWjQIDMbbt26xcmTJzXnBnckxjt27Mjvv//O33//Xei9HD16FC8vr3xfsnr06JGniuvFixe176Nu3bp5luXu7s6uXbtISkoiJSWFTZs2melVffzxx3h6ejJs2DCzrquSlBiHMtyiUAqoUGb9XLFRuXJl9u3bR6VKlQgNDeWNN97Q3lYeJCx98y9KdJlxy2nUqJEWo6Fly5aaMF9sbCz//ve/uXLlCtevX6dr167aNU8//TTlypXD2dkZJycnfvvtNwC6dOlCzZo1AaOjCQ8P55VXXqFmzZocPnyYixcv4u3traXJIjExMddU71WrVrFu3TrKlStHv379WL16NWPHjr2v7y9r7OBecHFxITQ0lC5dulC1alUMBoMmMT5mzBimTJmCUoopU6bw2muvsWTJEqBkJcahmFsUSqluSqnflVInlFJv5nF+kFIqxrTtVUoZLM77kfLwaMWiNbiM8tNPP2lvG5UqVeKbb74hLi6OadOmPZBOwlroMuOWk5d8OMDQoUP5+OOPOXLkCFOnTjWTAM95r9mjueV1fMSIESxbtoylS5cybNiwXDbklBiPiYkhPj6eLl264OjoyKpVqwqUGL98+TIODg64ubkRHR1tFl/bEurUqaNF5rtw4QK1a9fOM12Weu+uXbuoUaOGJjFep04dbGxsKFeuHCNHjuTAgQPaNSUpMQ7F6CiUUYRpIdAdcAUGKqVccyQ7BfiLiCfwDrDI0vyr1S0HdrnfoB4mTp48yb/+9S+6devG22+/rR339fWlUaNGVrTsweZhlhm/X5KTk6lbty7p6enavWaxevVqMjMzSUhI4OTJkzRr1gyAbdu2cfnyZVJTU/n+++9p3749YNRw2rJlCxEREWYtkyzs7e3JyMjQnMXKlSuZNm2aJjF+/vx5zp07xx9//IGPjw979uzhr7/+AiAyMpKbN2/y+OOP07hxY1q1asXUqVM15xwfH88PP/xQ4L1ml2TPT2Ic0Lqkzpw5w9q1axk4cCCA5mTAKGyYfSZZiUqMQ/HJjANtgZ+y7U8CJhWQ3h44V1i+WTLjsbFP5ymv+zBw48YN+fe//y2PPPKIAFK1alX54IMPrG1WsaPLjOempGXGDxw4IPXr1xdbW1upUaOGuLq65kqTvXwRkdmzZ8vUqVNFROSTTz4RR0dH8ff3l3HjxsmQIUNERGTIkCHyyiuvSIcOHcTZ2Vl+/PFHERFZunSp9O/fX3r06CFNmzaVadOmmZU1atQoCQ0NzbNuRESGDRsm27ZtExERR0dHOXbsmNn5CRMmyMyZM0VE5Pvvv9fuv3379nLw4EEt3dWrV2XEiBHi5OQk7u7u4u/vLwcOHBARke7du8u5c+dylZ2YmChBQUHSpEkTCQoKkqSkJBExypV3795dS9ehQwdxcXERT09P2b59u3Z88ODB4u7uLh4eHtK7d285f/68dm7s2LGyfv36fO+7qGXGi9NRhACfZ9t/Dvi4gPQTs6fPce5FIBKIzHIUZ89+mm8lPahkZmbK6tWr5fHHH9diRAwePDjPH+mDiLUdhU7xMWTIEFm9enWu40uXLpWxY8fmeU1GRoYYDAYtJkReHDp0SAYPHlxkdpYG0tLSxNfXV9LT84/FU9SOojjHKPLqXM2zU1UpFQgMB0LzOi8ii0SklWTTUn8YVWMPHTpE//79tbCP4eHhfP311/mGV9TReVCJi4ujSZMmdOrUKVfY0Ox4e3sTGBhotuCurHPmzBlmzpxpFle7uCm2wEVKqbbANBHpatqfBCAi7+VI5wmsA7qLyPHC8s0KXJSakkDlypYvWiqrpKWlmc1mGTNmDAaDgZEjRz4wayIsRQ9cpKNjGUUduKg4WxQRgLNSqpFSqiLwDLA+ewKlVENgLfCcJU4iO5UqPdiDtZmZmSxduhRHR0f27t2rHf/0008ZPXr0Q+ckdHR0rEexOQoRuQ2MA34CjgH/JyJHlVKjlVKjTcneBmoCnyilopRSkflkZ8YBd6DdcsgsW2FcLSUiIoJ27doxbNgwLl68yFdffWVtk3R0dB5iirWTS0Q2AZtyHPss2+cRwF1P1p46DgZOvMjzD9gwxaVLl3jrrbf44osvEBHq1q3L7Nmzy4yujo6OzoNJmVyZrYBkxLQ8+8Hgl19+4amnnuLKlStUqFCBV155hSlTplCtWjVrm6ajo/OQUyY1MBRQ/sHxEYBRERKga9euHDlyhFmzZulOopShy4zDhx9+iKurK56ennTq1Ik//vijSPMvyPYLFy7Qq1cvs2Mvv/wy9evXN1s1PW3aNObMmWOWztHRUVN7/euvv3jmmWdo3Lgxrq6u9OjRQ1sMmR+nTp3C19cXZ2dnBgwYoK1wz0loaCju7u64u7ubfVcdO3bU5MLr1atH3759AdiwYQNTp04tsOzSQNl0FAoqvNnG2mbcF2fPnuW1117TfnC1atUiKiqKzZs3aytSdUoXusy4cbppZGQkMTExhISElJh6KRid1MiRI7X9zMxM1q1bx+OPP86uXbssykNECA4OJiAggISEBOLi4nj33Xe5ePFigdeFhoYyYcIE4uPjsbe3NxMzzGLjxo0cOnSIqKgo9u/fz+zZs7l27RpgVHPOkgtv27Yt/fr1A6Bnz56sX78+14r60kaZ7XoKHnNPs7yszs2bN/nwww+ZMWMGKSkp1KtXj9deew2AJ554wsrWlR1GfnK5WPJd/D81LEr3sMqMBwYGap/btGnD8uXLc93HjRs3ePrppzl79iwZGRlMmTKFAQMGMH36dH788UdSU1Np164d//3vf1FKcfDgQYYNG4atrS0dOnTIt86/++47ZsyYoe2HhYXh7u7OgAEDWLlyJQEBAflem/2aChUqMHr0aO1YlnhhfogIv/zyiyZXPmTIEKZNm8aYMWPM0sXFxeHv70/58uUpX748BoOBLVu28PTTT2tpkpOT+eWXX1i6dClgXA8WEBDAhg0bzNKVNspmiwKwK4M6Txs3bsTd3Z233nqLlJQUnnrqKUJCQqxtls5dosuMG/niiy/o3r17ruNbtmyhXr16REdHExsbS7du3QAYN24cERERxMbGkpqayoYNGwB44YUXWLBgAb/++mu+ZZ06dQp7e3szscEsyfDg4GA2bNhAenp6gfYChX4XeTmNpKQk7OzstAVuDRo04Ny5c7nSGQwGNm/eTEpKComJiYSFhZlJhoNRs6lTp05mGl4lLRl+L5TZFkVZ4sSJE7zyyits3LgRMEoLL1iwgM6dO1vZsrKLpW/+RYkuM36H5cuXExkZyc6dO3Od8/DwYOLEiYSGhtKrVy86duwIGN/mZ82aRUpKCpcvX9ZCil65cgV/f38AnnvuOTZv3pwrzwsXLlCrVi1t/9atW2zatIm5c+dSrVo1fH192bp1Kz179ryv7yIqKirXsbwWJeeV15NPPqlNba9VqxZt27bNtXp65cqVuVR5S1oy/F4oky2Ki2UsAuqePXvYuHEjjz76KB9++CHR0dG6kyiD6DLjRrZv385//vMf1q9fb/aGn0XTpk05ePAgHh4eTJo0ienTp5OWlsb//M//sGbNGo4cOcLIkSNJS0sr0MlmJ6dk+JYtW7h69SoeHh44OjoSHh5eoGR4cnIydnZ2Fn8X2XFwcODKlSuaVPrZs2fzlc2ZPHkyUVFRbNu2DRExkxdJSkriwIED9OzZ0+yakpYMvxfKpKPwrG5tCwpGRIiLi9P2n3vuOaZPn87vv//OhAkTqFChghWt07lfHmaZ8cOHDzNq1CjWr1+fb3yF8+fPY2try+DBg5k4cSKHDh3SHvIODg5cv36dNWvWAGBnZ0f16tUJDw8HyCU9nkXTpk214EdgfDP//PPPNcnwU6dOsXXrVlJSUvDz82P9+vUkJycDsHbtWgwGAzY2NgQFBXHz5k0WL16s5RUREZFnyygLpRSBgYGazflJhmdkZJCUlAQYY1/ExMSYtcpWr15Nr169crX8Slwy/F64VzVBa20tQeasRTL3lU7F1JiYGAkICJBHHnlETp48aW1zHiisrR6ry4yLdOrUSWrXrq2l6d27d640W7ZsEQ8PDzEYDNKqVSuJiIgQEZHJkydL48aNpVOnTjJ06FBNejwyMlI8PT2lTZs2MnXqVDOJ8uwEBQVJfHy83LhxQ+zt7eXq1atm54ODg2XVqlUiIvLZZ5+Jp6enGAwG6dKliyQkJGjpzp07J/379xcnJydxdXWVHj16aAq0BoMhz7ITEhLEx8dHGjduLCEhIZKWliYiIhERETJ8+HAREUlNTRUXFxdxcXERX19fOXz4sFke/v7+snnz5lx59+zZU2JiYvIs914pMzLjxbW1BPlgLZLktvieK7E4+Oeff2T8+PFiY2MjgNSsWVN++ukna5v1QGFtR6FjXdauXSuTJ0+2thlFyl9//SVBQUFFnm9ZkhkvVmJuFT7DoSTIzMzk888/x9nZmY8++ggRYdy4ccTHx99XzGEdHR1zgoODcXR0tLYZRcqZM2f44IMPrG1GoZTZWU+lRT311VdfZf78+YBx9eVHH32EwWBx6G8dHZ274H7ieJdGfHx8rG2CRZTZFkV5l5rWNgGAUaNG4ejoyDfffMPOnTt1J6Gjo/PAUTZbFArs/uNX4uWmp6ezcOFCdu/ezZo1a1BK4eLiQnx8fIlGm9LR0dEpScrs0615c4cSLe+XX35h/Pjx2rTXXbt2aYuEdCeho6PzIFNmu55KijNnztC/f386depEXFwcTk5OrF+/Hj+/km/R6Ojo6FiDMukoSkrCY86cOTRv3pw1a9Zga2vLjBkzOHr0KL17974raQadBwNdZhw+++wzPDw88PLyokOHDmYLS0sTqamp+Pv7awsMwbhKvVKlSmayKMuWLWPcuHFm1wYEBGgr7a9fv86oUaNo3LixJjmyf//+Asu+fPkyXbp0wdnZmS5duuRaJZ7F/PnzcXd3x83NjXnz5mnHBwwYoEmSOzo6avpTR44cYejQoXdVD0WF7igK4Pr166SmpvL000/z22+/MXny5Dz1dHQeDnSZcXj22Wc5cuQIUVFRvPHGG7z66qslVnb2h35hLFmyhH79+pnNjly5ciU+Pj6sW7fO4nxGjBhBjRo1iI+P5+jRoyxbtkyLa5EfM2fOpFOnTsTHx9OpUydmzpyZK01sbCyLFy/mwIEDREdHs2HDBuLj4wH49ttvNUnyp556SpMk9/Dw4OzZs5w5c8Zi+4uKMukoiovjx4+zbds2bT80NJSdO3fy7bff8vjjj1vRMp1cKFU8m4W0bdtWUxDNT2Y86wFxNzLjffv2xdPTkzZt2hATE8Pff//N4MGDiYqKwsvLi4SEBLNrTpw4QefOnTEYDLRo0SLX+dOnT9OxY0datGhBixYt2Lt3L2AU2fPz89NaSLt37yYjI4OhQ4fi7u6Oh4cHc+fOzWVjdkmRGzdu5Nuy7tu3Ly1btsTNzY1FixZpx6tWrcrkyZMxGAy0adNGiwORkJBAmzZt8PHx4e2336Zq1aqAUcokMDCQZ599Fg8PD6ZMmaJNRwejttKCBQtylb9ixQozmY2EhASuX7/OjBkzNE2owkhISGD//v3MmDGDcuWMj0onJ6dcWk05+eGHHxgyZAhglCTPkljJzrFjx2jTpg22traUL18ef3//XA5MRPi///s/Bg4cqB3r3bs3q1atssj+IuVeV+pZa2sJsuB7JPOrI/e2ZDEPkpOTJTQ0VCpUqCC1a9eWK1euFFneOkWH2WpTKJ6tALLkL27fvi0hISGaHMOECRNk3rx5udLb2dnJ1atXxdvbW6Kiogq9v3Hjxsm0adNEROTnn3/W5CQKkvBo3bq1rF27VkSMEhI3btwwk/C4ceOGpKamiohRzqNly5YiIjJnzhyZMWOGdj/Xrl2TyMhI6dy5s5b3P//8k2eZH3/8sTg5OUmDBg006YucJCUliYhISkqKuLm5SWJiooiIALJ+/XoREXn99dflnXfeERGjjMU333wjIiKffvqpVtdhYWFia2uryeGcOnVKvL29RUQkIyNDnJyctLyzuHnzptSpU8fs2DvvvCPTp0+XjIwMeeKJJ+TixYsiIrJ06VIZO3asWVp/f3+JiIiQH374Qfr27Zvn/YmIdO/eXc6dyy0lVL16dbN9Ozu7XGni4uLE2dlZEhMT5caNG9KmTRsZN26cWZqdO3dq31cW4eHh0qtXr3xtyp5/TngYV2az/sR9ZyEifPPNNzRr1oz333+f9PR0evXqdVdNXB0rUVyuogCyZMZr1qyp9UMbTSk6mfHnnnsOuHeZcVtbW7M06enpjBw5Eg8PD/r376+NKfj4+LB06VKmTZvGkSNHqFatmpnM+JYtW/IVJBw7diwJCQm8//77ZoGEsrNgwQKt1fDnn39q3SoVK1bUwpm2bNlSE/r79ddf6d+/P2Ds3spO69atadSoEWAMaVqzZk0OHz7M1q1b8fb2pmZN8zVViYmJ2NnZmR1btWoVzzzzDOXKlaNfv36sXr0ayP/7seR727RpU74qsoXh4uJCaGgoXbp0oVu3bhgMhjwlybO3JsB6kuRl11FUzy1vfDdER0fj7+/PoEGDOH/+PD4+Puzfv58vvviCGjVKPtaBTulHlxk355lnnsmzW2XHjh1s376dX3/9lejoaLy9vTX12AoVKmj3ZGNjo0l3F0SVKlXM9keMGMGyZctYunQpw4YNy5U+pyR5TEwM8fHxdOnSBUdHR1atWlWgJPnly5dxcHDAzc2N6Ohos3jcllCnTh0uXLgAGLv48lPZzVLt3bVrFzVq1DCTJL99+zZr167NNSZlLUnyMukoakY1RznaFZouP0SEwYMHs3v3bmrVqsXnn3/Ovn37aN26ddEZqfPA8jDLjGe1DMAYsTH7wy17mfb29tja2vLbb7+xb9++/CvTRJs2bbSgTYX1wQcHB7NlyxYiIiLo2rVrrvP29vZkZGRozmLlypVMmzZNkyQ/f/48586d448//sDHx4c9e/bw119/ARAZGcnNmzd5/PHHady4Ma1atWLq1KmaU46Pj+eHH34o0L4+ffrw5ZdfAvlLkgP8/fffgHEK/tq1a81aD9u3b6d58+Y0aNDA7BqrSZLfa5+VtbaWIN8saC2y5e4kvG/fvi3Xr1/X9rdv3y4vvfRSvv2wOqUPa6vH6jLjIi+99JK4urqKwWCQgIAAiY2NzZUmLS1NunXrJh4eHhISEiL+/v4SFhaWqw5Xr14tQ4YM0exs3bq1+Pj4yLRp06RevXoF3vuoUaMkNDQ0zzoRERk2bJhs27ZNREQcHR3l2LFjZucnTJggM2fOFBGR77//Xrvv9u3by8GDB7V0V69elREjRoiTk5O4u7uLv7+/HDhwQETyH6NITEyUoKAgadKkiQQFBWnjNefOnZPu3btr6Tp06CAuLi7i6ekp27dvN8tjyJAh8umnn+bKe+zYsdoYT0HoMuMgq9Z3K7SisrN3715p2bKlphuvUzaxtqPQKT5u3LihOdaVK1dKnz598k2bkZEhBoMh34F0EZFDhw7J4MGDi9xOa5KWlia+vr6Snp5eaNqidhRlUnvCoallc8n/+usv3nzzTa0ZeOnSJZKTk6lWrVpxmqejo3OXHDx4kHHjxiEi2NnZsWTJkjzTxcXF0atXL4KDg/Ps9srC29ubwMBAMjIySo3S9P1y5swZZs6caRXJoDLpKAojPT2djz76iGnTppGcnEzFihV5/fXXmTRpUq6BMR0dHevTsWNHoqOjC03n6urKyZMnLcozr4Husoyzs3OBzrE4KZOOoqCZICkpKfj4+GjTAHv37s3cuXNp3LhxSZmnU4xIAVNRdXR0LJsNd7eUTUdRwDlbW1t8fHy4desW8+fPp0ePHiVml07xUqlSJZKSkqhZs6buLHR08kBESEpKKnKpoTLpKDh/HerdgqoVSU1NZdasWfj5+REYGAjAvHnzqFy5Mo88cn9rLXRKFw0aNODs2bNcunTJ2qbo6JRaKlWqlGta7f1SJh2FencfEtqT768d5tVXX+X06dO4ubkRExNDuXLlcq3K1HkwqFChgrZCV0dHp+Qo1gV3SqluSqnflVInlFJv5nFeKaUWmM7HKKVaWJLvmRvX6DZpCP369eP06dN4eHiwcOFCTbhLR0dHR6foUMUx8AGglLIBjgNdgLNABDBQROKypekBjAd6AL7AfBHxLSjfx5SSRKXIME2je+eddxg9erQeZU5HR0enAJRSB0Wk1b1cW5yv4K2BEyJyUkRuAauAnGvZ/wV8ZVoPsg+wU0rVLSjTJCBThJEhz3H8+HHGjRunOwkdHR2dYqQ4n7D1gT+z7Z/F2GooLE194EL2REqpF4EXTbs3gdjFa75m8Zqvi9TgMogDUHAUlYcHvS7uoNfFHfS6uEOze72wOB1FXvMXc/ZzWZIGEVkELAJQSkXea/PpQUOvizvodXEHvS7uoNfFHZRSkYWnypvi7Ho6C2QPC9cAyCmkbkkaHR0dHR0rUpyOIgJwVko1UkpVBJ4B1udIsx543jT7qQ1wVUQu5MxIR0dHR8d6FFvXk4jcVkqNA34CbIAlInJUKTXadP4zYBPGGU8ngBTgBQuyXlR4kocGvS7uoNfFHfS6uINeF3e457ootumxOjo6OjoPBvoKNR0dHR2dAtEdhY6Ojo5OgZRaR1Fc8h9lEQvqYpCpDmKUUnuVUgZr2FkSFFYX2dL5KKUylFIhJWlfSWJJXSilApRSUUqpo0qpnSVtY0lhwf9IdaXUj0qpaFNdWDIeWuZQSi1RSv2tlIrN5/y9PTfvNTRecW4YB78TACegIhANuOZI0wPYjHEtRhtgv7XttmJdtAPsTZ+7P8x1kS3dLxgnS4RY224r/i7sgDigoWm/trXttmJdvAW8b/pcC7gMVLS27cVQF35ACyA2n/P39NwsrS2KYpH/KKMUWhcisldE/jHt7sO4HuVBxJLfBRj1w74D/i5J40oYS+riWWCtiJwBEJEHtT4sqQsBqiljIJOqGB3F7ZI1s/gRkV0Y7y0/7um5WVodRX7SHneb5kHgbu9zOMY3hgeRQutCKVUfCAY+K0G7rIElv4umgL1SaodS6qBS6vkSs65ksaQuPgZcMC7oPQK8LCKZJWNeqeKenpulVU2vyOQ/HgAsvk+lVCBGR9GhWC2yHpbUxTwgVEQyHvAoeJbURXmgJdAJqAz8qpTaJyLHi9u4EsaSuugKRAFBQGNgm1Jqt4hcK27jShn39NwsrY5Cl/+4g0X3qZTyBD4HuotIUgnZVtJYUhetgFUmJ+EA9FBK3RaR70vGxBLD0v+RRBG5AdxQSu0CDBjl/x8kLKmLF4CZYuyoP6GUOgU0Bw6UjImlhnt6bpbWridd/uMOhdaFUqohsBZ47gF8W8xOoXUhIo1ExFFEHIE1wP88gE4CLPsf+QHoqJQqr5SyxajefKyE7SwJLKmLMxhbViil6mBUUj1ZolaWDu7puVkqWxRSfPIfZQ4L6+JtoCbwielN+rY8gIqZFtbFQ4EldSEix5RSW4AYIBP4XETynDZZlrHwd/EOsEwpdQRj90uoiDxw8uNKqZVAAOCglDoLTAUqwP09N3UJDx0dHR2dAimtXU86Ojo6OqUE3VHo6Ojo6BSI7ih0dHR0dApEdxQ6Ojo6OgWiOwodHR0dnQLRHYVOqcSk/BqVbXMsIO31IihvmVLqlKmsQ0qptveQx+dKKVfT57dynNt7vzaa8smql1iTGqpdIem9lFI9iqJsnYcXfXqsTqlEKXVdRKoWddoC8lgGbBCRNUqpJ4E5IuJ5H/ndt02F5auU+hI4LiL/KSD9UKCViIwralt0Hh70FoVOmUApVVUp9bPpbf+IUiqXaqxSqq5Sale2N+6OpuNPKqV+NV27WilV2AN8F9DEdO2rprxilVKvmI5VUUptNMU2iFVKDTAd36GUaqWUmglUNtmxwnTuuunvt9nf8E0tmaeUUjZKqdlKqQhljBMwyoJq+RWToJtSqrUyxiI5bPrbzLRKeTowwGTLAJPtS0zlHM6rHnV0cmFt/XR907e8NiADo4hbFLAOo4rAo6ZzDhhXlma1iK+b/r4GTDZ9tgGqmdLuAqqYjocCb+dR3jJMsQD48poAAALYSURBVCuA/sB+jIJ6R4AqGKWpjwLewFPA4mzXVjf93YHx7V2zKVuaLBuDgS9NnytiVPKsDLwI/Nt0/BEgEmiUh53Xs93faqCbaf9RoLzpc2fgO9PnocDH2a5/Fxhs+myHUfepirW/b30r3VuplPDQ0QFSRcQra0cpVQF4Vynlh1GOoj5QB/gr2zURwBJT2u9FJEop5Q+4AntM8iYVMb6J58VspdS/gUsYVXg7AevEKKqHUmot0BHYAsxRSr2Psbtq913c12ZggVLqEaAbsEtEUk3dXZ7qTkS+6oAzcCrH9ZWVUlGAI3AQ2JYt/ZdKKWeMaqAV8in/SaCPUmqiab8S0JAHUwNKp4jQHYVOWWEQxshkLUUkXSl1GuNDTkNEdpkcSU/ga6XUbOAfYJuIDLSgjNdFZE3WjlKqc16JROS4UqolRs2c95RSW0VkuiU3ISJpSqkdGGWvBwArs4oDxovIT4VkkSoiXkqp6sAGYCywAKOWUZiIBJsG/nfkc70CnhKR3y2xV0cH9DEKnbJDdeBvk5MIBJ7ImUAp9YQpzWLgC4whIfcB7ZVSWWMOtkqpphaWuQvoa7qmCsZuo91KqXpAiogsB+aYyslJuqllkxerMIqxdcQoZIfp75isa5RSTU1l5omIXAVeAiaarqkOnDOdHpotaTLGLrgsfgLGK1PzSinlnV8ZOjpZ6I5Cp6ywAmillIrE2Lr4LY80AUCUUuowxnGE+SJyCeODc6VSKgaj42huSYEicgjj2MUBjGMWn4vIYcADOGDqApoMzMjj8kVATNZgdg62YoxtvF2MoTvBGEskDjiklIoF/kshLX6TLdEYZbVnYWzd7ME4fpFFGOCaNZiNseVRwWRbrGlfR6dA9OmxOjo6OjoForcodHR0dHQKRHcUOjo6OjoFojsKHR0dHZ0C0R2Fjo6Ojk6B6I5CR0dHR6dAdEeho6Ojo1MguqPQ0dHR0SmQ/wcgzrM4y0TBDgAAAABJRU5ErkJggg==\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 = \"test2\"\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": "code",
   "execution_count": 53,
   "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 = \"test5\"\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": [
    "## 8.4.Evaluation: P-R Curve, average precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 55,
   "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": 56,
   "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}.png\".format(a=modelname,d=dataname,b=modelname,c=choose))\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "recalls,precisions,average_precisions = compute_p_r_curve_all(y_test1hot, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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"
    }
   ],
   "source": [
    "key_name=\"test2\"\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": "code",
   "execution_count": 59,
   "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"
    }
   ],
   "source": [
    "key_name=\"test5\"\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": "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": []
  },
  {
   "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
}