Model backlog/Train/158-Tweet-Train-5Fold-roBERTa base 64.ipynb

{"nbformat":4,"nbformat_minor":0,"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":"3.7.6"},"colab":{"name":"158-Tweet-Train-5Fold-roBERTa base 64.ipynb","provenance":[],"collapsed_sections":[]},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"kA4Pu2sPr_eG","colab_type":"code","colab":{}},"source":["# !nvidia-smi"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"rOXNne0OoLTd","colab_type":"code","outputId":"0fe57e20-bedb-44e2-f2d0-aec7af52d748","executionInfo":{"status":"ok","timestamp":1589839401232,"user_tz":180,"elapsed":9878,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["!pip --quiet install transformers\n","!pip --quiet install tokenizers"],"execution_count":3,"outputs":[{"output_type":"stream","text":["\u001b[K     |████████████████████████████████| 645kB 4.9MB/s \n","\u001b[K     |████████████████████████████████| 1.1MB 24.4MB/s \n","\u001b[K     |████████████████████████████████| 3.8MB 39.9MB/s \n","\u001b[K     |████████████████████████████████| 890kB 56.3MB/s \n","\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"htKChfMHoYRb","colab_type":"code","outputId":"106d3392-29e3-4768-8c7d-8a4b41cde701","executionInfo":{"status":"ok","timestamp":1589839420696,"user_tz":180,"elapsed":28967,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"xtDZkoyzod_S","colab_type":"code","colab":{}},"source":["!cp -r '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Scripts/.' ."],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"38KKgQn7owCr","colab_type":"code","colab":{}},"source":["COLAB_BASE_PATH = '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/'\n","MODEL_BASE_PATH = COLAB_BASE_PATH + 'Models/Files/158-roBERTa_base/'\n","\n","import os\n","os.mkdir(MODEL_BASE_PATH)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"MOE8CNSAnvq-","colab_type":"text"},"source":["## Dependencies"]},{"cell_type":"code","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","id":"x48gxo3vnvrA","colab_type":"code","outputId":"577c432d-8a27-4450-9375-9e3191a87308","executionInfo":{"status":"ok","timestamp":1589839442520,"user_tz":180,"elapsed":5513,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}},"colab":{"base_uri":"https://localhost:8080/","height":71}},"source":["import json, warnings, shutil\n","from scripts_step_lr_schedulers import *\n","from tweet_utility_scripts import *\n","from tweet_utility_preprocess_roberta_scripts_aux import *\n","from transformers import TFRobertaModel, RobertaConfig\n","from tokenizers import ByteLevelBPETokenizer\n","from tensorflow.keras.models import Model\n","from tensorflow.keras import optimizers, metrics, losses, layers\n","\n","SEED = 0\n","seed_everything(SEED)\n","warnings.filterwarnings(\"ignore\")"],"execution_count":7,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n","  import pandas.util.testing as tm\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"6HW0GQzPnvrF","colab_type":"text"},"source":["# Load data"]},{"cell_type":"code","metadata":{"id":"jS858zlQNNCs","colab_type":"code","collapsed":true,"outputId":"e53d9572-4b61-41cd-c804-01ac66045a87","executionInfo":{"status":"ok","timestamp":1589839514906,"user_tz":180,"elapsed":11197,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# Unzip files\n","!tar -xvf '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Data/complete_64/fold_1.tar.gz'\n","!tar -xvf '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Data/complete_64/fold_2.tar.gz'\n","!tar -xvf '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Data/complete_64/fold_3.tar.gz'\n","!tar -xvf '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Data/complete_64/fold_4.tar.gz'\n","!tar -xvf '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Data/complete_64/fold_5.tar.gz'"],"execution_count":9,"outputs":[{"output_type":"stream","text":["fold_1/\n","fold_1/y_train.npy\n","fold_1/x_train.npy\n","fold_1/x_valid.npy\n","fold_1/x_valid_aux.npy\n","fold_1/x_valid_aux2.npy\n","fold_1/y_train_mask.npy\n","fold_1/x_train_aux2.npy\n","fold_1/x_train_aux.npy\n","fold_1/y_valid_mask.npy\n","fold_1/y_valid.npy\n","fold_1/y_train_aux.npy\n","fold_1/y_valid_aux.npy\n","fold_2/\n","fold_2/y_train.npy\n","fold_2/x_train.npy\n","fold_2/x_valid.npy\n","fold_2/x_valid_aux.npy\n","fold_2/x_valid_aux2.npy\n","fold_2/y_train_mask.npy\n","fold_2/x_train_aux2.npy\n","fold_2/x_train_aux.npy\n","fold_2/y_valid_mask.npy\n","fold_2/y_valid.npy\n","fold_2/y_train_aux.npy\n","fold_2/y_valid_aux.npy\n","fold_3/\n","fold_3/y_train.npy\n","fold_3/x_train.npy\n","fold_3/x_valid.npy\n","fold_3/x_valid_aux.npy\n","fold_3/x_valid_aux2.npy\n","fold_3/y_train_mask.npy\n","fold_3/x_train_aux2.npy\n","fold_3/x_train_aux.npy\n","fold_3/y_valid_mask.npy\n","fold_3/y_valid.npy\n","fold_3/y_train_aux.npy\n","fold_3/y_valid_aux.npy\n","fold_4/\n","fold_4/y_train.npy\n","fold_4/x_train.npy\n","fold_4/x_valid.npy\n","fold_4/x_valid_aux.npy\n","fold_4/x_valid_aux2.npy\n","fold_4/y_train_mask.npy\n","fold_4/x_train_aux2.npy\n","fold_4/x_train_aux.npy\n","fold_4/y_valid_mask.npy\n","fold_4/y_valid.npy\n","fold_4/y_train_aux.npy\n","fold_4/y_valid_aux.npy\n","fold_5/\n","fold_5/y_train.npy\n","fold_5/x_train.npy\n","fold_5/x_valid.npy\n","fold_5/x_valid_aux.npy\n","fold_5/x_valid_aux2.npy\n","fold_5/y_train_mask.npy\n","fold_5/x_train_aux2.npy\n","fold_5/x_train_aux.npy\n","fold_5/y_valid_mask.npy\n","fold_5/y_valid.npy\n","fold_5/y_train_aux.npy\n","fold_5/y_valid_aux.npy\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_kg_hide-input":true,"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","id":"hVhA98wenvrG","colab_type":"code","outputId":"c623e7e2-4a53-4c73-c9ef-0f635102d627","executionInfo":{"status":"ok","timestamp":1589839518376,"user_tz":180,"elapsed":1178,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}},"colab":{"base_uri":"https://localhost:8080/","height":513}},"source":["database_base_path = COLAB_BASE_PATH + 'Data/complete_64/'\n","k_fold = pd.read_csv(database_base_path + '5-fold.csv')\n","display(k_fold.head())"],"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>textID</th>\n","      <th>text</th>\n","      <th>selected_text</th>\n","      <th>sentiment</th>\n","      <th>jaccard</th>\n","      <th>text_len</th>\n","      <th>text_wordCnt</th>\n","      <th>text_tokenCnt</th>\n","      <th>selected_text_len</th>\n","      <th>selected_text_wordCnt</th>\n","      <th>selected_text_tokenCnt</th>\n","      <th>fold_1</th>\n","      <th>fold_2</th>\n","      <th>fold_3</th>\n","      <th>fold_4</th>\n","      <th>fold_5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>cb774db0d1</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>35</td>\n","      <td>7</td>\n","      <td>10</td>\n","      <td>35</td>\n","      <td>7</td>\n","      <td>10</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>validation</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>549e992a42</td>\n","      <td>sooo sad i will miss you here in san diego!!!</td>\n","      <td>sooo sad</td>\n","      <td>negative</td>\n","      <td>0.200000</td>\n","      <td>45</td>\n","      <td>10</td>\n","      <td>13</td>\n","      <td>8</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>validation</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>088c60f138</td>\n","      <td>my boss is bullying me...</td>\n","      <td>bullying me</td>\n","      <td>negative</td>\n","      <td>0.166667</td>\n","      <td>25</td>\n","      <td>5</td>\n","      <td>6</td>\n","      <td>11</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>train</td>\n","      <td>validation</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>9642c003ef</td>\n","      <td>what interview! leave me alone</td>\n","      <td>leave me alone</td>\n","      <td>negative</td>\n","      <td>0.600000</td>\n","      <td>30</td>\n","      <td>5</td>\n","      <td>6</td>\n","      <td>14</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>train</td>\n","      <td>validation</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>358bd9e861</td>\n","      <td>sons of ****, why couldn`t they put them on th...</td>\n","      <td>sons of ****,</td>\n","      <td>negative</td>\n","      <td>0.214286</td>\n","      <td>74</td>\n","      <td>14</td>\n","      <td>17</td>\n","      <td>13</td>\n","      <td>3</td>\n","      <td>4</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>validation</td>\n","      <td>train</td>\n","      <td>train</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       textID  ... fold_5\n","0  cb774db0d1  ...  train\n","1  549e992a42  ...  train\n","2  088c60f138  ...  train\n","3  9642c003ef  ...  train\n","4  358bd9e861  ...  train\n","\n","[5 rows x 16 columns]"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"b0bUTMPynvrM","colab_type":"text"},"source":["# Model parameters"]},{"cell_type":"code","metadata":{"id":"Paexn4ywnvrM","colab_type":"code","colab":{}},"source":["vocab_path = COLAB_BASE_PATH + 'qa-transformers/roberta/roberta-base-vocab.json'\n","merges_path = COLAB_BASE_PATH + 'qa-transformers/roberta/roberta-base-merges.txt'\n","base_path = COLAB_BASE_PATH + 'qa-transformers/roberta/'\n","\n","config = {\n","  \"MAX_LEN\": 64,\n","  \"BATCH_SIZE\": 32,\n","  \"EPOCHS\": 5,\n","  \"LEARNING_RATE\": 3e-5,\n","  \"ES_PATIENCE\": 5,\n","  \"question_size\": 4,\n","  \"N_FOLDS\": 5,\n","  \"base_model_path\": base_path + 'roberta-base-tf_model.h5',\n","  \"config_path\": base_path + 'roberta-base-config.json'\n","}\n","\n","with open(MODEL_BASE_PATH + 'config.json', 'w') as json_file:\n","    json.dump(json.loads(json.dumps(config)), json_file)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"D_8F5nxDnvrW","colab_type":"text"},"source":["# Tokenizer"]},{"cell_type":"code","metadata":{"_kg_hide-output":true,"id":"6qeQlxrDnvrX","colab_type":"code","colab":{}},"source":["tokenizer = ByteLevelBPETokenizer(vocab_file=vocab_path, merges_file=merges_path, \n","                                  lowercase=True, add_prefix_space=True)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"S3FN1pb1nvrb","colab_type":"text"},"source":["## Learning rate schedule"]},{"cell_type":"code","metadata":{"_kg_hide-input":true,"id":"XUYfnwPRnvrc","colab_type":"code","outputId":"ee3e11fd-c68a-46ba-8547-5559b02b6f31","executionInfo":{"status":"ok","timestamp":1589839563880,"user_tz":180,"elapsed":1023,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}},"colab":{"base_uri":"https://localhost:8080/","height":440}},"source":["LR_MIN = 1e-6\n","LR_MAX = config['LEARNING_RATE']\n","LR_EXP_DECAY = .5\n","\n","@tf.function\n","def lrfn(epoch):\n","    lr = LR_MAX * LR_EXP_DECAY**epoch\n","    if lr < LR_MIN:\n","        lr = LR_MIN\n","    return lr\n","    \n","rng = [i for i in range(config['EPOCHS'])]\n","y = [lrfn(x) for x in rng]\n","\n","fig, ax = plt.subplots(figsize=(20, 6))\n","plt.plot(rng, y)\n","print(\"Learning rate schedule: {:.3g} to {:.3g} to {:.3g}\".format(y[0], max(y), y[-1]))"],"execution_count":14,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:5 out of the last 5 calls to <function lrfn at 0x7f134046f488> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n","Learning rate schedule: 3e-05 to 3e-05 to 1.87e-06\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"3aHwZDF9nvrR","colab_type":"text"},"source":["# Model"]},{"cell_type":"code","metadata":{"id":"7gIO5wkQnvrS","colab_type":"code","colab":{}},"source":["module_config = RobertaConfig.from_pretrained(config['config_path'], output_hidden_states=False)\n","\n","def model_fn(MAX_LEN):\n","    input_ids = layers.Input(shape=(MAX_LEN,), dtype=tf.int32, name='input_ids')\n","    attention_mask = layers.Input(shape=(MAX_LEN,), dtype=tf.int32, name='attention_mask')\n","    \n","    base_model = TFRobertaModel.from_pretrained(config['base_model_path'], config=module_config, name=\"base_model\")\n","    last_hidden_state, _ = base_model({'input_ids': input_ids, 'attention_mask': attention_mask})\n","    \n","    x_start = layers.Dropout(.1)(last_hidden_state) \n","    x_start = layers.Dense(1)(x_start)\n","    x_start = layers.Flatten()(x_start)\n","    y_start = layers.Activation('softmax', name='y_start')(x_start)\n","\n","    x_end = layers.Dropout(.1)(last_hidden_state) \n","    x_end = layers.Dense(1)(x_end)\n","    x_end = layers.Flatten()(x_end)\n","    y_end = layers.Activation('softmax', name='y_end')(x_end)\n","    \n","    model = Model(inputs=[input_ids, attention_mask], outputs=[y_start, y_end])\n","    \n","    return model"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"jy24FlCgnvrj","colab_type":"text"},"source":["# Train"]},{"cell_type":"code","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"id":"RU2sa648nvrj","colab_type":"code","outputId":"9010c0c8-a314-4af2-9552-c01c3a7e4f31","executionInfo":{"status":"ok","timestamp":1589845185089,"user_tz":180,"elapsed":5492792,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["AUTO = tf.data.experimental.AUTOTUNE\n","strategy = tf.distribute.get_strategy()\n","k_fold_best = k_fold.copy()\n","history_list = []\n","\n","for n_fold in range(config['N_FOLDS']):\n","    n_fold +=1\n","    print('\\nFOLD: %d' % (n_fold))\n","    base_data_path = 'fold_%d/' % (n_fold)\n","\n","    # Load data\n","    x_train = np.load(base_data_path + 'x_train.npy')\n","    y_train = np.load(base_data_path + 'y_train.npy')\n","    x_valid = np.load(base_data_path + 'x_valid.npy')\n","    y_valid = np.load(base_data_path + 'y_valid.npy')\n","    \n","    step_size = x_train.shape[1] // config['BATCH_SIZE']\n","    valid_step_size = x_valid.shape[1] // config['BATCH_SIZE']\n","\n","    # Build TF datasets\n","    train_dist_ds = strategy.experimental_distribute_dataset(get_training_dataset(x_train, y_train, config['BATCH_SIZE'], AUTO, seed=SEED))\n","    valid_dist_ds = strategy.experimental_distribute_dataset(get_validation_dataset(x_valid, y_valid, config['BATCH_SIZE'], AUTO, repeated=True, seed=SEED))\n","    train_data_iter = iter(train_dist_ds)\n","    valid_data_iter = iter(valid_dist_ds)\n","    \n","    # Step functions\n","    @tf.function\n","    def train_step(data_iter):\n","        def train_step_fn(x, y):\n","            with tf.GradientTape() as tape:\n","                probabilities = model(x, training=True)\n","                loss_start = loss_fn(y['y_start'], probabilities[0], label_smoothing=0.2)\n","                loss_end = loss_fn(y['y_end'], probabilities[1], label_smoothing=0.2)\n","                loss = tf.math.add(loss_start, loss_end)\n","            grads = tape.gradient(loss, model.trainable_variables)\n","            optimizer.apply_gradients(zip(grads, model.trainable_variables))\n","            # update metrics\n","            train_loss.update_state(loss)\n","            train_loss_start.update_state(loss_start)\n","            train_loss_end.update_state(loss_end)\n","        for _ in tf.range(step_size):\n","            strategy.experimental_run_v2(train_step_fn, next(data_iter))\n","\n","    @tf.function\n","    def valid_step(data_iter):\n","        def valid_step_fn(x, y):\n","            probabilities = model(x, training=False)\n","            loss_start = loss_fn(y['y_start'], probabilities[0])\n","            loss_end = loss_fn(y['y_end'], probabilities[1])\n","            loss = tf.math.add(loss_start, loss_end)\n","            # update metrics\n","            valid_loss.update_state(loss)\n","            valid_loss_start.update_state(loss_start)\n","            valid_loss_end.update_state(loss_end)\n","        for _ in tf.range(valid_step_size):\n","            strategy.experimental_run_v2(valid_step_fn, next(data_iter))\n","\n","    # Train model\n","    model_path = 'model_fold_%d.h5' % (n_fold)\n","    model = model_fn(config['MAX_LEN'])\n","    optimizer = optimizers.Adam(learning_rate=lambda: lrfn(tf.cast(optimizer.iterations, tf.float32)//step_size))\n","    loss_fn = losses.categorical_crossentropy\n","    train_loss = metrics.Sum()\n","    valid_loss = metrics.Sum()\n","    train_loss_start = metrics.Sum()\n","    valid_loss_start = metrics.Sum()\n","    train_loss_end = metrics.Sum()\n","    valid_loss_end = metrics.Sum()\n","        \n","    metrics_dict = {'loss': train_loss, 'loss_start': train_loss_start, 'loss_end': train_loss_end, \n","                    'val_loss': valid_loss, 'val_loss_start': valid_loss_start, 'val_loss_end': valid_loss_end}\n","        \n","    history = custom_fit(model, metrics_dict, train_step, valid_step, train_data_iter, valid_data_iter, \n","                         step_size, valid_step_size, config['BATCH_SIZE'], config['EPOCHS'], config['ES_PATIENCE'], \n","                         (MODEL_BASE_PATH + model_path), save_last=False)\n","    \n","    history_list.append(history)\n","    model.save_weights(MODEL_BASE_PATH +'last_' + model_path)\n","    \n","    # Make predictions (last model)\n","    x_train = np.load(base_data_path + 'x_train.npy')\n","    x_valid = np.load(base_data_path + 'x_valid.npy')\n","    train_preds = model.predict(get_test_dataset(x_train, config['BATCH_SIZE']))\n","    valid_preds = model.predict(get_test_dataset(x_valid, config['BATCH_SIZE']))\n","    \n","    k_fold.loc[k_fold['fold_%d' % (n_fold)] == 'train', 'start_fold_%d' % (n_fold)] = train_preds[0].argmax(axis=-1)\n","    k_fold.loc[k_fold['fold_%d' % (n_fold)] == 'train', 'end_fold_%d' % (n_fold)] = train_preds[1].argmax(axis=-1)\n","    k_fold.loc[k_fold['fold_%d' % (n_fold)] == 'validation', 'start_fold_%d' % (n_fold)] = valid_preds[0].argmax(axis=-1)\n","    k_fold.loc[k_fold['fold_%d' % (n_fold)] == 'validation', 'end_fold_%d' % (n_fold)] = valid_preds[1].argmax(axis=-1)\n","    \n","    k_fold['end_fold_%d' % (n_fold)] = k_fold['end_fold_%d' % (n_fold)].astype(int)\n","    k_fold['start_fold_%d' % (n_fold)] = k_fold['start_fold_%d' % (n_fold)].astype(int)\n","    k_fold['end_fold_%d' % (n_fold)].clip(0, k_fold['text_len'], inplace=True)\n","    k_fold['start_fold_%d' % (n_fold)].clip(0, k_fold['end_fold_%d' % (n_fold)], inplace=True)\n","    k_fold['prediction_fold_%d' % (n_fold)] = k_fold.apply(lambda x: decode(x['start_fold_%d' % (n_fold)], x['end_fold_%d' % (n_fold)], x['text'], config['question_size'], tokenizer), axis=1)\n","    k_fold['prediction_fold_%d' % (n_fold)].fillna(k_fold[\"text\"], inplace=True)\n","    k_fold['jaccard_fold_%d' % (n_fold)] = k_fold.apply(lambda x: jaccard(x['selected_text'], x['prediction_fold_%d' % (n_fold)]), axis=1)\n","\n","    # Make predictions (best model)\n","    model.load_weights(MODEL_BASE_PATH + model_path)\n","    train_preds = model.predict(get_test_dataset(x_train, config['BATCH_SIZE']))\n","    valid_preds = model.predict(get_test_dataset(x_valid, config['BATCH_SIZE']))\n","    \n","    k_fold_best.loc[k_fold_best['fold_%d' % (n_fold)] == 'train', 'start_fold_%d' % (n_fold)] = train_preds[0].argmax(axis=-1)\n","    k_fold_best.loc[k_fold_best['fold_%d' % (n_fold)] == 'train', 'end_fold_%d' % (n_fold)] = train_preds[1].argmax(axis=-1)\n","    k_fold_best.loc[k_fold_best['fold_%d' % (n_fold)] == 'validation', 'start_fold_%d' % (n_fold)] = valid_preds[0].argmax(axis=-1)\n","    k_fold_best.loc[k_fold_best['fold_%d' % (n_fold)] == 'validation', 'end_fold_%d' % (n_fold)] = valid_preds[1].argmax(axis=-1)\n","    \n","    k_fold_best['end_fold_%d' % (n_fold)] = k_fold_best['end_fold_%d' % (n_fold)].astype(int)\n","    k_fold_best['start_fold_%d' % (n_fold)] = k_fold_best['start_fold_%d' % (n_fold)].astype(int)\n","    k_fold_best['end_fold_%d' % (n_fold)].clip(0, k_fold_best['text_len'], inplace=True)\n","    k_fold_best['start_fold_%d' % (n_fold)].clip(0, k_fold_best['end_fold_%d' % (n_fold)], inplace=True)\n","    k_fold_best['prediction_fold_%d' % (n_fold)] = k_fold_best.apply(lambda x: decode(x['start_fold_%d' % (n_fold)], x['end_fold_%d' % (n_fold)], x['text'], config['question_size'], tokenizer), axis=1)\n","    k_fold_best['prediction_fold_%d' % (n_fold)].fillna(k_fold_best[\"text\"], inplace=True)\n","    k_fold_best['jaccard_fold_%d' % (n_fold)] = k_fold_best.apply(lambda x: jaccard(x['selected_text'], x['prediction_fold_%d' % (n_fold)]), axis=1)"],"execution_count":16,"outputs":[{"output_type":"stream","text":["\n","FOLD: 1\n","Train for 687 steps, validate for 171 steps\n","WARNING:tensorflow:From <ipython-input-16-3af83c004821>:42: StrategyBase.experimental_run_v2 (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","renamed to `run`\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","WARNING:tensorflow:6 out of the last 6 calls to <function lrfn at 0x7f134046f488> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","\n","EPOCH 1/5\n","time: 187.9s loss: 4.1986 loss_start: 2.0948 loss_end: 2.1037 val_loss: 1.8701 val_loss_start: 0.9743 val_loss_end: 0.8958\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_1.h5\"\n","\n","EPOCH 2/5\n","time: 178.6s loss: 3.7762 loss_start: 1.9059 loss_end: 1.8703 val_loss: 1.8649 val_loss_start: 0.9732 val_loss_end: 0.8918\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_1.h5\"\n","\n","EPOCH 3/5\n","time: 180.1s loss: 3.6595 loss_start: 1.8512 loss_end: 1.8083 val_loss: 1.8418 val_loss_start: 0.9507 val_loss_end: 0.8910\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_1.h5\"\n","\n","EPOCH 4/5\n","time: 180.1s loss: 3.5963 loss_start: 1.8226 loss_end: 1.7738 val_loss: 1.8689 val_loss_start: 0.9702 val_loss_end: 0.8986\n","\n","EPOCH 5/5\n","time: 176.6s loss: 3.5518 loss_start: 1.7982 loss_end: 1.7536 val_loss: 1.8696 val_loss_start: 0.9673 val_loss_end: 0.9023\n","Training finished\n","\n","FOLD: 2\n","Train for 687 steps, validate for 171 steps\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","\n","EPOCH 1/5\n","time: 187.6s loss: 4.1401 loss_start: 2.0715 loss_end: 2.0686 val_loss: 2.0135 val_loss_start: 1.0413 val_loss_end: 0.9721\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_2.h5\"\n","\n","EPOCH 2/5\n","time: 178.5s loss: 3.7333 loss_start: 1.8916 loss_end: 1.8417 val_loss: 1.8747 val_loss_start: 0.9670 val_loss_end: 0.9077\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_2.h5\"\n","\n","EPOCH 3/5\n","time: 180.8s loss: 3.6308 loss_start: 1.8397 loss_end: 1.7912 val_loss: 1.9777 val_loss_start: 1.0249 val_loss_end: 0.9529\n","\n","EPOCH 4/5\n","time: 177.0s loss: 3.5608 loss_start: 1.8055 loss_end: 1.7553 val_loss: 1.9732 val_loss_start: 1.0052 val_loss_end: 0.9680\n","\n","EPOCH 5/5\n","time: 177.0s loss: 3.5321 loss_start: 1.7911 loss_end: 1.7410 val_loss: 1.9228 val_loss_start: 0.9927 val_loss_end: 0.9301\n","Training finished\n","\n","FOLD: 3\n","Train for 687 steps, validate for 171 steps\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","\n","EPOCH 1/5\n","time: 187.8s loss: 4.2151 loss_start: 2.1038 loss_end: 2.1114 val_loss: 1.9991 val_loss_start: 1.0230 val_loss_end: 0.9761\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_3.h5\"\n","\n","EPOCH 2/5\n","time: 178.6s loss: 3.7933 loss_start: 1.9272 loss_end: 1.8661 val_loss: 1.8975 val_loss_start: 0.9847 val_loss_end: 0.9129\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_3.h5\"\n","\n","EPOCH 3/5\n","time: 180.3s loss: 3.6716 loss_start: 1.8658 loss_end: 1.8058 val_loss: 1.8294 val_loss_start: 0.9509 val_loss_end: 0.8786\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_3.h5\"\n","\n","EPOCH 4/5\n","time: 180.3s loss: 3.6174 loss_start: 1.8362 loss_end: 1.7812 val_loss: 1.8748 val_loss_start: 0.9716 val_loss_end: 0.9032\n","\n","EPOCH 5/5\n","time: 176.6s loss: 3.5737 loss_start: 1.8142 loss_end: 1.7595 val_loss: 1.8672 val_loss_start: 0.9534 val_loss_end: 0.9137\n","Training finished\n","\n","FOLD: 4\n","Train for 687 steps, validate for 171 steps\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","\n","EPOCH 1/5\n","time: 187.5s loss: 4.2797 loss_start: 2.1195 loss_end: 2.1602 val_loss: 1.9397 val_loss_start: 0.9971 val_loss_end: 0.9426\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_4.h5\"\n","\n","EPOCH 2/5\n","time: 178.2s loss: 3.8258 loss_start: 1.9213 loss_end: 1.9045 val_loss: 1.8432 val_loss_start: 0.9683 val_loss_end: 0.8750\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_4.h5\"\n","\n","EPOCH 3/5\n","time: 180.0s loss: 3.7084 loss_start: 1.8691 loss_end: 1.8393 val_loss: 1.8546 val_loss_start: 0.9732 val_loss_end: 0.8814\n","\n","EPOCH 4/5\n","time: 176.2s loss: 3.6450 loss_start: 1.8361 loss_end: 1.8089 val_loss: 1.8566 val_loss_start: 0.9861 val_loss_end: 0.8706\n","\n","EPOCH 5/5\n","time: 176.2s loss: 3.6097 loss_start: 1.8202 loss_end: 1.7895 val_loss: 1.8478 val_loss_start: 0.9610 val_loss_end: 0.8868\n","Training finished\n","\n","FOLD: 5\n","Train for 687 steps, validate for 171 steps\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","\n","EPOCH 1/5\n","time: 187.4s loss: 4.2054 loss_start: 2.0954 loss_end: 2.1101 val_loss: 1.9219 val_loss_start: 0.9734 val_loss_end: 0.9485\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_5.h5\"\n","\n","EPOCH 2/5\n","time: 178.6s loss: 3.7821 loss_start: 1.9054 loss_end: 1.8768 val_loss: 1.9012 val_loss_start: 0.9821 val_loss_end: 0.9191\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_5.h5\"\n","\n","EPOCH 3/5\n","time: 180.2s loss: 3.6729 loss_start: 1.8567 loss_end: 1.8162 val_loss: 1.8984 val_loss_start: 0.9871 val_loss_end: 0.9113\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_5.h5\"\n","\n","EPOCH 4/5\n","time: 181.2s loss: 3.6044 loss_start: 1.8233 loss_end: 1.7811 val_loss: 1.8859 val_loss_start: 0.9847 val_loss_end: 0.9012\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/158-roBERTa_base/model_fold_5.h5\"\n","\n","EPOCH 5/5\n","time: 180.2s loss: 3.5659 loss_start: 1.8032 loss_end: 1.7627 val_loss: 1.8871 val_loss_start: 0.9821 val_loss_end: 0.9050\n","Training finished\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_59x5WnGnvro","colab_type":"text"},"source":["# Model loss graph"]},{"cell_type":"code","metadata":{"_kg_hide-input":true,"id":"Tlq60Zvenvro","colab_type":"code","outputId":"657b216e-ab77-49c4-d745-01b18de1c829","executionInfo":{"status":"ok","timestamp":1589845189907,"user_tz":180,"elapsed":4801,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["for n_fold in range(config['N_FOLDS']):\n","    print('Fold: %d' % (n_fold+1))\n","    plot_metrics(history_list[n_fold])"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Fold: 1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x1080 with 3 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Fold: 2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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size 1440x1080 with 3 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Fold: 3\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABIUAAAN0CAYAAAAqJZifAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeXxU9b3/8fc5s2XfyAJJKEIVpAVlE1qFqmirtoJor/eq9adWLVZwQ+uCa6VFpFfwtqBSXNtaK3UDuWi1Am7obTEVMIhoRUWSQAJhS0hmMjPn90cyk9myQTY4r+fjgZlzzvec8535AiFvP9/vMSzLsgQAAAAAAABbMXu6AwAAAAAAAOh+hEIAAAAAAAA2RCgEAAAAAABgQ4RCAAAAAAAANkQoBAAAAAAAYEOEQgAAAAAAADZEKAQAANBk4sSJeu+993q6GwAAAN2CUAgAAAAAAMCGCIUAAAAAAABsiFAIAAAghs/n0+zZszV+/HiNHz9es2fPls/nkyRVV1frqquu0pgxYzR27FhddNFFCgaDkqTFixdrwoQJGjlypM444wy9//77Pfk2AAAAWuXs6Q4AAAD0No888ojWr1+vZcuWyTAMTZs2TQ8//LBuuOEGPfnkkyooKAgHPuvXr5dhGNqyZYv+/Oc/6/nnn1dBQYG2bdsWDosAAAB6IyqFAAAAYixfvlzTp09Xnz59lJOTo+nTp+vll1+WJDmdTlVVVam8vFwul0tjxoyRYRhyOBzy+Xz6/PPP1dDQoOLiYn3jG9/o4XcCAADQMkIhAACAGJWVlSosLAxvFxYWqrKyUpJ0xRVXaMCAAbr88st12mmnafHixZKkAQMG6Pbbb9eCBQt04oknasaMGdqxY0eP9B8AAKA9CIUAAABi5Ofnq7y8PLxdUVGh/Px8SVJaWppuu+02rVy5Uo888oiefPLJ8FSySZMm6S9/+YtWr14twzD0wAMP9Ej/AQAA2oNQCAAAIMaPfvQjPfLII6qurlZ1dbUeeughTZo0SZK0evVqffXVV7IsS+np6XI4HOE1hd5//335fD653W55PB6ZJv/UAgAAvRcLTQMAAMSYNm2aamtrNXnyZEnSmWeeqWnTpkmSvvrqK/3qV79SdXW1MjIydOGFF+o73/mOPvnkE82bN0+ff/65XC6XRo4cqVmzZvXk2wAAAGiVYVmW1dOdAAAAAAAAQPeiphkAAAAAAMCGCIUAAAAAAABsiFAIAAAAAADAhgiFAAAAAAAAbKhXhEKWZcnr9Yo1rwEAAAAAALpHrwiFfD6fSktL5fP5erornWLjxo093QX0EMbenhh3+2Ls7Yuxty/G3r4Ye/ti7O3JLuPeK0KhI019fX1PdwE9hLG3J8bdvhh7+2Ls7Yuxty/G3r4Ye3uyy7gTCgEAAAAAANgQoRAAAAAAAIANEQoBAAAAAADYEKEQAAAAAACADREKAQAAAAAA2BChEAAAAAAAgA05e7oDAAAAAAD0Jvv27VNlZaUaGhrkdDq1adOmnu4SutnhMO4ul0v5+fnKyMg46GsQCgEAAAAA0GTfvn3asWOHioqKlJycrAMHDig1NbWnu4VuVltb26vH3bIs1dXVqaysTJIOOhgiFOpk9z31T23+okqDPvw/FeenqSgvTUVNX7PTPTIMo6e7CAAAAABoQWVlpYqKipSSktLTXQFaZBiGUlJSVFRUpPLyckKh3uLE4f20d+8e7dxTpw2fVcnnD4aPpSQ5VZiXpuKIoKg4P039clOV5GYoAAAAAKCnNTQ0KDk5uae7AbRLcnKyGhoaDvp8kohOdsro/kpXpUaPHq1g0NLOPXXaVlWjssoalTV9Ld2yS2/+a1vUeXnZyY1VRXnN1UXFeWnKzUqWaVJdBAAAAADdhRkeOFwc6u9VQqEuZJqG8nNSlJ+TolFD8qOO1Xv9Kt9Z2xgUNYVF26pqtOqDr1Xn9YfbuV0OFeamhkOiyAqjlCRXd78lAAAAAABwhCAU6iFJHqcGFWVqUFFm1H7LsrR7vzccEoUqjLZs26v3N5QraDW3zU73NE5Hy4+uLirISZHDYXbzOwIAAAAAAIcTQqFexjAM5WQkKScjScOPzo061uAPaPuuA9oWMRWtrKpG722o0P4DvnA7p8NQ3z6p4YqiyMWuM1LdlEICAAAAgA0MGTKkzTZFRUVatWrVQd/jxRdf1MyZM7Vy5UoVFxcf9HVCFixYoIULF2rz5s2HfC20jVDoMOJyOtS/IF39C9Ljju2r9TWFRPubQ6OqWpV8Uil/oHmx67RkV9QUtFBgVJibKpfT0Z1vBwAAAADQhZYsWRK1fc0112jIkCG69tprw/vcbvch3eOUU07RkiVLlJ+f33Zj9DqEQkeIjFS3MgbmaOjAnKj9gUBQlbvrVFZVE1VhtO7TSq364OtwO9OQ8nNSoqqKQsFRTkYS1UUAAAAAcJgZMWJE1Lbb7VZ2dnbc/kiBQECWZcnpbF9ckJOTo5ycnLYbolciFDrCORym+uWmql9uqsYMLYg6dqC+IWIaWm3U09G8vkC4XbLHocJQSBQTGiV5+C0EAAAAAIerIUOG6Oc//7lSU1P17LPPqqKiQi+88IK++c1vat68eXrvvfdUVlamlJQUDR8+XDfffLO++c1vhs9PNH1s4sSJGjVqlE499VQtXLhQFRUVGjRokG6//XaNGTOmw32sqanR/Pnz9frrr2vPnj0qKirShRdeqEsvvTRcwFBbW6v58+dr5cqV2rlzp9LT0zVkyBDddddd4f7+4Q9/0LPPPquysjJ5PB71799fV199tb7//e93wid5eOInehtLSXLpmP7ZOqZ/dtT+YNDSrr31KqvaH7Xg9SdfVuuddWWyIha7zs1MagyMYp6OlpedIodJdREAAAAA9HYvvvii+vfvr1tvvVXJycnKz8+Xz+dTbW2trr76auXl5Wnv3r165plndMEFF+iVV15RXl5eq9csKSnRF198oeuvv14ej0e//e1v9fOf/1yrVq1SRkZGu/sWDAY1depUffzxx7ruuus0ePBgvfnmm5ozZ46qq6t14403SpLmzJmjVatWacaMGTrqqKO0Z88e/etf/9L+/fslSS+//LLmzp2radOmacyYMfJ6vdq8ebP27Nlz8B/cEYBQCHFM01BedrLyspM1YnD0vFBvQ0DlVTUqr6rVtqbQqKyqRm//a5tq6/3hdi6nqcLc1Kj1iwqbKo3SUg5tzioAAAAAdKdVH2zV3/+5tUf78P2x39DEMd/okmtblqUnnnhCSUlJUftnz54dfh0IBDR+/HideOKJWrFihS677LJWr1lTU6OlS5cqM7Pxidu5ubn6j//4D7311luaNGlSu/v21ltvqaSkRHPmzNF5550nSRo/frzq6ur0xBNP6LLLLlNOTo7WrVunSZMm6fzzzw+fG1kBtG7dOg0ZMkTXXHNNeN/JJ5/c7n4cqQiF0CEel0MDCzM1sDAzar9lWdpT4w2HRKH1i76q2Kf/K92uYLC5vCgzzR21ZlFoHaO+fVLldJjd/ZYAAAAAwNYmTJgQFwhJ0iuvvKInn3xSX3zxRbjiRpK2bNnS5jVHjBgRDoSk5iehVVRUdKhva9eulWmaOvvss6P2T548Wc8//7zWrVuniRMnavjw4XrppZeUnZ2tk046Sd/61rfkcDQ/TGn48OF65pln9Ktf/UqnnXaaRo4cqeTk5A715UhEKIROYRiGstOTlJ2epGHfzI065g8EtX1XbVxg9M+Pt+vv//SF2zlMQ337pKgoL12FealRgVFWmofFrgEAAAD0iIljuq5KpzdI9OSw0FSsc889V9dcc42ys7NlGIamTp0qn8+X4CrRIgMhqfkpZ16vt0N927t3rzIzM+Oekpabmxs+Lkl33nmncnNz9cILL+jBBx9UVlaWzjnnHM2YMUPJycmaMmWKvF6vnn/+eT3zzDNyOp06+eSTddttt4XXQrIjQiF0OafDVHF+uorz0+OO1RzwhdcsKqtqfjrah59WqsEfDLdLTXI2L3Cdn6bivHQV5aepX26qPC5H3HUBAAAAAAdvxYoVGjBggO6///7wvoaGhnAI010yMzO1d+9e+Xy+qGBo586d4eOSlJqaqptuukk33XSTysrK9Nprr2nevHlyuVy6+eabZRiGLrjgAl1wwQXau3ev1qxZo/vvv18zZszQc889163vqTchFEKPSktx69gBOTp2QPQjDANBS1W7D4RDolBwtOHfO7W6ZFu4nWFIeVnJEWFRaLHrdPXJTJLJYtcAAAAA0GH19fVR068kadmyZQoEAi2c0TXGjh2rxx9/XH/72980efLk8P7ly5fL5XJpxIgRcecUFRXp8ssv1/Lly/XZZ5/FHc/MzNQPf/hDrV+/XkuWLOnS/vd2hELolRqnkqWqb59UjT62IOpYndevsqoalUcGRlU12vTPrar3Nf8F5XE7VJSbFlVhVJSXqqK8NKUkubr7LQEAAADAYWPChAl64403dN999+nUU0/VRx99pKeffrpDTw7rDN/73vc0evRo3XPPPaqurtYxxxyjt956S88995yuuuoq5eQ0Fhj813/9lyZOnKjBgwcrJSVFa9eu1SeffKIpU6ZIku666y6lpqZqxIgR6tOnj7788kstW7ZMJ510Ure+n96GUAiHnWSPU0cXZ+no4qyo/ZZlqXpffXjNolBg9OnW3Xp3fZms5rWulZPhUVHTFLTIBa/zc1LkoLoIAAAAgM3953/+pyoqKvTCCy9oyZIlGj58uBYtWhT19K7uYJqmFi9erPnz5+uxxx7Tnj17VFRUpJkzZ+rSSy8NtxszZoxeffVVLV68WIFAQP3799fMmTN1ySWXSJJGjRqlF198UcuWLdP+/fuVn5+vyZMn67rrruvW99PbGJYV+aNy6xYuXKgFCxZo+fLlGjx4cNSxuro6zZw5Uxs3bpTD4dCtt96qU089tV3X9Xq9Ki0t1bBhw+TxeDr2DnqhkpISjR49uqe7gQi+hoAqEix2XVZZo5q6hnA7p8NUv9zGRa4Lc0OLXTeGRxmp7lbu0IixtyfG3b4Ye/ti7O2Lsbcvxt4+Nm3apKFDh4a3a2trlZqa2oM9Qk84nMY99vdsR7S7Umjjxo1at26dioqKEh5//PHHlZaWpr///e/68ssv9ZOf/ESvv/76YfMh4sjmdjk0oG+GBvSNLnW0LEv7an1RIVFZVY2+3rFf/9y4XYFgc2aanuKOeiJaqMKob59UuZxmd78lAAAAAAAOSbtCIZ/Pp1mzZmnevHnh0qtYr776anhV8qOOOkrDhg3T22+/rbPOOqvzegt0MsMwlJnmUWaaR98e1CfqWCAQ1I7qA1FPR9tWWaMPPtmhN9ZuDbczDakgJ1VF+WlyBmtV6fsyvOB1drpHhsF0NAAAAABA79OuUOi3v/2tJk+erOLi4hbblJeXR1UR9evXT9u3b+9QZ0pLSzvUvjcrKSnp6S6gkzgkfSO98ZcGJUtKVr0vqJ37/Nq1v0G79vm1c79fX1dUa9f+Bv3f5vXhcz0uQ33SneqT4VJuulN9Mpp+pTvlprroiMKfefti7O2Lsbcvxt6+GHt7cDqdqq2tjdoXuw17OFzG3efztfr3U2tTX9sMhT788EOVlpbqF7/4xcH1rgNYUwiHu7UffKABg74VVV0UWvD6oy/3RbXNzUoOVxSFpqQV56UpNytZJotdH1b4M29fjL19Mfb2xdjbF2NvH5s2bYpaBuVwWlsGnedwGne3263jjz/+oM5tMxRau3atPv/8c5122mmSpO3bt+uKK67QnDlzNH78+HC7wsJClZWVhR8HV1FRoXHjxh1Up4DDlWkYys9JUX5OikYNyY86Vu/zq2JnbdzT0VZ98LXqvP5wO7fLocLc1OawKOLpaKnJru5+SwAAAACAI1SbodDUqVM1derU8PbEiRO1aNGiuKePnXnmmeHH1H355Zf66KOPNG/evM7vMXCYSnI7NbAwUwMLM6P2W5al3fu94ZAoVGG0pWyv3t9Qroi1rpWV7okKiULVRQU5KXI4mI4GAAAAAGi/dj99LJFzzjlHixcvVkFBga644grddttt+v73vy/TNDVr1iylpaV1Vj+BI5ZhGMrJSFJORpKGH50bdazBH9T2XbVxT0d7b0OF9h/whds5TEN9+6QmfDpaRqqbxa4BAAAAAHE6HAqtWrUq/HrZsmXh1ykpKfrd737XOb0CIElyOU31L0hX/4L0uGP7an3N6xZVNT8dreSTSvkDwXC7tGRXVEhUmNdYXdQvN1Vul6M73w4AAAAAoBc5pEohAD0nI9WtjIE5GjowJ2p/IBBU5e66cEgUqjBa92mlVn3wdbidaUh52SnhKWiRwVFORhLVRQAAAABwhCMUAo4wDoepfrmp6pebqjFDC6KOHahvUHlVbdzT0TZu2SWvLxBul+xxqDAvTUW50U9HK8pLU7KHvzYAAAAA4EjAT3eAjaQkuXR0/ywd3T8ran8waGnX3nqVVe2PWvD6k6279c76MlkRi133yUyKWuQ6FBblZafIYVJdBAAAAPQW06ZN09q1a7VmzRq53e644zU1NRo/frzOPPNM3X///e265sSJEzV27Nhw+xdffFEzZ87UypUrVVxc3OJ527Zt02mnnaY5c+bovPPO69D7eOqpp1RYWKgf/OAHUfsXLFighQsXavPmzR263qGK/QwOZ4RCAGSahvKyk5WXnawRg/OjjnkbAqrYWdsUFu0PVxi9/a9tqq33h9u5nI0VSomejpaWEv8NCAAAAEDXOvfcc7Vy5UqtXr1aZ5xxRtzx1157TXV1dZoyZcpB3+OUU07RkiVLlJ+f33bjg/THP/5Ro0aNiguFzj//fE2YMKHL7msHhEIAWuVxOXRUvwwd1S8jar9lWdpT4w2HRNsqa1ReVaut2/fpHxu3KxhsLi/KTHM3hkQRi10XNS127XSY3f2WAAAAAFs4+eSTlZWVpaVLlyYMhZYuXarCwkKNGzfuoO+Rk5OjnJyctht2gb59+6pv3749cu8jBaEQgINiGIay05OUnZ6kYd/MjTrmDwS1fVdtVGBUVlWjf368XX//py/czjQN9c1JiVrkOlRhlJXmYbFrAAAA4BC43W6dffbZWrJkiXbv3q3s7OzwsfLycq1du1ZXXXWVDMPQu+++qz/84Q/atGmT9u/fr/79++u8887TpZdeKoej5acWJ5o+VldXp7lz5+rVV1+Vz+fTuHHjdOWVV8adu2HDBj322GNat26d9uzZo379+umMM87QtGnTlJSUJKlxqlZZWZnKysq0fPlySY0VUPfff3/C6WM1NTWaP3++Xn/9de3Zs0dFRUW68MILdemll4Z/vvjHP/6hSy65RA8//LDWrFmjFStWSJImTJigu+++WxkZGeqoDRs2aP78+Vq/fr0sy9Lxxx+vm266Sccdd1xUmwcffFAff/yx6urqlJeXpwkTJuiXv/ylJKmqqkoPPPCA1qxZoz179igrK0vf/va3dd9996lPnz4d7lN7EAoB6HROh6ni/HQV56fHHas54NO2qhqVxz0drUoN/mC4XWqSU0VNVUWRaxcV5qXJ42r5mxIAAACAZlOmTNHTTz+tV155RT/5yU/C+19++WVZlhWeOvb111/ru9/9ri6++GJ5PB6VlpZqwYIFqq6u1i9+8YsO3fPuu+/Wq6++qunTp2v48OFas2ZNwmtUVFTo2GOP1bnnnqvU1FR99tlnevjhh/X111/rwQcflCQtXLhQU6dO1ZAhQ3TttddKUouVScFgUFOnTtXHH3+s6667ToMHD9abb76pOXPmqLq6WjfeeGNU+9mzZ+vUU0/VvHnz9MUXX+i///u/5XA4NHfu3A69308++UQXX3yxjj76aM2ZM0eGYWjx4sW6+OKL9de//lXHHnusamtrdeWVV2r48OGaM2eOUlNTVVZWpg8//DB8nVtuuUXl5eW65ZZb1K9fP+3cuVPvv/++6urqOtSfjiAUAtCt0lLcOnZAjo4dEP0XeSBoqWr3gXBIFFrs+qN/79SbJdvC7QxDystKjlvsujAvTbmZyTJZ7BoAAACdbP+GN7V//aoe7UP68ROVftwpHT5v+PDhOvroo7V06dKoUGjZsmUaMWKEBg4cKEm68MILw8csy9KYMWPU0NCgJ554QjfeeKNMs33LPmzZskX/+7//qxkzZmjq1KmSpPHjx+vAgQN69tlno9pGTmmzLEujRo1SWlqabr31Vt19993Kzs7Wt771LbndbmVnZ2vEiBGt3vutt95SSUlJ1GLW48ePV11dnZ544glddtllUYHSCSecoLvuuivc7osvvtBzzz3X4QWkH374Ybndbj311FPhKqOTTjpJEydO1MKFC7Vw4UJt2bJFe/fu1c0336xjjz02fG7kotvr1q3TjBkzNHny5PC+s846q0N96ShCIQC9gsM01LdPqvr2SdXoYwuijtV5/SqvqokOjKpqtOmfW1XvC4TbedwOFTYtdh37dLSUJFd3vyUAAACgV5gyZYoeeOABffHFFxo4cKA2bNigLVu2hKctSVJlZaUWLlyod955R5WVlfL7mx8qs2vXLuXl5bXrXhs2bFAwGIwLM370ox/FhUI1NTV65JFH9Nprr2n79u1qaGgIH/vqq6+ipru1x9q1a2Waps4+++yo/ZMnT9bzzz+vdevWaeLEieH9J598clS7wYMHy+fzaefOnUpJSenQfU855ZSoaWdpaWmaOHGiVq9eLUk66qijlJGRoXvuuUcXXXSRxo4dq379+kVdZ9iwYXr88cdlWZa+853vaPDgwV2+pAahEIBeL9nj1DeLs/TN4qyo/ZZlqXpffdQ0tLKqGv172x69t6FcEWtdKyfDo6K89HBIVJSXqqL8NBVkp8jBYtcAAABoRfpxpxxUlU5vMXnyZM2fP19Lly7VjBkztHTpUrndbv3whz+U1Djt6uqrr1ZlZaWuvfZaDRo0SB6PR2+88YYWLVokr9fb7ntVVlZKUtwaOInWxJk5c6bee+89XXfddRo6dKiSk5O1YcMGzZo1q0P3DNm7d68yMzPldkc//Tg3Nzd8PFJWVvTPF6HzvF5vh0KhvXv3JgzNcnNzw/dMT0/XH//4Rz388MO69957VVtbq2OOOUbXXnttuGLqf/7nf7Rw4UI99thjuu+++5SXl6cLLrhA06ZNa3elVkcRCgE4bBmGoT6ZyeqTmazjj4n+S9jXEFBFgsWu311Xppq65v8D4XSY6pebEvV0tFB4lJHqjr0lAAAAcNgpKCjQiSeeqOXLl2v69Ol65ZVXdOqppyozM1OStHXrVpWWluo3v/mNzjnnnPB5oSqXjgg9mn7Xrl1RwcquXbui2nm9Xq1cuVLXXHONLr300vD+Tz/9tMP3DMnMzNTevXvl8/migqGdO3eGj3eFzMzM8D0i7dy5M+qeQ4cO1YIFC+T3+1VaWqrf//73uuGGG7Rs2TINHjxYffr00T333KN77rlHW7Zs0dKlS7VgwQLl5OTooosu6pK+EwoBOCK5XQ4N6JuhAX2jnxxgWZb21fq0rbImPCVtW2Xjrw827ZA/0FxelJ7iDlcURT4drV9uqlxOFrsGAADA4ePcc8/VTTfdpPnz52v37t0699xzw8fq6+slSS5X85ILDQ0N4ad9dcRxxx0n0zT16quvhtcUkhR+wleIz+dTIBCQ0xkdS7z00ktx13S5XO2qHBo7dqwef/xx/e1vf4tal2f58uVyuVxtrkl0sE444QS9/fbbqqmpUVpamqTGqXGrV6/W2LFj49o7nU6NGDFC119/vVatWqXPP/9cgwcPjmozaNAg3XjjjXr22Wf12WefdUm/JUIhADZjGIYy0zzKTPPo24OiS1gDgaB2VB8IL3IdCoxKPqnUyrVfh9uZhlSQ0xwWNRyo0T59rSS3Q0luZ+Mvj0Met0PJbqc8boc8bq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TNmTPHdYMq/Ht5U/vc3FwdOnRIdrtdwcHBysrK0tixYwOdOm5AUVGRdu3apZaWFg0YMEDh4eGqrKxkzVuAN7VnzZvPkSNHlJqaqtjYWN16662SpJiYGJWVlWnq1Kl699135XA41NnZqby8PNXV1clutys7O1uTJk0KcPa4Ed7WftasWWptbZXNZtNtt92mnJwcjRw5MsDZ40a0tLQoMzNTXV1dstvtCgsLU25uroYPH85+b3Le1p793twmTJigt99+W0OHDrXUXk9TCAAAAAAAwIK4fAwAAAAAAMCCaAoBAAAAAABYEE0hAAAAAAAAC6IpBAAAAAAAYEE0hQAAAAAAACzolkAnAAAA4Avl5eXKz8+/4mP9+vXTt99+6+eMLsrLy9O+ffu0d+/egLw+AADA1dAUAgAAprJq1SpFRUW5zQUFBQUoGwAAgH8umkIAAMBUEhISNGTIkECnAQAA8I/HPYUAAIBllJeXa9iwYaqtrVVmZqZGjRqlxMREvfLKKzp//rxbbHNzs3JycpSYmCin06kpU6Zo69atHs954sQJZWdnKykpSU6nUxMnTlRRUZFH3OHDhzVjxgyNGDFCjz76qDZu3Oj2+B9//KHc3FwlJyfL6XQqOTlZ8+bNU2trq2/fBAAAgP/iTCEAAGAqPT09+vPPP93m7Ha77Pb/fReWnZ2tyZMna8aMGTpw4IDeeustdXV1admyZZKkzs5OzZw5U+3t7Vq0aJGioqL0ySefKCcnR+fPn9f06dMlXWwIpaenq2/fvsrKytKQIUPU2Nio6upqt9c/d+6cFi9erGeeeUYvvPCCysvLtXTpUsXFxWn06NGSpJycHDU0NCgnJ0fR0dFqaWnR/v371dXVdTPfLgAAYGE0hQAAgKlMnjzZY27cuHF65513XONHHnlEubm5kqTk5GTZbDatXr1a8+bNU1xcnMrLy3X8+HGtX79eiYmJkqSxY8eqtbVVJSUleuqppxQUFKTS0lJduHBBW7dulcPhcD3/E0884fb6HR0dKigocDWAHnzwQVVXV6uystI198MPP2jhwoVKS0v7y/8FAADAV2gKAQAAUykrK3Nr0EhS//793caXN1sef/xxlZSU6MCBA4qLi1Ntba0cDoerIXRJWlqa8vPz9csvv2jYsGGqqanRuHHjPF7vcn379nU1fyQpJCREsbGxamhocM05nU6tW7dOhmFo9OjRGjp0qGw229/63wEAAP4OmkIAAMBU7r333mveaPr22293G0dEREiSmpqaJEnt7e2KjIy86t+1t7dLkk6fPu3xS2dXcnlTSrrYGOru7naNS0pKtGbNGq1du1avv/66IiMjlZGRoczMTLdL3wAAAHyFTxgAAMByWlpa3MaXbuZ86YyfsLAwj5j//7uwsDBJ0oABA1yNpBsVERGhgoICVVVVaefOnXryySdVWlqqTZs2+eT5AQAALkdTCAAAWM7OnTvdxpWVlbLb7RoxYoQk6aGHHtLJkyf13XffucVt375dERERuueeeyRJSUlJ+vLLL9Xc3OzT/OLj47Vo0SKFhYXpyJEjPn1uAACAS7h8DAAAmEpdXZ3a2to85p1Op+t47969Ki4uVnJysg4cOKCysjJNmzZNsbGxki7eKHr9+vVasGCBFi5cKIfDoW3btqmmpkaFhYUKCgqSJC1YsEBfffWVMjIyNH/+fN11111qampSVVWVVq5c6XXOZ8+e1axZszRlyhTFx8crODhYX3zxhdrb25WUlHRjbwgAAMBV0BQCAACm8uKLL15xfv/+/a7jFStW6P3339emTZsUHBys9PR016+RSVJoaKg2bNigFStWaOXKlero6FBcXJyWL1+uqVOnuuJiYmL00UcfqaSkRG+88YY6OzvlcDg0ceLEv5Vznz59NHz4cG3ZskUNDQ2y2WyKi4vTypUrNWnSpL/5DgAAAHjHZhiGEegkAAAA/KG8vFz5+fnatWvXNW9GDQAAYHbcUwgAAAAAAMCCaAoBAAAAAABYEJePAQAAAAAAWBBnCgEAAAAAAFgQTSEAAAAAAAALoikEAAAAAABgQTSFAAAAAAAALIimEAAAAAAAgAXRFAIAAAAAALCg/wD3zQ08ji6O0QAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 1440x1080 with 3 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Fold: 4\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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cAAOiKwpaYNfE7r+PtV2Hm7rLO7kKH4BFBAAAAAAAAFkQoBAAAAAAAYEGEQgAAAAAAABZEKAQAAAAAAGBBhEIAAAAAAAAWRCgEAAAAAABgQYRCAAAAAAAAFkQoBAAAAAAAYEGOzu6AJJmmKUnyeDyd3JO2U1tb29ldQCdh7K2Jcbcuxt66GHvrYuyti7G3Lsbemk6kcXe5XDIMI2q7YTYkMp2osrJS//nPfzq7GwAAAAAAACecIUOGyO12R23vEqGQ3+9XdXW1nE5nzOQKAAAAAAAAR6dLzxQCAAAAAABAx6LQNAAAAAAAgAURCgEAAAAAAFgQoRAAAAAAAIAFEQoBAAAAAABYEKEQAAAAAACABREKAQAAAAAAWBChEAAAAAAAgAURCgEAAAAAAFgQoRAAAAAAAIAFEQoBAAAAAABYEKEQAAAAAACABREKAQAAAAAAWBChEAAAAAAAgAURCgEAAAAAAFgQoRAAAAAAAIAFEQoBAAAAAABYEKEQAAAAAACABREKAQAAAAAAWBChEAAAAAAAgAURCgEAAAAAAFgQoRAAAAAAAIAFEQoBAAAAAABYEKEQAAAAAACABREKAQAAAAAAWBChEAAAAAAAgAURCgEAAAAAAFgQoRAAAAAAAIAFEQoBAAAAAABYEKEQAAAAAACABREKAQAAAAAAWBChEAAAOO6NGzdOH374YWd3AwAA4LhCKAQAANBGCgsLNXjwYNXV1R3TeQi5AABARyAUAgAA6CKONUwCAACIB6EQAAA4YXg8Hs2ePVtjxozRmDFjNHv2bHk8HklSeXm5Jk+erJEjR2rUqFG65ppr5Pf7JUnLli3T2LFjNXz4cF100UX66KOPmr3O5s2bdcUVV2jEiBE6++yzNWfOHEnStddeK0k666yzNHz4cH366afavXu3rrvuOo0ePVqjR4/WXXfdpUOHDgXPNW7cOC1btkzjx4/XsGHDdOedd6q4uFg/+9nPNHz4cD3xxBPtcasAAADk6OwOAAAAtJUlS5Zo06ZNWrVqlQzD0JQpU7R48WLdcccdWr58ubKzs4OBz6ZNm2QYhnbs2KG//OUveumll5Sdna3CwsJgWNSU2bNn67rrrtPEiRNVXV2tbdu2SZKeffZZXXDBBdqwYYMcjsDXrF27dmny5Mk666yzVFVVpdtuu00LFy7U/fffHzzf66+/rmXLlikjI0MJCQnauHGjfv3rX+vss89upzsFAADATCEAAHACWb16taZOnaru3bsrMzNTU6dO1WuvvSZJcjgc2rt3r4qLi+V0OjVy5EgZhiG73S6Px6Pt27fL6/UqNzdXJ510UrPXcTgc2r17t8rLy5WcnKxhw4Y12bZfv34655xz5HK5lJmZqZ/85CfasGFDWJv/9//+n3r37q2EhIRjvwkAAACtRCgEAABOGGVlZerTp0/wfZ8+fVRWViZJuvHGG9WvXz/99Kc/1QUXXKBly5ZJCoQ29913nxYuXKizzz5b06dPV2lpabPXmT17tnbu3KlLLrlEP/zhD7Vu3bom2+7bt0/Tp0/X2LFjNWLECN199906cOBAWJvevXsf7UcGAAA4aoRCAADghJGVlaXi4uLg+5KSEmVlZUmSUlJSdO+992rNmjVasmSJli9fHlxKNn78eD3//PNat26dDMPQvHnzmr1O//79tWDBAn300Ue6+eabNW3aNB0+fFiGYUS1XbBggQzD0OrVq/XJJ5/ot7/9rUzTDGsT6zgAAID2RigEAABOGD/4wQ+0ZMkSlZeXq7y8XI8//rjGjx8vSVq3bp127dol0zSVmpoqu90erCn00UcfyePxyOVyye12y2Zr/ivSqlWrVF5eLpvNprS0NEmSzWZTZmambDab9uzZE2xbXV2tpKQkpaamqrS0VE8++WSLn6NHjx5h5wAAAGgPhEIAAOCEMWXKFA0ZMkQTJkzQhAkTdPrpp2vKlCmSAgWff/KTn2j48OH60Y9+pKuvvlrf+ta35PF4NH/+fI0ePfr/s3fn8VFV9//H33dmsrMlkAAJiCImWImCRG0roARcWURb6/pVtGwCLuBPBRtFcAFahVYQEBfcK19cQL5uVMDduqRliShuKJqAEDBIQshkZu7vj2QmM5klE0jIcl/Px6OPR1s/lAAAIABJREFUzNx77p0z95gy8845n6sBAwZo7969mjp1asTXee+99zRs2DD169dP9957r+bPn6/4+HglJCRowoQJuuyyy5STk6MNGzZo8uTJ2rJli3JycjRu3DidffbZdb6PcePGafHixcrJydFjjz3WINcGAACgNsOsPX8ZAAAAAAAArR4zhQAAAAAAACzI0dQdAAAAaI7GjBmj/Pz8oO3jx4/XhAkTmqBHAAAADYvlYwAAAAAAABbULJaPmaapioqKoNuzAgAAAAAAoHE0i1DI6XSqoKBATqezqbvSID7//POm7gKaCGNvTYy7dTH21sXYWxdjb12MvXUx9tZklXFvFqFQa3Pw4MGm7gKaCGNvTYy7dTH21sXYWxdjb12MvXUx9tZklXEnFAIAAAAAALAgQiEAAAAAAAALIhQCAAAAAACwIEIhAAAAAAAACyIUAgAAAAAAsCBCIQAAAAAAAAtyNHUHAAAAAACt16+//qpdu3apsrKyqbtySBwOh7744oum7gaOsJYw7jExMUpLS1O7du0O+RyEQg3sYIVLByo8Td0NAAAAAGhyv/76q37++WdlZGQoISFBhmE0dZfqraysTElJSU3dDRxhzX3cTdNUeXm5CgsLJemQgyFCoQZ268L3tK3oVy1+/XVlpCYpPbWN0lOTlJHaRumd2ii9U5Li47jsAAAAAFq/Xbt2KSMjQ4mJiU3dFaBVMQxDiYmJysjIUFFREaFQczHlspP1f+v+KyOug3YUl2nj17u17rMfA9p0bB9fFRD5wqKq8KhLxyTFOCjzBAAAAKB1qKysVEJCQlN3A2i1EhISDmtpJqFQAzsmvb1+f3xb9e/f17ftYIVLO/aUqXB3qQp3l6pod5mKdpfqw007tP+A09fOZkhpKYlVs4s6VQdG1Y9TkxNlt7W8qZYAAAAArK0lLhkDWorD/f0iFDoC4uMcOia9vY5Jbx+0b/8Bp4p2l6qouKwmMCou1Rfb9qi8wu1r57Db1LVTUmBYVD3TKLltHP9HCwAAAAAA6oVQqIm1TYxVVo8UZfVICdhumqZ+2V+hot2lKqyeWVRUXPU4/8tdcrlrilknxNnVtVObgKVo3npGbRNjj/RbAgAAAAAALQChUDNlGIZS2sUrpV28+hzbKWCf22OquKRchbtLtWN3qQqrZxl982OJPthYKI9Z07ZtYiwFrwEAAADgMGVlZdXZJiMjQ+vWrTvk13jppZc0ffp0rV27Vt26dTvk83gtWLBACxcu1NatWw/7XEfaxx9/rE8++USTJk2SzdawtXffeust/fjjj7rmmmsa9LwtEalAC2S3GeqckqjOKYlSVlrAvkqXRzv3lGlHcU0No2gKXqd3qpldRMFrAAAAAAi0fPnygOeTJ09WVlaWrr/+et+22NjDW6lx5plnavny5UpLS6u7cSv3ySefaOHChbruuusaJRT68MMPCYVEKNTqxDhs6t65rbp3bhu0z7/gddFu789SfbR5h34to+A1AAAAAITTt2/fgOexsbFKTk4O2u7P7XbLNE05HNF99U5JSVFKSkrdDXFInE7nYQd3rQ2hkIU0TsFrbw0jCl4DAAAAsLasrCxNmDBBSUlJev7557Vjxw69+OKLOvbYY/XAAw/oww8/VGFhoRITE5Wdna1bbrlFxx57rO/4UMvHcnNzdfLJJ2vw4MFauHChduzYoZ49e+r2229XTk5OvftYWlqqefPmac2aNSopKVFGRoYuu+wyXX311b7vc2VlZZo3b57Wrl2r4uJitW3bVllZWbrjjjt8/X3yySf1/PPPq7CwUHFxcerevbuuu+46nXXWWVH1Y9OmTZo/f762bNmi8vJypaamauDAgbrrrrt8y94k6YQTTvAd410G9+CDD2r9+vX64YcfFBMTo6ysLE2dOjUgoPv444911VVXacGCBXr33Xf11ltvyeVyaejQoXr55Zd94yUd/rK/loxQCJKiL3i9o7i0ellamf6zdZcqXRS8BgAAABC9dZ9t178+2d6kfTjr1KOUm3NUo5z7pZdeUvfu3XXbbbcpISFBaWlpcjqdKisr03XXXafU1FTt27dPzz33nC699FK99tprSk1NjXjO/Px8bdu2TTfeeKPi4uL0j3/8QxMmTNC6devUrl27qPvm8Xg0btw4bdmyRTfccIMyMzP19ttva/bs2dq7d6+mTp0qSZo9e7bWrVunKVOm6Oijj1ZJSYn+85//aP/+/ZKkV155RXPnztXEiROVk5OjiooKbd26VSUlJVH1o6ysTGPGjFF2drZmz56tpKQkFRYW6r///a8k6eKLL9bOnTv1wgsv6LnnnpPdbg84/ueff9bVV1+tLl26qLy8XK+88oquvPJKvfjii0G1n+6++24NGjRIf/3rX+V0OpWZmam9e/dq8+bNWrx4saTDX/bXkhEKIaLGKnid7hceUfAaAAAAQGthmqYef/xxxcfHB2y/9957fY/dbrcGDBig3//+93r11Vc1evToiOcsLS3VypUr1b591aqPTp066Y9//KPeeecdjRgxIuq+vfPOO8rPz9fs2bN10UUXSZIGDBig8vJyPf744xo9erRSUlK0YcMGjRgxQhdffLHvWP8ZQBs2bFBWVpYmT57s23bGGWdE3Y/vvvtO+/bt0y233KLevXv7tnv71KVLF3Xp0kWSdNJJJwUtv6t9LQcOHKhhw4ZpxYoVysvLC2h74oknBrSXqpbpxcTERFz6ZxV8G8chq0/B66LiMhXtLg1Z8DqlXXzNUjQKXgMAAACtWm5O483SaQ4GDhwYFAhJ0muvvaZly5Zp27Ztvhk3UlVAUpe+ffv6AiGpZtnTjh076tW3Tz/9VDabTcOHDw/YPnLkSL3wwgvasGGDcnNzlZ2drZdfflnJyck6/fTT9Zvf/CZgtk52draee+453X333RoyZIj69eunhISEqPtx9NFHq127dpoxY4Yuv/xynXrqqeratWvUx3/44YdavHixvvrqq4DZSaHu2BbtcjarIhRCo6hvwesdxWXRFbyuvlsaBa8BAAAANEeh7hzmXYp14YUXavLkyUpOTpZhGBo3bpycTmeIswTyD4SkmuVOFRUV9erbvn371L59+6DlUp06dfLtl6S8vDx16tRJL774oubPn68OHTroggsu0JQpU5SQkKBRo0apoqLCt7zL4XDojDPO0LRp00IGM7W1bdtWTz31lBYtWqSZM2eqrKxMxx13nK6//nqdc845EY/9/PPPNW7cOA0YMED33nuvUlNTZbPZlJeXF/Ja1rU0z+oIhXDERSp4XXrA6St2XbUsrUyFYQteJ9YsQ6PgNQAAAIBm6tVXX1WPHj00Z84c37bKykpfCHOktG/fXvv27Qu6C1dxcbFvvyQlJSXp5ptv1s0336zCwkK9+eabeuCBBxQTE6NbbrlFhmHo0ksv1aWXXqp9+/bpgw8+0Jw5czRlyhStWLEiqr4cf/zxWrBggVwulwoKCvTwww/rpptu0qpVq5SZmRn2uDVr1shut2vBggWKiYnxbf/1119D1lfiu2FkhEJoVtokxirzqFhlHpUcsN00TZXsr/AVufYWvC4qjq7gdXpq1UwjCl4DAAAAONIOHjwYVCx51apVcrvdYY5oHKeeeqoee+wxvfHGGxo5cqRv++rVq8PW2MnIyNC1116r1atX6+uvvw7a3759e51//vnauHGjli9fXu8+ORwO9e3bVzfeeKPWrVunb7/9VpmZmb7Q6uDBg2rTpo2vfXl5uWw2W0DY89FHH6moqCiqWUpS1Uyr+s6yaq0IhdAiGIah5HbxSo5Q8Lpod2nVXdKq6xeFK3jtDYgoeA0AAADgSBg4cKDeeust3XfffRo8eLA2b96sZ555pl53DmsIgwYNUv/+/TVjxgzt3btXxx13nN555x2tWLFC48ePV0pK1d2oL7nkEuXm5iozM1OJiYn69NNP9eWXX2rUqFGSpDvuuENJSUnq27evOnbsqO+//16rVq3S6aefHlU/1q9fr+XLl2vo0KHq1q2bysvL9fTTTyspKUn9+vWTJB177LGSpGXLlmnQoEGy2WzKzs7WwIED9eSTT2ratGn6wx/+oG3btmnRokXq3Llz1Nfh2GOPVUlJiZ577jn16dNHcXFxQXctswq+BaPF8y943S9Eweuf95b5ahd5C15vqlfB60TFOAJTfQAAAACI1p/+9Cft2LFDL774opYvX67s7GwtWbIk4O5dR4LNZtPSpUs1b948PfrooyopKVFGRoamT5+uq6++2tcuJydHr7/+upYuXSq3263u3btr+vTpuuqqqyRJJ598sl566SWtWrVK+/fvV1pamkaOHKkbbrghqn706NFD8fHxWrRokXbv3q2kpCRlZ2dr2bJlvruODR48WJdffrmee+45PfTQQzJNU1u3btXAgQOVl5enZcuWac2aNTruuOP017/+1Xd7+WhcfPHF2rhxo+bPn69ff/1VGRkZWrduXT2uZOthmKZp1t2scVVUVKigoMCX0LV0+fn56t+/f1N3A3XwFrz2BkbegteFu0tDF7zuVLMMLVzBa8bemhh362LsrYuxty7G3roY+0PzxRdf6Pjjj2/qbhyWsrIyJSUlNXU3cIS1pHE/nN8zZgrBsg6p4PX3e1Ve4fK1q13wuvJAqWI7FCu9U5JS2sVT1AwAAAAA0GzVGQr98ssvuvXWW7V9+3bFxsaqR48emjVrlm+toVd5ebmmT5+uzz//XHa7XbfddpsGDx7caB0HGlM0Ba+9S9FqF7xe/ckHkmoKXqd38tYwouA1AAAAgNbNNM06C2g7HMxPaS7qHAnDMDRmzBiddtppkqS5c+fq/vvv13333RfQ7rHHHlObNm30r3/9S99//72uuOIKrVmzpsVMtwKiUVfB6/XvfaKOXY6pKnpdPdPo25/26cPNO+Txq3gdruB1105JSqDgNQAAAIAW6uWXX9b06dMjttm6desR6g3qUue3zw4dOvgCIUnq27ev/vnPfwa1e/311zVnzhxJ0tFHH60+ffro3Xff1XnnndeA3QWaL7vNUHIbh/plpUUseF1UXKrC3RS8BgAAAND6DB48WC+88EJTdwNRqteUBI/Ho3/+85/Kzc0N2ldUVKSMjAzf865du2rnzp2H30OgFYhx2NQtra26pbUN2le74HVRcamKdpfpo807DrngNQAAAAA0heTkZCUnJ9fdEM1Cve4+NnPmTP38889auHChbDZbwL5+/fpp7dq1vlpDd911l3r06KFrrrmmzvN67z4GIFC506M9v7q0Z3+l9u53ac9+V/Vzl5yuml9du01KbuNQx3YOdWzr/V+MUto61DbBRsFrAAAANAmHw6FevXo1dTeAVu2bb76Ry+UKuz/SnROjnik0d+5c/fDDD1qyZElQICRJ6enpKiws9IVCO3bsCFh2Fg1uSY+W7kiNvbfgtbdukX8No0+/LlOly+NrGx9rrypyTcHrRsPvvHUx9tbF2FsXY29djP2h+eKLL1p8ndmWdGtyNJyWNO6xsbE66aSTDunYqEKhefPmqaCgQEuXLlVsbOgvkeeee66WL1+u7Oxsff/999q8ebMeeOCBQ+oUgMj8C16f0LNjwD63x9SekvKgsCh0wesYpae2qV6KluR7TMFrAAAAAGj96vzW9/XXX+vhhx/W0UcfrUsvvVSS1K1bNz300EO64IILtHTpUnXu3Fl//vOfNW3aNJ111lmy2WyaNWuW2rRp0+hvAEAgu81QWkqi0lISwxe8Lq4qdE3BawAAAACwrjpDoeOOOy7s7eJWrVrle5yYmKgHH3yw4XoGoMFFLHjtdGlHcXQFr1OTE2sFRhS8BgAAAICWhvUhACRJ8bEOHZPeXsektw/aV3rAGTS7qKi4VF98v1flFTUFzRx2m7p28t4hrWZ2UXqnJKW0i6fgNQAAAAA0I4RCAOrUJjFWmUfFKvOowFtLRip4/Z+tu4ILXneqKXKdnlpTw4iC1wAAAGjuJk6cqE8//VQffPBByFq7paWlGjBggM4991zNmTMnqnPm5ubq1FNP9bV/6aWXNH36dK1du1bdunULe9xPP/2kIUOGaPbs2brooovq9T6eeOIJpaen6+yzzw7YvmDBAi1cuDDsSqHGUvsatCThrmVDWLBggXJycvS73/2uwc/tj1AIwCGLuuC1b5ZRqb4tpOA1AAAAWp4LL7xQa9eu1fr163XOOecE7X/zzTdVXl6uUaNGHfJrnHnmmVq+fLnS0tLqbnyInnrqKZ188slBQcbFF1+sgQMHNtrrtkbhrmVDWLhwoSZMmEAoBKBlCix4Hbiv0uXRrl8O1Mwuqq5jFK7gtW92kd9MIwpeAwAA4Eg644wz1KFDB61cuTJkKLRy5Uqlp6frtNNOO+TXSElJUUpKyuF085B16dJFXbp0aZLXRg2n0xn2ru+NgVAIwBEX47Apo3o2UG3+Ba+Likurg6My/btgh/aVUvAaAAAATSM2NlbDhw/X8uXL9csvvyg5uaa0QlFRkT799FONHz9ehmHo/fff15NPPqkvvvhC+/fvV/fu3XXRRRfp6quvlt0e/g+boZaPlZeXa+7cuXr99dfldDp12mmnacyYMUHHbtq0SY8++qg2bNigkpISde3aVeecc44mTpyo+Ph4SVVLtQoLC1VYWKjVq1dLqpoBNWfOnJDLx0pLSzVv3jytWbNGJSUlysjI0GWXXaarr77aVy/0448/1lVXXaVFixbpgw8+0KuvvipJGjhwoO688061a9eu3td606ZNmjdvnjZu3CjTNHXSSSfp5ptv1oknnhjQZv78+dqyZYvKy8uVmpqqgQMH6q677pIk7d69W/fff78++OADlZSUqEOHDjrhhBN03333qWPHjmFeOdCTTz6p559/XoWFhYqLi1P37t113XXX6ayzzop4LX/44QctXLhQ+fn5Ki4uVmpqqgYMGKCpU6eqffuaGq7Tpk3Thx9+qH/84x+aO3eutmzZoj/96U96+umnJUlLlizRkiVLJEmTJ0/W9ddfX+9rWRdCIQDNSsSC1+WV1TOLqgteF1c9jqrgdfUsIwpeAwAA4FCNGjVKzzzzjF577TVdccUVvu2vvPKKTNP0LR378ccf9bvf/U5XXnml4uLiVFBQoAULFmjv3r36f//v/9XrNe+88069/vrrmjRpkrKzs/XBBx+EPMeOHTvUu3dvXXjhhUpKStLXX3+tRYsW6ccff9T8+fMlVS1JGjdunLKysnwBQ7iZSR6PR+PGjdOWLVt0ww03KDMzU2+//bZmz56tvXv3aurUqQHt7733Xg0ePFgPPPCAtm3bpr/97W+y2+2aO3duvd7vl19+qSuvvFK9evXS7NmzZRiGli5dqiuvvFL/+7//q969e6usrExjxoxRdna2Zs+eraSkJBUWFuq///2v7zy33nqrioqKdOutt6pr164qLi7WRx99pPLy8qj68dprr2nu3LmaOHGicnJyVFFRoa1bt6qkpKTOa7lr1y517dpVt99+u9q3b68ff/xRDz/8sMaNG6fly5cHvM7+/fs1depUXXvttZoyZYri4+M1fPhwXXLJJbrooot0ySWXSFKjzeIiFALQYrRJiFHmUcmhC16XVviWoVHwGgAAoPnav+lt7d+4rkn70PakXLU98cx6H5edna1evXpp5cqVAaHQqlWr1LdvXx1zzDGSpMsuu8y3zzRN5eTkqLKyUo8//rimTp0qm80W1et99913+r//+z9NmTJF48aNkyQNGDBABw4c0PPPPx/Q1n9Jm2maOvnkk9WmTRvddtttuvPOO5WcnKzf/OY3io2NVXJysvr27Rvxtd955x3l5+cHFLMeMGCAysvL9fjjj2v06NEBgdIpp5yiO+64w9du27ZtWrFihebMmVOvP8ouWrRIsbGxeuKJJ3yzjE4//XTl5uZq4cKFWrhwob777jvt27dPt9xyi3r37u071r/o9oYNGzRlyhSNHDnSt+28886Luh+bNm1SVlaWJk+e7Nt2xhln+B5HupannHKKTjnlFN/zfv366aijjtIVV1yhLVu26De/+Y1v34EDB/S3v/1NQ4cODepDWlpaneN0uAiFALR4hmEouW28ktsGF7z2eEwVl5RXL0Ur8wVGkQpep3fyBkY1ha8peA0AAACparbQ/fffr23btumYY47Rpk2b9N133/mWLUlVM0UWLlyo9957T7t27ZLLVTOrfc+ePUpNTY3qtTZt2iSPxxMUZgwbNiwoFCotLdXixYv15ptvaufOnaqsrPTt++GHHwKWu0Xj008/lc1m0/DhwwO2jxw5Ui+88II2bNig3Nxc33b/wESSMjMz5XQ6fcun6vO6Z555ZsCyszZt2ig3N1fr16+XJB199NFq166dZsyYocsvv1ynnnqqunbtGnCePn366LHHHpNpmvrtb3+rzMzMeoVTJ5xwglasWKG7775bQ4YMUb9+/ZSQkBDVsU6nU48//rhWrlypoqIiVVRU+PZt27YtIBSKiYnR4MGDo+5XQ+NbDoBWzeZX8LpvZuC+cAWvN39TrPX5PwW0jVTwGgAAANFre+KZhzRLp7kYOXKk5s2bp5UrV2rKlClauXKlYmNjdf7550uqWnZ13XXXadeuXbr++uvVs2dPxcXF6a233tKSJUsCAoK67Nq1S5KCauCEqokzffp0ffjhh7rhhht0/PHHKyEhQZs2bdKsWbPq9Zpe+/btU/v27YOKHnfq1Mm331+HDh0CnnuPq+9r79u3L2SI1KlTJ99rtm3bVk899ZQWLVqkmTNnqqysTMcdd5yuv/5634ypv//971q4cKEeffRR3XfffUpNTdWll16qiRMnRjVTyxuGvfDCC3ruuefkcDh0xhlnaNq0ab56T+HMmzdPzzzzjCZOnKh+/fopKSlJP//8syZPnhx0PZKTkyPWmWpshEIALCuqgtfFZdU1jMIXvG6TYFeHdesUF2NXXKy91k+H73lsjE1xMY4QbcL8jLHLbo9uajEAAACOjM6dO+v3v/+9Vq9erUmTJum1117T4MGDfQWEt2/froKCAv31r3/VBRdc4DvOO8ulPry3pt+zZ48SE2v+GLlnz56AdhUVFVq7dq0mT56sq6++2rf9q6++qvdrerVv31779u0LuhtWcXGxb39jaN++ve81/BUXFwe85vHHH68FCxbI5XKpoKBADz/8sG666SatWrVKmZmZ6tixo2bMmKEZM2bou+++08qVK7VgwQKlpKTo8ssvr7MfhmHo0ksv1aWXXqp9+/bpgw8+0Jw5czRlyhStWLEi4rGvvvqqLrjgAk2cONG37d///nfY12lKhEIAEEJ9Cl5/8c2PSmrbRhVOtyoq3fq1zKmKErfvufen/1K1aDnshl9QFBgoxdYKkCKHTDXHxsbYaoVVdu7WBgAAUA8XXnihbr75Zs2bN0+//PKLLrzwQt++gwcPSqpaFuRVWVnpu0NVfZx44omy2Wx6/fXXfTWFJPnu8OXldDrldrvlcAR+xX/55ZeDzhkTExPV7J1TTz1Vjz32mN54442AujyrV69WTExMo9W6OeWUU/Tuu++qtLRUbdpU/fG2tLRU69ev16mnnhrU3uFwqG/fvrrxxhu1bt06ffvtt8rMDFwi0LNnT02dOlXPP/+8vv7663r3qX379jr//PO1cePGgELR4a7lwYMHg8bipZdeqtdrRjtOh4tQCADqqXbB6/z8MvXv3z/iMaZpyuU25awMDIoqnK5azwN/OiPsK9l/MKjtQadbZv2zJ8U4bCEDpdg6Zj/V3dYvxHLYZSN8AgAArcDQoUPVpk0bPfHEE+rYsaMGDhzo29ezZ09lZGRo/vz5stlscjgcevLJJw/pdXr27Knhw4frwQcflMfjUXZ2tt5//329++67Ae3atm2rvn37atmyZUpLS1NycrJefPFF/fzzz0Hn7NWrlz777DOtX79enTp1UnJycsjlUIMGDVL//v01Y8YM7d27V8cdd5zeeecdrVixQuPHjw9717LDNXHiRL399tsaPXq0xo4dK8Mw9Mgjj6i8vFyTJk2SVDXravny5Ro6dKi6deum8vJyPf3000pKSlK/fv20f/9+jR49WiNGjFDPnj0VExOjtWvXat++fTr99NOj6sfdd9+tDh06qG/fvurYsaO+//57rVq1KuD4cNdy4MCBWrlypTIzM9WjRw+tWbMm4M5o0ejVq5fefvttDRw4UO3atVNaWpo6d+5cr3NEg1AIAI4AwzAU4zAU47ApKSGm7gMOUVX45AkdJNXeFmU4VVpeGSLMch9S/2K9s5pibCFnP9UvbKo+ttbsqViHrcmn4QIAgNYtPj5e5513nlasWKHhw4cHzAqJjY3VQw89pFmzZum2225T+/bt9Yc//EHp6enKy8ur92vNmjVLiYmJevzxx1VZWanTTjtN999/f9ASqAceeEB33XWXZs6c6evfX/7yF40fPz6g3dSpU3XHHXfopptu0sGDB3XhhRdqzpw5Qa9rs9m0dOlSzZs3T48++qhKSkqUkZGh6dOnByxRa2i9e/fW008/rfnz52vatGkyTVMnnXSSnnnmGd+dxnr06KH4+HgtWrRIu3fvVlJSkrKzs7Vs2TJ16dJFTqfTVyi6qKhIhmHomGOO0f333x/yLl+h9O3bV6+++qpWrVql/fv3Ky0tTSNHjtQNN9zgaxPuWubl5ck0Tf3973+XVBWwPfDAA7r44oujvg533HGH7r33Xk2YMEFOp1OTJ0/W9ddfX48rGR3DNA/lb8oNq6KiQgUFBerTp4/i4uKaujuHLT8/v85ZA2idGHtrsuK4m6Ypp8vjFza5oprdVPdMKFfAc6fLU+++GYZf+BSiTpM3UIr1BVNhZj+FPbbqp8Nu03/+8x/LjT2qWPH3HlUYe+ti7A/NF198oeOPP76pu3FYysrKlJSU1NTdwBHWksb9cH7PmCkEAKg3wzB8QYka8d9KjyfEkruwIVNql+TpAAAgAElEQVStQClE2wMVLv2yvyJou8td//DJZlTVfEp8Zbdi6wiQwi6pC2obHE45KDYOAACARkIoBABotmw2Q/FxDsXHNe4/V25v+FTPWk/bfypSh+ROIZbcObVnX/BsKJe7/pNz7TYjTIDEne4AAAAOhdvtVqRFU9Hcsr61IBQCAFie3WYoIc6hhHqGT/n5B9S//0lRt3e5PZGX19Ux+6n2z5B3unO6dAg3uuNOdwAAwDJGjx6tTz75JOz+Cy+8UHfccccR7FHTIRQCAOAIcdhtcthtSoxv7GLjZtCMp1DL6cL9rN229p3uvD+50x0AAGiJZs6cqbKysrD7k5OTj2BvmhahEAAArYj/ne7aNPKd7ipdntAFw+ua7RThTneBhcab753unC6P3G6PbDaDu90BANDC9OzZs842kUKj1oRQCAAA1JthGIqtDlIaU6Q73dV9N7vggOqg06V9ZRVBbQ/lTnf63yIZhhRjt8nhqJoFFlP90/fYYavab7fJYTcU47DL4TAC2niP9z828Hij+rldDrsR9Fq1f/of67DbWLIHAADCIhQCAADNVpPe6S5Cradt329X5y7pcrk8crk9qnR5VOn2BDx3uT1yuU25qvcdOOhWpdsZuN/l39ZzSMXI62IzJIfDXhUu+YVQAQFSmHDJ4TBqwqhaIVZwW7/9/vscRp2vw3I/oHUzTZNZlUAjiVQwOxqEQgAAwPLqe6e7/MQS9e+f1eD9qKoJ5Q2KzLABkjeA8g+ifG1dHlWGOtY/tPJr63/sQadLLpcZeP5a53EfSiXzOthtfjOgvAFTiEDJEbCv6mdN+FQ7bDLCh1h+z2NqhVreY337HTbZbQRXwKGKiYlReXm5EhMTm7orQKtUXl6umJhDLxlAKAQAANBMVNWEsivG0bjL8g6Hx2MGhlC1ZkZVutxyucyw4VVlyBDL79jq2VUB5/cLxcorXEHHe4/1hmGeRgiuvEGR5FH86t2By/UCluwZQQFUQNBVa3lf4OwpQzH24CWG/oFZpBCMmRhojtLS0lRYWKiMjAwlJCTw3ynQQEzTVHl5uQoLC9W5c+dDPg+hEAAAAKJmsxmKtTV+PanD4fYGV7UCqOAQy1M1M6o6jIo4A6v6Z9GOnUpO6VRzrLsmBHO5PXJWelR20BViaWBgCHaYs/1D8gZLtcOmoDpUIWZG+desqh1i1a55FbgEMHCJoX/b2iGWncLsltSuXTtJUlFRkSorK5u4N4fG6XQqNja2qbuBI6wljHtMTIw6d+7s+z07FIRCAAAAaFXsNkN2W3UtqgaWn1+h/v37HvZ53J7wM6Nqh1i1l/AF1rEKDqZqh1+Bx5pVywTLa82wCnFMQzMM1SqmbtTUu6o1qypyiBVYuL3OECtsCBZYNyumur4VwVXDa9eu3WF9aW1q+fn5Oumkk5q6GzjCrDLuhEIAAADAEWa3GbLHNt+P4qZpyuMxay3h8wZQwcv9KoNmYAUvG6z0C6Z8x/q19T++vMKl/QeCi7FXuky53O7qn40TXIUrxu50HlTS+vWyGYZstqpZczbDkN1uq9lmGFXbq/d5H9vDbLcZCthuD9mm9jaFPV+k44OPUci2Efvg12e733aCNKDlar7/EgEAAABoEoZhyG6vCjyaK9M0q2dcBQdRoZcC1tS8ClVEPVIx9kqXR3v2utW+faI8HsljmnK7PfKYpu+5q7IqSHNXB2oej1m9P9zjqhljvu219jfGEsPGYhihA7FQAZM9QiAVdGxDBFl+wVtV0XjVI4Cr6u+27QfkjC2KEJSFO5+C2ttrPw7TB+BIIRQCAAAA0OIYhuFXALzx5efnq3///kfktaSa2Vqe6vCr6rHChky+NkHHhAqm5HvsrjO88g+7FLg9RFv/foQOyBR0fKg+eIvG1z4u6H1F6Lvb21+PR4ddf/79vQ0yrtEKDJLCz0ILH6ZFCN5CBFm+x408my1ymOadhWaL+B6YzdawCIUAAAAAoJnxzdaSdOg3m4aXafqFahGDpODtBZ9vUe/ex9cZZNWeCRYuGIsc2NU+Jnyfw/dBId9XpW92W63Xs/BstpqQKXh/YoxLRzAHbjJ1hkJz587Vm2++qcLCQq1evVqZmZlBbfbs2aPp06drx44dcrlcOu2005SXlyeHg8wJAAAAANC0DMOQ3aiq51Vfuwtj1DOjfSP0qmWr72y2aIMst8fT+LPZQgVvtba7KvY19SU+IupMbYYMGaKrrrpKV1xxRdg2S5Ys0bHHHqulS5eqsrJSl19+udasWaPzzz+/QTsLAAAAAACaXmufzZafn9/UXTgi6gyFcnJy6jyJYRgqKyuTx+OR0+lUZWWlOnfu3CAdBAAAAAAAQMNrkKpsEydO1LZt2zRgwADf/45kETYAAAAAAADUj2Ga0ZWHys3N1ZIlS0LWFHr++ef1zTff6Pbbb1dZWZnGjh2r0aNH69xzz42qExUVFSooKKhfzwEAAAAAABBRpEk7DVIJ+plnntF9990nm82mtm3bKjc3Vx9//HHUoZBXnz59FBcX1xBdalJH+naVaD4Ye2ti3K2Lsbcuxt66GHvrYuyti7G3JquMe4MsH+vWrZveffddSZLT6dRHH32k4447riFODQAAAAAAgEZQZyh0zz33aNCgQdq5c6euueYaDRs2TJI0duxYbd68WZJ0++23Kz8/XyNGjNCoUaN09NFH609/+lPj9hwAAAAAAACHrM7lY3l5ecrLywva/sgjj/geH3XUUVq2bFnD9gwAAAAAAACNpkGWjwEAAAAAAKBlIRQCAAAAAACwIEIhAAAAAAAACyIUAgAAAAAAsCBCIQAAAAAAAAsiFAIAAAAAALAgQiEAAAAAAAALIhQCAAAAAACwIEIhAAAAAAAACyIUAgAAAAAAsCBCIQAAAAAAAAsiFAIAAAAAALAgQiEAAAAAAAALIhQCAAAAAACwIEIhAAAAAAAACyIUAgAAAAAAsCBCIQAAAAAAAAsiFAIAAAAAALAgQiEAAAAAAAALIhQCAAAAAACwIEIhAAAAAAAACyIUAgAAAAAAsCBCIQAAAAAAAAsiFAIAAAAAALAgQiEAAAAAAAALIhQCAAAAAACwIEIhAAAAAAAACyIUAgAAAAAAsCBCIQAAAAAAAAuqMxSaO3eucnNzlZWVpa+++ipsu9dee00jRozQ8OHDNWLECBUXFzdoRwEAAAAAANBwHHU1GDJkiK666ipdccUVYdts3rxZCxcu1JNPPqnU1FTt379fsbGxDdpRAAAAAAAANJw6Q6GcnJw6T/LEE0/o2muvVWpqqiSpbdu2h98zAAAAAAAANBrDNE0zmoa5ublasmSJMjMzg/aNGjVKZ5xxhj777DMdOHBAZ511lq677joZhhFVJyoqKlRQUFC/ngMAAAAAACCi/v37h91X50yhaLjdbm3dulXLli2T0+nUmDFjlJ6erlGjRtXrPH369FFcXFxDdKlJ5efnR7zoaL0Ye2ti3K2Lsbcuxt66GHvrYuyti7G3JquMe4PcfSw9PV3nnnuuYmNj1aZNGw0ZMkSbNm1qiFMDAAAAAACgETRIKDR8+HC9//77Mk1TlZWV+ve//63evXs3xKkBAAAAAADQCOoMhe655x4NGjRIO3fu1DXXXKNhw4ZJksaOHavNmzdLkoYNG6aOHTvq/PPP16hRo9SrVy/98Y9/bNyeAwAAAAAA4JDVWVMoLy9PeXl5QdsfeeQR32Obzabp06dr+vTpDds7AAAAAAAANIoGWT4GAAAAAACAloVQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCogqF5s6dq9zcXGVlZemrr76K2Pa7777TSSedpLlz5zZIBwEAAAAAANDwogqFhgwZomeffVYZGRkR27ndbs2YMUNDhw5tkM4BAAAAAACgcTiiaZSTkxPVyZYuXaozzzxTBw4c0IEDBw6rYwAAAAAAAGg8DVZT6Msvv9T777+v0aNHN9QpAQAAAAAA0EgM0zTNaBvn5uZqyZIlyszMDNheWVmpyy+/XLNnz1avXr20YMECHThwQLfddltU562oqFBBQUH9eg4AAAAAAICI+vfvH3ZfVMvH6rJ7925t375d48aNkyT9+uuvMk1TpaWluvvuu6M+T58+fRQXF9cQXWpS+fn5ES86Wi/G3poYd+ti7K2Lsbcuxt66GHvrYuytySrj3iChUHp6uj7++GPf8/rOFAIAAAAAAMCRFVVNoXvuuUeDBg3Szp07dc0112jYsGGSpLFjx2rz5s2N2kEAAAAAAAA0vKhmCuXl5SkvLy9o+yOPPBKy/fXXX394vQIAAAAAAECjarC7jwEAAAAAAKDlIBQCAAAAAACwIEIhAAAAAAAACyIUAgAAAAAAsCBCIQAAAAAAAAsiFAIAAAAAALAgQiEAAAAAAAALIhQCAAAAAACwIEIhAAAAAAAACyIUAgAAAAAAsCBCIQAAAAAAAAsiFAIAAAAAALAgQiEAAAAAAAALIhQCAAAAAACwIEIhAAAAAAAACyIUAgAAAAAAsCBCIQAAAAAAAAsiFAIAAAAAALAgQiEAAAAAAAALIhQCAAAAAACwIEIhAAAAAAAACyIUAgAAAAAAsCBCIQAAAAAAAAsiFAIAAAAAALAgQiEAAAAAAAALIhQCAAAAAACwIEIhAAAAAAAACyIUAgAAAAAAsCBCIQAAAAAAAAuKKhSaO3eucnNzlZWVpa+++ipkm4ceekjDhg3TiBEjdNFFF+m9995r0I4CAAAAAACg4TiiaTRkyBBdddVVuuKKK8K2OfHEE3XttdcqISFBX375pa688kq9//77io+Pb7DOAgAAAAAAoGFEFQrl5OTU2WbgwIG+x1lZWTJNUyUlJerSpcuh9w4AAAAAAACNolFqCq1cuVJHHXUUgRAAAAAAAEAzZZimaUbbODc3V0uWLFFmZmbYNp988oluvfVWPf744+rZs2dU562oqFBBQUG03QAAAAAAAEAU+vfvH3ZfVMvHovXf//5Xt9xyixYtWhR1IOSvT58+iouLa8guNYn8/PyIFx2tF2NvTYy7dTH21sXYWxdjb12MvXUx9tZklXFvsOVjmzZt0pQpU/Tggw/qhBNOaKjTAgAAAAAAoBFEFQrdc889GjRokHbu3KlrrrlGw4YNkySNHTtWmzdvliTNnDlTBw8e1J133qkLLrhAF1xwgbZu3dp4PQcAAAAAAMAhi2r5WF5envLy8oK2P/LII77HL774YsP1CgAAAAAAAI2qUe4+BgAAAAAAgOaNUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALKjOUGju3LnKzc1VVlaWvvrqq5Bt3G63Zs6cqaFDh+qss87SihUrGryjAAAAAAAAaDh1hkJDhgzRs88+q4yMjLBtVq9ere3bt2vNmjVavny5FixYoJ9++qlBOwoAAAAAAICGU2colJOTo65du0Zs89prr+niiy+WzWZTSkqKhg4dqjfeeKPBOgkAAAAAAICG5WiIk+zYsUPp6em+5127dtXOnTvrfZ6CgoKG6E6TSvrvi2q/d7u+WWeTbHaZRuBPGXaZtlqPDbtks8k07FVtvdsMm0xb1c+q7X6PA44J3FZ13hDHB+yv6YMMo6kvW6uSn5/f1F1AE2DcrYuxty7G3roYe+ti7K2Lsbem1jLu/fv3D7uvQUKhhtKnTx/FxcU1dTcOy/7YUv244QOldkyR6XZJHrdMd6VMt7vmucdV9djt3Vcu0+lt65JZ/VPu6namp3E7bdhk2B2S3SHDZpdR/VN2R/Vjhwx79XPv4+qf3v3+x9YcV/3cVv3cbq9ua68+T+DjqrZ2v9es3u//3Lvf24dmFmjl5+dH/IVD68S4Wxdjb12MvXUx9tbF2FsXY29NVhn3BgmFunbtqqKiIp144omSgmcOWUnb7DNU7myj1Ab8j8c0PVUBUnWYZLrdNWGTx10dHvmHTTXBkulx1ex3uyTvOXzBk995fefyHldzjpqAyyWPs0KmuyzgXDXHuQPaNnqgZbMHhlF+YVVVgBTjF3CFCbECAi7/8Kt2GOYfSjlqAi7fuWJkLylSxc7kwDAsRFgmm73ZBVoAAKDlMU2z6vOWaVZ9ZjRNyeORTI/vuemp3m56t/s99u3zHu+RPJ5a53XXtKk+t+88Hk/gsRHO43s9hehTQD/cNe1DvJ+Q/fC9V3ed7yfitfE+lydgn2maalfh1I/5STIMe8AfSX2fR32fI22Bfwj1fvbztfV+5vT7HOv9I211u6pjvJ8jvY/92tgcUvVz37kMe8A5AvoFAGE0SCh07rnnasWKFTr77LNVUlKit956S88++2xDnBqSDMMmOWwyFNPUXak30+OuDo1qhVTe0CpkiOUODJsCwjCX77ja+/2PCxeGeVzlgeGYOzDE8j+XZNb7/baTVPjvKBv7z6CqNUsqYNZWwD6/GVzhQqzaM6/8zxMwG8wREJYFh1h2GbaYoP7JsBFoAYBFRA4c/L+Ih/qCHiY08H5Bl1n92BPhPNVt5AkdPoT6Qh8qcAjoR63AIVQIEqof1UFC0p49+vn79ZH7ESmM8XgUKnAIHcaEfg3TrLkeh/J5pUUwbFWfOWy2qlIH3s8fNlvNY6Nqn1HdVrbA7VXtqttUH1dzTNW5DEeMr01dr3FgT7FiO7T3/TFW1Z9zTVelTM/Bms+nvs+/3s+l3j/oeo/zNP4fTgMvZk0A5fvs6A2e/EMn737vTP6qUhO+gMn/j5u+P67a/FYSeMOq2sGWLfCzpPcY/2As6Dj/16s6R83nWO8fWOssjwsgCnWGQvfcc4/WrFmj4uJiXXPNNerQoYNeffVVjR07VjfccIOys7N1wQUXaOPGjTr77LMlSZMmTVL37t0bvfNo/nz/uDSrhYp1834gCwiM3G6ZnsrA8KlWwPT11i/Vq+fRdc6gqjkuurBMHrc8lRWhwzD//lWft3EZEZf1hQyYAkKrmhlctZcIBodhkZYp+i09rCMs870GHx5whPi+8Jqmd0P1c8kMs716Q9WXv6B9NceYYbYHHh9if/VxZqjjap3TDNm/EH2LeHzoPgT0zXdstNfI//jQ7y/w+HDXTyH3hb9GgdfLNE3F/fijSpw/hggcIs2WCP5CXzPzwW8WRMigpFawEC688AYOkcKYgNcLEzj4zmWVwCF8sGD4Bwk2m+wVFXK69gcEDuHCi8DAIcJr2OzBbQICihABh/zDiwj9iPq91u5T6H4EXI9IYUxQe//zBr6fkP1ohn7Mz1fvBloNELASwOPxC5C8M/mDgyTT4/dH0urn8t9fK5Sq+YNq4Dm85695DVfA61Wdv+qzZ01bl19/agKv2q91RBm2gGDJNwvLf5aUX+BVEzo5au33/uHTL5Tyn/Flsyv+559VUvFDzWfTUGFVqNlbQTO+/AO14KCO1QRoCoZpmk3+r31FRYUKCgpaRU0hyTprDxGsOYx91RcB7z/QrhDBVO0ZUpV+bf2XKQbOoPKfeRUYYgUvTQya4eVrW1krZAsOvxqVEfiXqsClhyFqYIWrc+V7XHXczl271bVLF4X/Mi1F82U/7Jd5v2N8X1YjHl/ry3aUX/arNofod6RQINzx3kOj+cLtO8b//Sl4fxTvoabbYa5RNNcg0vG1xsg0PTJkBF5roK4v0mG+0Pt/WQ7/hd5vex3hRcAX77Bf6MP0Q6FeI0LgEOm9RgwcAvsRMnAIdW0CAg57YJ+CgpIQ/TgMzeHfejQNxj4y3x9VvasEgsKq6jDL7Qr8rOq/ssAbktUOs4JmYXnb1grP/AM1X4jmF4K5QxwTEGwFH9fon09rC7mUMDjwCgyo/AOv8MsXfQGUYav1+TfM7K2AmVy1PkcHnCPcTLGWvdLAKr/zLWz+BoC6GIbhmzWjmJYVslYFWpGX9YWqcxWw9NC/bcC5atfTihBieetnVVbUUbOr6th4d6VKvvd+iVJVSOD9h88wfNurN1R9QQnaV3OM4d3u30YKaBfV8YZqnafmGMO3L7ivRq3zBb8Po+Yfdu92m+HbHtA3/35HOj7ke1DIfVXvP9w1rnndoPfh1y7y8d7XqPsa79i5U127dvVr4z9Gofse8hqHuF5B16jO4wOvV6RrHLJvoa5RxOsfeL3qc40D/xut3Q9vH+u+/v7XK+pr7Htvoa9ttP+Nbti4Uf36nayGDhwAoKWqCn5b5iqBSEzT1H8++1T9+p5U5+ytgBlf/mFWVDO+wi879M7eCgjEagVXnsqKWrO3XAH7a4dsR3YJo2rCrBDLF30BVD3rdYVavlj/el3Bs7d8wZez/MheoybSin5dAbR0VYFWjAx7y6qfZZW/IiDYtvx8pTD21uSIky02vql7AQBoZFUzEO2ytbA/ttalaimzOyBcChtAhZm9FWoWVkBIFmL5YrjXC5q95W76el3t7bEyf3t6q/+DD6EQAAAAAAAWUrX8y9bi/hhbl4as1/Vt0e5WHwhJhEIAAAAAAKAVaMg7d7sO5jdAj5o/bsUDAAAAAABgQYRCAAAAAAAAFkQoBAAAAAAAYEGEQgAAAAAAABZEKAQAAAAAAGBBhEIAAAAAAAAWRCgEAAAAAABgQYRCAAAAAAAAFuRo6g5IkmmakiSn09nEPWk4FRUVTd0FNBHG3poYd+ti7K2Lsbcuxt66GHvrYuytqTWNe2xsrAzDCNpumN5Epgnt379fX331VVN3AwAAAAAAoNXp06eP4uLigrY3i1DI4/GorKxMMTExIZMrAAAAAAAAHJpmPVMIAAAAAAAARxaFpgEAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAAAAAACyIUAgAAAAAAMCCCIUAAAAAAAAsiFAIAAAAAADAggiFAAAAAAAALIhQCAAAAAAAwIIIhQAAQKuSm5urDz/8sKm7cUg+/vhjDRo0qKm7AQAALIJQCAAAAAAAwIIIhQAAAAAAACyIUAgAALRKTqdT9957rwYMGKABAwbo3nvvldPplCTt3btX48ePV05Ojk499VRdfvnl8ng8kqSlS5dq4MCB6tevn8455xx99NFHEV/H4/Fo6dKlGjp0qE477TTdeOONKikpkST99NNPysrK0ssvv6wzzzxTp512mhYvXuw79uDBg5o2bZpOOeUUnX/++dq8eXMjXQ0AAIBgjqbuAAAAQGNYvHixNm7cqFWrVskwDE2cOFGLFi3STTfdpGXLlqlz586+wGfjxo0yDEPfffednn32Wb3wwgvq3LmzfvrpJ19YFM7TTz+tt956S88884xSUlJ0zz33aNasWZo3b56vTX5+vt544w19//33+uMf/6izzz5bxx57rBYuXKjt27frX//6l8rLyzV27NhGvSYAAAD+mCkEAABapdWrV2vSpEnq2LGjUlJSNGnSJL3yyiuSJIfDod27d6uoqEgxMTHKycmRYRiy2+1yOp369ttvVVlZqW7duumoo46K+DrPP/+8pkyZoi5duig2NlaTJ0/Wm2++KZfL5WszefJkxcfHq3fv3urdu7e+/PJLSdLrr7+uCRMmqEOHDuratav+53/+p/EuCAAAQC3MFAIAAK3Srl27lJ6e7nuenp6uXbt2SZL+/Oc/a+HChbr22mslSZdcconGjRunHj166Pbbb9eCBQv0zTffaMCAAZo2bZo6d+4c9nWKioo0adIk2Ww1f2uz2Wzas2eP73mnTp18jxMSEnTgwAFfH7t27RrQRwAAgCOFmUIAAKBVSktLU1FRke/5jh07lJaWJklq06aNpk2bprVr12rx4sVatmyZbynZiBEj9M9//lPr16+XYRi6//77I75Oly5d9Mgjj+izzz7z/W/z5s0RgySv1NRU7dixI6CPAAAARwqhEAAAaJWGDRumxYsXa+/evdq7d68eeughjRgxQpK0fv16/fDDDzJNU23btpXdbvfVFProo4/kdDoVGxuruLi4gBlAoVx22WX6+9//rsLCQklVRazfeuutqPp43nnnaenSpdq3b5927typp59++vDeNAAAQD2wfAwAALRKEydOVFlZmUaOHClJOvfcczVx4kRJ0g8//KC7775be/fuVbt27XTZZZfpt7/9rb788ks98PlVcU8AACAASURBVMAD+vbbbxUTE6N+/fpp1qxZEV/nqquukmmauvbaa7Vr1y517NhR559/voYOHVpnHydPnqwZM2ZoyJAhSktL00UXXaSnnnrq8N88AABAFAzTNM2m7gQAAAAAAACOLJaPAQAAAAAAWBDLxwAAAOowZswY5efnB20fP368JkyY0AQ9AgAAOHzNYvmYaZq+go6GYTR1dwAAAAAAAFq9ZrF8zOl0qqCgQE6ns6m70iA+//zzpu4Cmghjb02Mu3Ux9tbF2FsXY29djL11MfbWZJVxr3P52C+//KJbb71V27dvV2xsrHr06KFZs2YpJSUloN3MmTP10UcfKTY2VomJifrLX/6i7OzsRut4c3bw4MGm7gKaCGNvTYy7dTH21sXYWxdjb12MvXUx9tZklXGvc6aQYRgaM2aM3nzzTa1evVrdu3fX/fffH9Ru0KBBWr16tV555RWNHz9eU6ZMaZQOAwAAAADw/9m78/ioqoP/49/Zs28sISQQRASsgGiotgooQesCKPrUlooL+ggqihatIhVF3ICq+FTQUtzrUvnhguURXOq+1C2PCnFXkAgk7IEkJJnMzP39MclkJjOTTGCSkNzP+/XqKzPnnnvuuXMMMN+ecy6AA9diKJSRkaFjjz028H748OHasmVLWL0xY8bI4XAE6pSVlcnn88WxqwAAAAAAAIiXVu0p5PP59M9//lOFhYXN1nvqqad04oknymo9KLYsAgAAAAAAQBOtevrYvHnztHXrVi1ZsiRq4PPSSy/pvvvu01NPPaXu3bvH1G5tba2Ki4tj7QYAAAAAAABiUFBQEPVYixtNN1i4cKE2btyopUuXRg2EXnvtNd1777167LHHYg6Egg0ZMkQul6vV5x1sioqKmv3Q0XUx9ubEuJsXY29ejL15MfbmxdibF2NvTmYZ95hCoUWLFqm4uFjLli2T0+mMWOfNN9/U/Pnz9eijjyovLy+unQQAAAAAAEB8tRgKff/99/r73/+ufv36adKkSZKkvLw83X///TrzzDO1bNkyZWdna/bs2XI4HLrqqqsC5z722GPKzMxsu94DAAAAADqtPXv2aMeOHXK73R3dlajsdru+/vrrju4G2llnGHen06nu3bsrPT19v9toMRQ67LDD9O2330Y89uKLLwZef/jhh/vdCQAAAACAudTU1Gjr1q3Ky8tTYmKiLBZLR3cpoqqqKiUnJ3d0N9DODvZxNwxD1dXV2rRpk1wulxISEvarHR4PFmc/birXj6U12rW3Rq3YwxsAAAAATGX79u3q0aOHkpKSDtpACDhYWSwWJSUlqXv37tq+fft+txPzRtOIzf3PfqHvfy7XE2++otQkh/r2SlPfXqnKz05V35w05fdKU1py5H2ZAAAAAMAsampq1KtXr47uBtCppaamaufOnft9PqFQnN126XFa/cbHSkjL0cayvSopq9A7/7dJVTWeQJ2MVJfye6UqvyEwqv+ZlODowJ4DAAAAQPvxeDyy2/lKChwIu90uj8fTcsVo58exL5CUnOhQ/14JKijoHygzDEM799SopKxCG8v2BsKiVz7aqFq3N1Cve0ai8nulqm+vtEBolJedogQnwwQAAACg62HZGHBgDvR3iLShHVgsFnXPSFT3jEQdPbhnoNznM7Rt975AWNTw84vvd8jj9dWfK/XKSlbfXqkhs4ryeqbIYbd11C0BAAAAAIBOjlCoA1mtFvXqlqxe3ZJ1zBGNa2m9Xp9Kd1ZpY1mFSkr3auPWCpWU7dUnX2+Vz2cEzs3tkay+2WmB2UV9e6Wqd/dk2WzsHw4AAAAA7WXQoEEt1snNzdUbb7yx39d4/vnnNXv2bL3++uvKy8vb73YaLF68WEuWLIn6tHEzOP/88yVJTzzxRAf3pOMQCh2EbDar8nqmKq9nqo4f1jtQXufxavP2Km0s3auSrRXaWLpX6zfv0QfrtqjhQWd2m1V5PVOU3ytN+Tmp6pudqvycNPXMTJLVytRMAAAAAIi35cuXh7y/8sorNWjQIM2YMSNQ5nQe2AOHTjzxRC1fvlw9e/ZsuTIQI0KhTsRht6lfTpr65aSFlNe4Pdq0tTJkCdqXG3bq7c82Beq4nDb1yU71zyrK9gdG+b3S1C09gXW8AAAAAHAAhg8fHvLe6XQqMzMzrDyY1+uVYRgxb7adlZWlrKysA+on0BShUBeQ4LRrQJ8MDeiTEVJeVV2nn7dW+Jeh1W9w/X/fbNPrn/wcqJOcYA8sPWvYsyi/V5oyUl3tfRsAAAAA0GUNGjRIl112mZKTk/XMM8+otLRUzz33nA499FDdc889+uCDD7R582YlJSVp6NChuu6663TooYcGzo+0fKywsFBHH320xowZoyVLlqi0tFT9+/fXn//8Z40YMaLVfaysrNSiRYv06quvqry8XLm5ufrDH/6gCy+8MDCZoKqqSosWLdLrr7+uHTt2KDU1VYMGDdJNN90U6O/jjz+uZ555Rps3b5bL5VKfPn10+eWX6+STT465Lx9//LHuv/9+rV27VoZhqKCgQLNmzdLAgQMDdc4//3x5PB7NmDFDd911l9avX68+ffro6quvDrvWSy+9pMWLF2vTpk3Kz8/XH//4x1Z/Pl0RoVAXlpzo0OB+WRrcLzRN3lNZq5KtFSEbXL//xRa98mFdoE5asjOwqXXwE9FSkg5syiMAAAAAmNXzzz+vPn36aNasWUpMTFTPnj3ldrtVVVWlyy+/XD169NCePXv09NNPa9KkSVq9erV69OjRbJtFRUXasGGDrr76arlcLv31r3/VZZddpjfeeENpaWnNnhvM5/Np2rRp+uqrr3TVVVdp4MCBeuuttzR//nzt2rVL11xzjSRp/vz5euONNzRz5kz169dP5eXl+r//+z9VVFRIkv71r39p4cKFmj59ukaMGKHa2lp9++23Ki8vj7kvb731lqZPn64TTjhBd911lyTpoYce0uTJk/Wvf/1LOTk5gbo///yz7rjjDk2bNk2ZmZl69NFHdfXVV2vNmjXKz8+XJH3wwQe69tprdeKJJ+qGG27Qrl27dMcdd8jj8eiQQw6JuV9dEaGQCaWnuDQ0xaWhh3YPlBmGod0VtfUziioC+xa98WmJqmu9gXpZaQkhIVHfXqnqk52qpARHR9wKAAAAgC7kjU9L9NrHJR3ah5OP6avCEX3bpG3DMPTII48oISEhpPyOO+4IvPZ6vRo5cqSOO+44vfTSS5oyZUqzbVZWVmrlypVKT0+XJHXv3l2//e1v9fbbb2vChAkx9+3tt99WUVGR5s+fr7PPPluSNHLkSFVXV+uRRx7RlClTlJWVpc8//1wTJkzQOeecEzg3eFbO559/rkGDBunKK68MlJ1wwgkx90Pyfx6//OUv9be//S1Q9qtf/Upjx47VI488ohtvvDFQvnv3bj355JPq16+fJOmII47QyJEjtWbNGl122WWSpPvuu0/9+/fXAw88IKvV/2Cm/v376/e//z2hUEd3AAcHi8WirLQEZaUlaPjAxo3LDMPQ9t3VgY2tS7b6Zxet+WCD3B5foF7PrCT/pta9/Btb981OVV52qlwOW0fcDgAAAAAcdEaNGhUWCEnS6tWr9eijj2rDhg2BGTeStH79+hbbHD58eCAQkhqfhFZaWtqqvn3yySeyWq0aP358SPkZZ5yhZ599Vp9//rkKCws1dOhQvfDCC8rMzNTxxx+vX/ziF7LZGr/3DR06VE8//bRuu+02jR07VkcddZQSExNj7sdPP/2kkpISXXrppfJ4PIHyhIQEHXXUUfr0009D6ufn5wcCIUnq1q2bunXrpi1btkjyh2zFxcWaOnVqIBCS/J9bbm5uzP3qqgiF0CyLxaKeWUnqmZWkEYdnB8q9PkNbd1VpY2mFSrbuVUmpPyz6/Ltt8nj9j0KzWqSc7smBPYvys9PUNydVuT1SZLdZo10SAAAAgEkVjmi7WToHg0hPDmtYinXWWWfpyiuvVGZmpiwWi6ZNmya3291im8GBkNT4lLPa2tpW9W3Pnj1KT08Pe0pa9+7dA8clac6cOerevbuee+453XvvvcrIyNCZZ56pmTNnKjExURMnTlRtba2effZZPf3007Lb7TrhhBN0ww03BPZCas7OnTslSTfeeGPIjKAGvXv3Dnnf9P4l/2fQ8Nnt3r1bdXV1gfuIdG9mRiiE/WKzWtS7e4p6d0/Rr4c2ruf0eH3asr2yfnPrxj2LPioulc+fFclus6h3j5SQPYvye6Upu1uybFaehAYAAADAPF566SXl5+drwYIFgbK6urpACNNe0tPTtWfPHrnd7pBgaMeOHYHjkpScnKxrr71W1157rTZv3qxXXnlF99xzjxwOh6677jpZLBZNmjRJkyZN0p49e/T+++9rwYIFmjlzplasWNFiPzIy/A9Quvbaa/XrX/867LjD0bqtSzIzM+VwOAL3EWzHjh2mny1EKIS4stus9TODQjc0c9d5tWlbZeOeRWV79V3Jbr37+eZAHafdqrzs1CZ7FqWpR0airIRFAAAAALqgmpqakOVXkvTiiy/K6/VGOaNtHHPMMXr44Yf18ssv64wzzgiUr1q1Sg6HQ8OHDw87Jzc3VxdffLFWrVql77//Pux4enq6Tj/9dH3xxRdavnx5TP3o37+/cnNz9f3332vatGn7f0P1bDabhgwZoldeeUUzZswILCH74osvtHnzZkKhju4AzMHpsKl/brr654ZO7auu9ejnrRWBsKikrEJrf9ihN4s2Beokumzqm+2fVRS8wXVWWkLgsYgAAAAA0BmNGjVK//73v3XnnXdqzJgxWrdunZ588slWPTksHkaPHq2CggLNnTtXu3bt0mGHHaa3335bK1as0KWXXqqsLP9TrX//+9+rsLBQAwcOVFJSkj755BN98803mjhxoiTppptuUnJysoYPH65u3brpp59+0osvvqjjjz8+pn5YLBbNnTtX06dPV11dnU477TRlZmZqx44d+uyzz9S7d29ddNFFrbq3q666ShdffLGmT5+uSZMmadeuXVq8eHGLT3YzA0IhdKhEl10D+2ZqYN/MkPLKfe76Ta0rVFK/wfXHX5WFPIkgJdER2NS6YVZR316pSk9xtfdtAAAAAMB++d3vfqfS0lI999xzWr58uYYOHaqlS5eGPL2rPVitVi1btkyLFi3SQw89pPLycuXm5mr27Nm68MILA/VGjBihNWvWaNmyZfJ6verTp49mz56tCy64QJJ09NFH6/nnn9eLL76oiooK9ezZU2eccYauuuqqmPtywgkn6Mknn9TSpUs1Z84c1dTUqEePHjryyCN1+umnt/rejjvuON19991avHixrrzySuXn5+vPf/6z/vGPf7S6ra7GYhiG0dGdqK2tVXFxsYYMGSKXq/N/oS8qKlJBQUFHd6NLKq+oVcnWvfUbXNc/Ea1sr6pqGnelz0h1+YOinPpZRfWzjJITW7f2dH8w9ubEuJsXY29ejL15MfbmxdjH39dff63DDz+8o7vRoqqqKiUnJ3d0N9DOOtO4H8jvEjOF0KlkpLqUkdpDwwY0TvMzDEO79tZoY2njxtYby/bqtY82qsbduA63e3qC+uak+Te4zk5Vfk6q+vRMVYKLXwMAAAAAgPnwbRidnsViUbf0RHVLT9TRgxsf8ejzGdq2e1/IU9A2lu3Vuh92qM7jqz9Xys5KCjwJrWHPoryeKXLYbdEuCQAAAABoJcMwWtxA224npmhPfNrosqxWi3p1S1avbsk65ohegXKv16fSnVX1IVFjYPTJ11vl8xmBc3t3Tw6ERQ0/e3dPls1m7ahbAgAAAIBO64UXXtDs2bObrfPtt9+2U28gEQrBhGw2q/J6piqvZ6qOG9ZYXufxavP2qqAnoe3V+i179MG6LWrYectusyqvZ0pQWOSfXZSdlSSrlSehAQAAAEA0Y8aM0bPPPtvR3UAQQiGgnsNuU7+cNPXLCX30Y43bo03bKv1hUf0G11/9tFNvf7YpUMfltKlPdqqS7W5t3PuD8nP8G1x3z0iQxUJYBAAAAACZmZnKzMxsuSLaDaEQ0IIEp10D8jI0IC8jpHxfTV39E9AqVLJ1r0pKK/TDz3v1xYYvA3WSEuyBJ6H17ZWq/Ow09c1JVUaKi7AIAAAAANChCIWA/ZSU4NDg/CwNzs8KlBUVFemwwUNDlqBtLKvQB2u36JUP6wL1UpOcys9JDduzKDXJ2RG3AgAAAAAwoRZDod27d+v6669XSUmJnE6n8vPzdeuttyorKyukXnV1tWbPnq0vv/xSNptNs2bN0pgxY9qs48DBKi3ZqSGHdteQQ7sHygzDUHlFbdBT0PwbXL/x6c+qrvUE6mWlueqfgNa4Z1Gf7FQlJTg64lYAAAAAAF1Yi6GQxWLRJZdcomOPPVaStHDhQt1999268847Q+o9/PDDSklJ0WuvvaaffvpJkydP1quvvqrk5OS26TnQiVgsFmWmJSgzLUHDB/YMlBuGoe3l1SoJmlVUUrZXa/7zk9x1jY9q7JmZWB8WpQZ+5mWnyuWwdcDdAAAAAAC6ghZDoYyMjEAgJEnDhw/XP//5z7B6a9as0YIFCyRJ/fr105AhQ/TOO+/otNNOi2N3ga7FYrGoZ2aSemYmacTh2YFyr8/Q1l1V9bOK/PsVlWyt0OffbZPH638UmtUi9eqWHFh+1jC7qHePFDns1o66JQAAAABAJ2ExjIaHbbfM5/Pp4osvVmFhoS644IKQY0cddZRef/31wLKyW265Rfn5+broootabLe2tlbFxcWt7DpgPl6foV0VHm3bU6dt5XXatsf/eleFRw2/yVaL1C3Nrp7pDv//MhzqkW5XVopdViubWwMAAODgYLfbNWDAgI7uBtDp/fDDD/J4PFGPFxQURD3Wqo2mb7vtNiUlJem8885rzWkxGzJkiFwuV5u03Z6Kioqa/dDRdXXU2LvrvNq8vVIbS/cGnoi2sWyvvizZG6jjtFuV1zNVfZtscN0jI5Gw6ADxO29ejL15MfbmxdibF2Mff19//XWn2G6kqqqqU/QT8dWZxt3pdOrII4/cr3NjDoUWLlyojRs3aunSpbJaw5em9O7dW5s3bw7MFCotLQ1Zdgag7TgdNh3SO12H9E4PKa+u9ejnrRWNy9DKKlT8ww69VbQpUCfRZVOf7MagqGHPoqy0BFkshEUAAABAS6ZPn65PPvlE77//vpzO8CcKV1ZWauTIkTr11FMD2660pLCwUMccc0yg/vPPP6/Zs2fr9ddfV15eXtTzNm3apLFjx2r+/Pk6++yzW3Ufjz32mHr37q3f/OY3IeWLFy/WkiVL9O2337aqvQPV9DMwmwMZy1jFFAotWrRIxcXFWrZsWcT/wCXp1FNP1fLlyzV06FD99NNPWrdune655564dhZA6yS67BrYN1MD+2aGlFdW16kk8CQ0/89Pvtqq1z4uCdRJTnQov1forKK+vVKVntL5Z/MBAAAA8XTWWWfp9ddf15tvvqlTTjkl7Pgrr7yi6upqTZw4cb+vceKJJ2r58uXq2bNny5X30z/+8Q8dffTRYaHQOeeco1GjRrXZddFxWgyFvv/+e/39739Xv379NGnSJElSXl6e7r//fp155platmyZsrOz9d///d+64YYbdPLJJ8tqterWW29VSkpKm98AgNZLSXToF4d00y8O6RZSvqeyNhAUNTwJ7Z3PN6uqui5QJyPFVT+jKHSD6+RER3vfBgAAAHBQOOGEE5SRkaGVK1dGDIVWrlyp3r17H9BqmqysrMDKnPbWq1cv9erVq0OujbbVYih02GGHRZ0i9uKLLwZeJyUl6b777otfzwC0u/QUl4YOcGnogO6BMsMwtGtvTSAk2lhaoZKte/Xvj0tU4/YG6nVPT1DfnDT1DV6Klp2qBFerti4DAAAAOh2n06nx48dr+fLl2r17tzIzG2fqb9myRZ988okuvfRSWSwWvffee3r88cf19ddfq6KiQn369NHZZ5+tCy+8UDabLeo1Ii0fq66u1sKFC7VmzRq53W4de+yxuuSSS8LOXbt2rR566CF9/vnnKi8vV05Ojk455RRNnz5dCQkJkvxLtTZv3qzNmzdr1apVkvwzoBYsWBBx+VhlZaUWLVqkV199VeXl5crNzdUf/vAHXXjhhYFtKD766CNdcMEFeuCBB/T+++/rpZdekiSNGjVKN998s9LS0lr9Wa9du1aLFi3SF198IcMwdOSRR+raa6/VsGHDQurce++9+uqrr1RdXa0ePXpo1KhRuuWWWyRJ27dv19133633339f5eXlysjI0BFHHKE777xT3bp1i3LlUB6PRw8//LBeeOEFbdq0SRkZGRo/frxmzpwZ2Cu5YfnXvHnztHXrVq1YsUI1NTUaMWKEbrnllpCgLdaxjDe+rQFolsViUbf0RHVLT9TRgxqnqvp8hraXV/tnFdVvcF1SWqF1P+xQncdXf66UnZWkvtlpys/xh0T5OWnK65kihz36X3gAAAAwp4q1b6niizc6tA+pRxYqddiJrT5v4sSJevLJJ7V69WpNnjw5UP6vf/1LhmEElo79/PPP+vWvf63zzjtPLpdLxcXFWrx4sXbt2qU//elPrbrmzTffrDVr1uiKK67Q0KFD9f7770dso7S0VIMHD9ZZZ52l5ORkff/993rggQf0888/695775UkLVmyRNOmTdOgQYM0Y8YMSYo6M8nn82natGn66quvdNVVV2ngwIF66623NH/+fO3atUvXXHNNSP077rhDY8aM0T333KMNGzborrvuks1m08KFC1t1v998843OO+88DRgwQPPnz5fFYtGyZct03nnn6f/9v/+nwYMHq6qqSpdccomGDh2q+fPnKzk5WZs3b9Znn30WaOf666/Xli1bdP311ysnJ0c7duzQf/7zH1VXV8fcl+uuu05vvvmmLrnkEh199NH68ccf9de//lWbN2/W4sWLQ+ouW7ZMRx11lO644w7t2rVLCxYs0HXXXacnnngiUCfWsYw3QiEA+8VqtSg7K0nZWUk65heNCbfX61PZrn1BT0LzL0Ur+marvD4jcG5Ot2TlN3kSWk73ZNlt4RvZAwAAAAe7oUOHasCAAVq5cmVIKPTiiy9q+PDhOuSQQyRJf/jDHwLHDMPQiBEjVFdXp0ceeUTXXHNNxAc7RbJ+/Xr97//+r2bOnKlp06ZJkkaOHKl9+/bpmWeeCakbvKTNMAwdffTRSklJ0axZs3TzzTcrMzNTv/jFL+R0OpWZmanhw4c3e+23335bRUVFIRsgjxw5UtXV1XrkkUc0ZcqUkEDpl7/8pW666aZAvQ0bNmjFihVasGBBqx5u88ADD8jpdOqxxx4LzDI6/vjjVVhYqCVLlmjJkiVav3699uzZo+uuu06DBw8OnBu8UfPnn3+umTNn6owzzgiUnXbaaTH349NPP9Xq1au1cOHCQNh33HHHKT09Xdddd52+/vprHX744YH6ubm5IXsu79q1S3/5y1+0detWZWdnt2os441QCEBc2WxW5fZIUW6PFB0XVF7n8WnL9srAxtYby/Zqw5a9+s+6Uhn+rEh2m1V5PVPC9izKzkqS1cqT0AAAALq61GEn7tcsnYPFxIkTdffdd2vDhg065JBDtHbtWq1fvz6wbEmStm3bpiVLlujdd9/Vtm3b5PF4Asd27typHj16xHSttWvXyufzhYUZ48aNCwsSKisr9be//U2vvPKKysrKVFfXuGfoxo0bQ5a7xeKTTz6R1WrV+PHjQ8rPOOMMPfvss/r8889VWFgYKD/hhBNC6g0cOFBut1s7duyI+X4brnviiSeGLDtLSUlRYWGh3nzzTUlSv379lJaWprlz5+rcc8/VMccco5ycnJB2hgwZoocffliGYehXv/qVBg4c2Kpw6t1335XD4dApp5wSMn4jR44M9DM4FBo9enTY/Uv+GVzZ2dmtGst4IxQC0C4cdqvyc9KUnxO6brjG7dGmbZUqadizqKxC3/y0S+98tjlQx+mwqW92ivr2SlN+r9T6n2nqnpHQqj+8AQAAgLZ0xhlnaNGiRVq5cqVmzpyplStXyul06vTTT5fkX3Z1+eWXa9u2bZoxY4b69+8vl8ulf//731q6dKlqa2tjvta2bdskKWwPnEh74syePVsffPCBrrrqKh1++OFKTEzU2rVrdeutt7bqmg327Nmj9PT0sKeTd+/ePXA8WEZGRsj7hvNae+09e/ZEDJG6d+8euGZqaqr+8Y9/6IEHHtC8efNUVVWlww47TDNmzAjMmPqf//kfLVmyRA899JDuvPNO9ejRQ5MmTdL06dNjmqm1c+dO1dXVRZ1RVV5eHvK+pftvzVjGG6EQgA6V4LRrQF6GBuSF/kG5r6bOv09R/ayikrIKff7dNr3x6c+BOkkJdvXNTg2ERQ1L0TJSXYRFAAAAaHfZ2dk67rjjtGrVKl1xxRVavXq1xowZo/T0dElSSUmJiouL9Ze//EVnnnlm4LyGWS6t0fBo+p07dyopKSlQvnPnzpB6tbW1ev3113XllVfqwgsvDJR/9913rb5mg/T0dO3Zs0dutzskGNqxY0fgeFtIT08PXCPYjh07Qq55+OGHa/HixfJ4PCouLtbf//53/fGPf9SLL76ogQMHqlu3bpo7d67mzp2r9evXa+XKlVq8eLGysrJ07rnnttiPjIwMuVwuPfXUUxGPN4xNrGIdy7ZAKATgoJSU4NDg/CwNzg/d3K5inzsQFDXsW/SfdaV69aONgTqpSc6Qja0bfqYmOZteBgAAAIirs846S9dee60WLVqk3bt366yzzgocq6mpkSQ5HI5AWV1dXeBpX60xbNgwWa1WrVmzJrAPjaTAE74auN1ueb1e2e2hX/9feOGFsDYdDkdMs3eOOeYYPfzww3r55ZdD9uVZtWqVHA5Hi3sS7a9f/vKXeuedd1RZWamUlBRJ/qVxb775po455piw+na7XcOHD9fVV1+tN954Qz/++GNg+I/mdAAAIABJREFU6VaD/v3765prrtEzzzyj77//PqZ+jBo1Sg8++KAqKyv161//+oDvK9axbAuEQgA6ldQkp47o301H9G+cSmkYhsora1VSWj+rqH6D6zeLNqm6tnGNb1aaS32z09Q3aIPrvtmpSkpwRLoUAAAA0GonnXSSUlJS9Nhjj6lbt24aNWpU4Fj//v2Vm5ure++9V1arVXa7XY8//vh+Xad///4aP3687rvvPvl8Pg0dOlTvvfee3nnnnZB6qampGj58uB599FH17NlTmZmZeu6557R169awNgcMGKBPP/1Ub775prp3767MzEzl5eWF1Rs9erQKCgo0d+5c7dq1S4cddpjefvttrVixQpdeemnUp5YdqOnTp+utt97SlClTNHXqVFksFj344IOqrq7WFVdcIck/62r58uU66aSTlJeXp+rqaj3xxBNKTk7WUUcdpYqKCk2ZMkUTJkxQ//795XA49Prrr2vPnj06/vjjY+rHscceq/Hjx+uqq67SlClTAqHO5s2b9fbbb+tPf/pTYGPxWMQ6lm2BUAhAp2exWJSZmqDM1AQdObBxjbFhGNpRXhOyuXVJ2V69/J+Nctd5A/V6ZiYG7VfkX47WJztVLoetI24HAAAAnVhCQoJOO+00rVixQuPHjw+ZoeN0OnX//ffr1ltv1axZs5Senq7/+q//Uu/evTVnzpxWX+vWW29VUlKSHnnkEdXV1enYY4/V3XffHbYE6p577tEtt9yiefPmBfp344036tJLLw2pd8011+imm27SH//4R9XU1Oiss87SggULwq5rtVq1bNkyLVq0SA899JDKy8uVm5ur2bNnhyxRi7fBgwfriSee0L333qsbbrhBhmHoyCOP1JNPPhl40lh+fr4SEhL0wAMPaPv27UpOTtbQoUP16KOPqlevXnK73TriiCO0YsUKbdmyRRaLRYcccojuvvtunXTSSTH35a677tITTzyh5557TkuXLpXT6VRubq5GjhwZ2FupNWIdy3izGEbDc386Tm1trYqLizVkyBC5XK6O7s4BKyoqUkFBQUd3Ax2Ase8cvD5D23btaxIWVWjTtgp5vP4/Ei0WqVe35JC9ivJ7pal3jxQ57KGbzzHu5sXYmxdjb16MvXkx9vHX9LHdB6uqqiolJyd3dDfQzjrTuB/I7xIzhQCYjs1qUU73ZOV0T9avhjQ+ntLj9al0R1XjnkX1YdHHX22Vz2cEzu3dIyXoKWip2rOnTjv3VMvltCvBaZPd1vITCwAAAACgoxEKAUA9u82qPtmp6pOdquOP7B0od9d5tXl7pTaWVaikPij6YVO53vtiS6DOAy+9GtSORQn1AZHLaVeCy6YEp10up00JTlvgWHgd/+vE4LquoDoETgAAAECb8nq9MgxDHo9HHo8n7LjVao3psfWdBaEQALTA6bDpkN7pOqR36KM1a2o9+nlbhd7/uFi9evdRjdurWrdH1bUe1bq9qnF7VeP21Jd7VVHl1vbd/vc1tf66bo+vVX2x26z1gVJ44JQYIXgKDpz8ZQROAAAAQDRTpkzRxx9/HPV4tH2WOitCIQDYTwkuuw7rk6m925JUUNBvv9rw+gzVupuESLXBYVJ9iBQ4Fjlw2lvl1vbd+9okcGoIlKIHThHquEJnQ9kInAAAANAJzJs3T1VVVaqpqVFCQkLY8czMzA7oVdshFAKADmSzWpSU4FBSgiPubTcETg0BUm19YBQeODXMXgoPnGrcHu2pcmtbBwROia7GWUwETgAAAGgP/fv3l9S5Npo+EIRCANBFdWTgFBY8tWPgFDVMaihrsmwuuE6iq+E1gRMAAO3BMAxZLJaO7gbQaR3oA+UJhQAArdZxgVPjcrpIgVN1YCleeOBU4/ao7gACp0hhUmVFuT7+6Qt/iBSYwRQeOCUGziVwAgCggd1ul8fjkcMR/39PAGbh8Xhkt+9/tEMoBAA4qHRE4OQPkyIHTk33bwoOnPZW1Gr91s2qcXv3P3CKKUyKXCd4GR6BEwCgs0lISFBlZWWX26MFaE8VFRUR9z6KFaEQAMA04h04FRUVqaCgQJLk9fpUW+cNCZyqa8M3DI8lcNq6a19I3f0JnEKX0TUXJkWo02TfJgInAEBb6NGjh0pKSuRyuZSYmMgyMqAVDMNQdXW1duzYob59++53O4RCAADEgc1mVZLN2jYznJoETtGeUFfr9qi6NnwT8UDgVFmrre4DC5wcdmsrwqTQOk33eyJwAgBzS0hIUHZ2tsrKylRbW9vR3YnK7XbL6XR2dDfQzjrDuLtcLmVnZzNTCACArqw9AqfqCJuBBwdONWGznhoDp+ra+sDpAGc4RQucGjcAjxw4RdrvqSFw8p9rl83K//sMAAej9PR0paend3Q3mlVUVKQjjzyyo7uBdmaWcScUAgDAxNo6cArZvynKk+nCltY1OVZeURNWJx6Bkz9MaiZwirLfU4LTpj37PNpdUSOHzSqbzSq7zSKb1Sor4RMAAOhECIUAAECbsNmsSk60KjmxfQOn8FlNTQKnoOV3TQOn6lqvPN4YA6eVZWFFVot/T6dAUGSzym6t/xlcVh8iOexW2ayWkPr+oMnib8dqCWnP3nDMaq2va2lyLavs9sbXtuBz6vvQ2KYlNNSq76vVamFfDwAATIJQCAAAdDodEjgFhUk//LheuXl95fH65PX65PEa/p8+Qx6PTx6fT16vUX/c/9Pj9cnrCy+rdRsh9T3B7XkNeX2++jYN+XxG3O+3KYtFsllDA6xAMBUtwLI2CZ6ahFp2e2gY1XDMH4q1fK2I7dqbBl/+cxquRbAFAEDLCIUAAACCxBI4pVu2qaDgkHbslZ/PZ8jrCw2gvL7gEMn/OhA8+RpDq+AwquGcQFgVKK8PtaIEWI1thgZYNW6PPL7QPkQKtRrK2oMtLFQKn0HVGEYFBVi2JjOsrNb6AMpfd8f2cn217evG86OFWkHHooZazc3cYjkiAKAdEAoBAAB0Etb65V0Oe+d9Upth+Gc8eVoTQEWYfVXXMEsrKGwKCaWCwrI6b5OZW/VBVfB16zw+f7jlCQ3TQkI1n0/uOq9833wnox2yLatF4UsPW7kcsWFJ4f4uR7TZIs3KYjkiAHQVLYZCCxcu1CuvvKLNmzdr1apVGjhwYFidnTt3avbs2SotLZXH49Gxxx6rOXPmyG4ncwIAAEAji8Uim80im01yOWwd3Z1WKyoqUkFBQeOMreZCrfpjdWEzuhrqhwdYITOtfLGHWtGWI4YEaEHX8nh9HbYc0dZk9lVrlyNGOxbrcsRoe3eFzhJjOSIAc2gxtRk7dqwuuOACTZ48OWqdpUuX6tBDD9WyZctUV1enc889V6+++qpOP/30uHYWAAAAOBjYrBbZrDY5O2Gw1cAwjLD9sJoGVIFQa3+XI3qjB1ixLUeMHKAFl7UHfzBkyP5sqaxWi2z1s6CslqDXDe9t/p9h9WzW+nKFvW9YLtj0/OauFVwefq3I5SHvG15HKG/uWv6+qsl9qb5ew32IIA3oJFoMhUaMGNFiIxaLRVVVVfL5fHK73aqrq1N2dnZcOggAAAAg/iwWixx2Ey5HbBpeBc2+CluO6PPJ4/GHUVu2lKpHz2z56jd99/oM+Yyg1w3lhhFap+F/hv+9py6onrex3OfzyedTfbmvvm01HjMMeX2qr2eoHSZ6HZDQIEqyWq3hIVozwZQtUjAWKbCyWGS1RSmPcH7E8hZCsA0l++R2bmnzMI99xNARLIYR24rowsJCLV26NOLysfLycs2YMUM//vijqqurNXnyZP3pT3+KuRO1tbUqLi6OvdcAAAAAYGKG4Q+GDEP+AKnhtc+oL/OXN5RFrFsfPIWWh7YV7Rrh5f6f/vbqXzeUh10j+NpN6oa1EXQ/QfcW6Rqh5ZHPO9j5Z1k1hGr1ry2Ns6/8s7KCXgeVN3ueNbRuaLmlvn7Q60jXCGsj6DxrhDaalIedF+3aIedFv7fgdtG8goKCqMfisunPyy+/rEGDBunxxx9XVVWVpk6dqpdfflmnnnpqq9oZMmSIXC5XPLrUoRrWmsN8GHtzYtzNi7E3L8bevBh782Lsu4aGWVshM7kizfgKKl9X/KUGDz68ccaXN/gcX30g5Z9d1hB2BY610HYss8zCyutnmUUsj3K+x1d/TpOZa77ALLXgNnz1s9Ia2zyYNYRIVqvVPyvN0vC6MfSy2qz1s8TUeKx+llnY7LGG974q3Xzp2C4fOsUlFHryySd15513ymq1KjU1VYWFhfroo49aHQoBAAAAANBWrFaLrLJIrdgObNsmh/rnprddpw5yjTO4GkIw//vGEKxhqWPLIVjTMC6sXpTQLjSIiy3MC1u+GUOY5/UZcnu98hmGbIavywdCUpxCoby8PL3zzjsaNmyY3G63/vOf/+jkk0+OR9MAAAAAAKCDWCwW2Sz+Ddcd6rx7kLVWUVFRR3ehXbQ4orfffrtGjx6tsrIyXXTRRRo3bpwkaerUqVq3bp0k6c9//rOKioo0YcIETZw4Uf369dPvfve7tu05AAAAAAAA9luLM4XmzJmjOXPmhJU/+OCDgdd9+/bVo48+Gt+eAQAAAAAAoM2YZ+4XAAAAAAAAAgiFAAAAAAAATIhQCAAAAAAAwIQIhQAAAAAAAEyIUAgAAAAAAMCECIUAAAAAAABMiFAIAAAAAADAhAiFAAAAAAAATIhQCAAAAAAAwIQIhQAAAAAAAEyIUAgAAAAAAMCECIUAAAAAAABMiFAIAAAAAADAhAiFAAAAAAAATIhQCAAAAAAAwIQIhQAAAAAAAEyIUAgAAAAAAMCECIUAAAAAAABMiFAIAAAAAADAhAiFAAAAAAAATIhQCAAAAAAAwIQIhQAAAAAAAEyIUAgAAAAAAMCECIUAAAAAAABMqMVQaOHChSosLNSgQYP03XffRa23evVqTZgwQePHj9eECRO0Y8eOuHYUAAAAAAAA8WNvqcLYsWN1wQUXaPLkyVHrrFu3TkuWLNHjjz+uHj16qKKiQk6nM64dBQAAAAAAQPy0GAqNGDGixUYee+wxXXzxxerRo4ckKTU19cB7BgAAAA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size 1440x1080 with 3 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Fold: 5\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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size 1440x1080 with 3 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"MvzO_AjPnvrt","colab_type":"text"},"source":["# Model evaluation (last model)"]},{"cell_type":"code","metadata":{"_kg_hide-input":true,"id":"ELe26ujlnvrt","colab_type":"code","outputId":"9fc7c6ae-3d66-46e5-cea8-bbc176f69564","executionInfo":{"status":"ok","timestamp":1589845194644,"user_tz":180,"elapsed":9533,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}},"colab":{"base_uri":"https://localhost:8080/","height":588}},"source":["display(evaluate_model_kfold(k_fold, config['N_FOLDS']).style.applymap(color_map))"],"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/html":["<style  type=\"text/css\" >\n","    #T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col0 {\n","            color:  black;\n","        }    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#T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col8 {\n","            color:  green;\n","        }    #T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col9 {\n","            color:  black;\n","        }    #T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col10 {\n","            color:  green;\n","        }    #T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col11 {\n","            color:  black;\n","        }    #T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col12 {\n","            color:  green;\n","        }</style><table id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Train</th>        <th class=\"col_heading level0 col1\" >Valid</th>        <th class=\"col_heading level0 col2\" >Var</th>        <th class=\"col_heading level0 col3\" >Train_fold_1</th>        <th class=\"col_heading level0 col4\" >Valid_fold_1</th>        <th class=\"col_heading level0 col5\" >Train_fold_2</th>        <th class=\"col_heading level0 col6\" >Valid_fold_2</th>        <th class=\"col_heading level0 col7\" >Train_fold_3</th>        <th class=\"col_heading level0 col8\" >Valid_fold_3</th>        <th class=\"col_heading level0 col9\" >Train_fold_4</th>        <th class=\"col_heading level0 col10\" >Valid_fold_4</th>        <th class=\"col_heading level0 col11\" >Train_fold_5</th>        <th class=\"col_heading level0 col12\" >Valid_fold_5</th>    </tr>    <tr>        <th class=\"index_name level0\" >Metric/Jaccard</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n","                <tr>\n","                        <th id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >Overall</th>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.768491</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.707071</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.061419</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.772995</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.705735</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.775755</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.703252</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.763027</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col8\" class=\"data row0 col8\" >0.713568</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col9\" class=\"data row0 col9\" >0.759839</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col10\" class=\"data row0 col10\" >0.702106</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col11\" class=\"data row0 col11\" >0.770837</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row0_col12\" class=\"data row0 col12\" >0.710697</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >Sentiment negative</th>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.638530</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.526080</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.112450</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.647784</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.516617</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.651410</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.522896</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.632929</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col8\" class=\"data row1 col8\" >0.532458</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col9\" class=\"data row1 col9\" >0.620357</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col10\" class=\"data row1 col10\" >0.526460</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col11\" class=\"data row1 col11\" >0.640172</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row1_col12\" class=\"data row1 col12\" >0.531967</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >Sentiment neutral</th>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.974791</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.971890</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.002900</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.974460</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.972111</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.975002</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.971269</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.974264</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col8\" class=\"data row2 col8\" >0.973509</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col9\" class=\"data row2 col9\" >0.975382</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col10\" class=\"data row2 col10\" >0.970076</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col11\" class=\"data row2 col11\" >0.974845</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row2_col12\" class=\"data row2 col12\" >0.972487</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >Sentiment positive</th>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.619083</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.528128</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.090955</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.625576</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.531986</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.630423</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.519429</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col7\" class=\"data row3 col7\" >0.607329</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col8\" class=\"data row3 col8\" >0.541153</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col9\" class=\"data row3 col9\" >0.607070</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col10\" class=\"data row3 col10\" >0.514342</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col11\" class=\"data row3 col11\" >0.625016</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row3_col12\" class=\"data row3 col12\" >0.533728</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >0 <= text wordCnt < 3</th>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.866694</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.856313</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.010381</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.875874</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.832090</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.865465</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.856512</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.862406</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col8\" class=\"data row4 col8\" >0.876640</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col9\" class=\"data row4 col9\" >0.869394</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col10\" class=\"data row4 col10\" >0.832265</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col11\" class=\"data row4 col11\" >0.860329</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row4_col12\" class=\"data row4 col12\" >0.884058</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >3 <= text wordCnt < 10</th>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.812854</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.768257</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.044597</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.817684</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.756335</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.817309</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.768688</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.806404</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col8\" class=\"data row5 col8\" >0.778330</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col9\" class=\"data row5 col9\" >0.808332</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col10\" class=\"data row5 col10\" >0.760458</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col11\" class=\"data row5 col11\" >0.814543</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row5_col12\" class=\"data row5 col12\" >0.777474</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >10 <= text wordCnt < 30</th>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.741474</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.669308</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.072167</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.745716</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.673179</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.750123</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.665304</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.736195</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col8\" class=\"data row6 col8\" >0.675460</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col9\" class=\"data row6 col9\" >0.730688</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col10\" class=\"data row6 col10\" >0.665055</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col11\" class=\"data row6 col11\" >0.744649</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row6_col12\" class=\"data row6 col12\" >0.667540</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >30 <= text wordCnt < 100</th>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.709932</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.628299</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.081633</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.718154</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.681121</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.747507</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.513658</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.707269</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col8\" class=\"data row7 col8\" >0.557949</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col9\" class=\"data row7 col9\" >0.678767</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col10\" class=\"data row7 col10\" >0.631203</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col11\" class=\"data row7 col11\" >0.697962</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row7_col12\" class=\"data row7 col12\" >0.757566</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >0 <= selected text wordCnt < 3</th>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col0\" class=\"data row8 col0\" >0.724423</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col1\" class=\"data row8 col1\" >0.619471</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col2\" class=\"data row8 col2\" >0.104952</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col3\" class=\"data row8 col3\" >0.725956</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col4\" class=\"data row8 col4\" >0.608738</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col5\" class=\"data row8 col5\" >0.719731</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col6\" class=\"data row8 col6\" >0.603934</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col7\" class=\"data row8 col7\" >0.736596</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col8\" class=\"data row8 col8\" >0.637387</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col9\" class=\"data row8 col9\" >0.725554</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col10\" class=\"data row8 col10\" >0.628376</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col11\" class=\"data row8 col11\" >0.714279</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row8_col12\" class=\"data row8 col12\" >0.618920</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >3 <= selected text wordCnt < 10</th>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col0\" class=\"data row9 col0\" >0.727249</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col1\" class=\"data row9 col1\" >0.673616</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col2\" class=\"data row9 col2\" >0.053633</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col3\" class=\"data row9 col3\" >0.736818</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col4\" class=\"data row9 col4\" >0.675946</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col5\" class=\"data row9 col5\" >0.743982</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col6\" class=\"data row9 col6\" >0.675179</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col7\" class=\"data row9 col7\" >0.708983</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col8\" class=\"data row9 col8\" >0.669110</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col9\" class=\"data row9 col9\" >0.706766</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col10\" class=\"data row9 col10\" >0.660006</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col11\" class=\"data row9 col11\" >0.739699</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row9_col12\" class=\"data row9 col12\" >0.687841</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10 <= selected text wordCnt < 30</th>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col0\" class=\"data row10 col0\" >0.874227</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col1\" class=\"data row10 col1\" >0.855123</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col2\" class=\"data row10 col2\" >0.019104</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col3\" class=\"data row10 col3\" >0.876218</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col4\" class=\"data row10 col4\" >0.858560</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col5\" class=\"data row10 col5\" >0.883774</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col6\" class=\"data row10 col6\" >0.858275</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col7\" class=\"data row10 col7\" >0.864179</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col8\" class=\"data row10 col8\" >0.861089</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col9\" class=\"data row10 col9\" >0.867861</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col10\" class=\"data row10 col10\" >0.849283</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col11\" class=\"data row10 col11\" >0.879101</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row10_col12\" class=\"data row10 col12\" >0.848406</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >30 <= selected text wordCnt < 100</th>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col0\" class=\"data row11 col0\" >0.891779</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.887863</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col2\" class=\"data row11 col2\" >0.003916</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col3\" class=\"data row11 col3\" >0.923796</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col4\" class=\"data row11 col4\" >0.835486</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col5\" class=\"data row11 col5\" >0.913939</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col6\" class=\"data row11 col6\" >0.801239</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col7\" class=\"data row11 col7\" >0.890382</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col8\" class=\"data row11 col8\" >0.901795</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col9\" class=\"data row11 col9\" >0.874199</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col10\" class=\"data row11 col10\" >0.908811</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col11\" class=\"data row11 col11\" >0.856580</td>\n","                        <td id=\"T_dfc9a340_9960_11ea_bf5f_0242ac1c0002row11_col12\" class=\"data row11 col12\" >0.991987</td>\n","            </tr>\n","    </tbody></table>"],"text/plain":["<pandas.io.formats.style.Styler at 0x7f11da1967f0>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"xxbg-_R_p2dE","colab_type":"text"},"source":["# Model evaluation (best model)"]},{"cell_type":"code","metadata":{"id":"KgsapEFup3zY","colab_type":"code","outputId":"fb2660f2-99cd-4832-a428-156103909010","executionInfo":{"status":"ok","timestamp":1589845198548,"user_tz":180,"elapsed":13434,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}},"colab":{"base_uri":"https://localhost:8080/","height":588}},"source":["display(evaluate_model_kfold(k_fold_best, config['N_FOLDS']).style.applymap(color_map))"],"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/html":["<style  type=\"text/css\" >\n","    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col0 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col1 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col2 {\n","            color:  red;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col3 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col4 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col5 {\n","            color:  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#T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col6 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col7 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col8 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col9 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col10 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col11 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col12 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col0 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col1 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col2 {\n","            color:  red;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col3 {\n","            color:  green;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col4 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col5 {\n","            color:  green;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col6 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col7 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col8 {\n","            color:  green;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col9 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col10 {\n","            color:  green;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col11 {\n","            color:  black;\n","        }    #T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col12 {\n","            color:  green;\n","        }</style><table id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Train</th>        <th class=\"col_heading level0 col1\" >Valid</th>        <th class=\"col_heading level0 col2\" >Var</th>        <th class=\"col_heading level0 col3\" >Train_fold_1</th>        <th class=\"col_heading level0 col4\" >Valid_fold_1</th>        <th class=\"col_heading level0 col5\" >Train_fold_2</th>        <th class=\"col_heading level0 col6\" >Valid_fold_2</th>        <th class=\"col_heading level0 col7\" >Train_fold_3</th>        <th class=\"col_heading level0 col8\" >Valid_fold_3</th>        <th class=\"col_heading level0 col9\" >Train_fold_4</th>        <th class=\"col_heading level0 col10\" >Valid_fold_4</th>        <th class=\"col_heading level0 col11\" >Train_fold_5</th>        <th class=\"col_heading level0 col12\" >Valid_fold_5</th>    </tr>    <tr>        <th class=\"index_name level0\" >Metric/Jaccard</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n","                <tr>\n","                        <th id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >Overall</th>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.748705</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.704267</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.044438</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.754891</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.703807</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.744480</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.697949</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.748355</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col8\" class=\"data row0 col8\" >0.710718</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col9\" class=\"data row0 col9\" >0.733859</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col10\" class=\"data row0 col10\" >0.696076</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col11\" class=\"data row0 col11\" >0.761939</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row0_col12\" class=\"data row0 col12\" >0.712786</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >Sentiment negative</th>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.603501</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.524714</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.078787</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.615804</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.512979</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.594027</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.518962</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.607599</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col8\" class=\"data row1 col8\" >0.533870</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col9\" class=\"data row1 col9\" >0.577445</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col10\" class=\"data row1 col10\" >0.525114</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col11\" class=\"data row1 col11\" >0.622631</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row1_col12\" class=\"data row1 col12\" >0.532643</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >Sentiment neutral</th>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.974192</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.972297</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.001895</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.974177</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.973149</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.973667</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.972521</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.973962</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col8\" class=\"data row2 col8\" >0.973509</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col9\" class=\"data row2 col9\" >0.974245</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col10\" class=\"data row2 col10\" >0.968769</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col11\" class=\"data row2 col11\" >0.974910</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row2_col12\" class=\"data row2 col12\" >0.973537</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >Sentiment positive</th>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.588263</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.519859</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.068404</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.596971</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.527764</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.584038</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.504392</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col7\" class=\"data row3 col7\" >0.583704</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col8\" class=\"data row3 col8\" >0.530744</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col9\" class=\"data row3 col9\" >0.564257</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col10\" class=\"data row3 col10\" >0.497951</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col11\" class=\"data row3 col11\" >0.612345</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row3_col12\" class=\"data row3 col12\" >0.538444</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >0 <= text wordCnt < 3</th>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.864436</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.854533</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.009903</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.871795</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.828358</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.859159</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.856512</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.860679</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col8\" class=\"data row4 col8\" >0.864829</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col9\" class=\"data row4 col9\" >0.867576</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col10\" class=\"data row4 col10\" >0.846154</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col11\" class=\"data row4 col11\" >0.862969</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row4_col12\" class=\"data row4 col12\" >0.876812</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >3 <= text wordCnt < 10</th>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.799207</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.766639</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.032568</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.805096</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.758139</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.793530</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.767997</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.799139</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col8\" class=\"data row5 col8\" >0.773549</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col9\" class=\"data row5 col9\" >0.790451</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col10\" class=\"data row5 col10\" >0.754695</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col11\" class=\"data row5 col11\" >0.807819</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row5_col12\" class=\"data row5 col12\" >0.778814</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >10 <= text wordCnt < 30</th>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.717788</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.665862</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.051926</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.724381</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.669312</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.713956</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.657798</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.717073</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col8\" class=\"data row6 col8\" >0.673586</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col9\" class=\"data row6 col9\" >0.699401</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col10\" class=\"data row6 col10\" >0.658246</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col11\" class=\"data row6 col11\" >0.734131</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row6_col12\" class=\"data row6 col12\" >0.670370</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >30 <= text wordCnt < 100</th>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.682054</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.620321</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.061733</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.675092</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.674830</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.702677</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.477686</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.686582</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col8\" class=\"data row7 col8\" >0.594777</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col9\" class=\"data row7 col9\" >0.657739</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col10\" class=\"data row7 col10\" >0.591989</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col11\" class=\"data row7 col11\" >0.688180</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row7_col12\" class=\"data row7 col12\" >0.762324</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >0 <= selected text wordCnt < 3</th>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col0\" class=\"data row8 col0\" >0.688970</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col1\" class=\"data row8 col1\" >0.611976</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col2\" class=\"data row8 col2\" >0.076993</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col3\" class=\"data row8 col3\" >0.687472</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col4\" class=\"data row8 col4\" >0.598511</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col5\" class=\"data row8 col5\" >0.682609</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col6\" class=\"data row8 col6\" >0.607948</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col7\" class=\"data row8 col7\" >0.681532</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col8\" class=\"data row8 col8\" >0.607070</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col9\" class=\"data row8 col9\" >0.673522</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col10\" class=\"data row8 col10\" >0.608232</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col11\" class=\"data row8 col11\" >0.719713</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row8_col12\" class=\"data row8 col12\" >0.638120</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >3 <= selected text wordCnt < 10</th>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col0\" class=\"data row9 col0\" >0.712969</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col1\" class=\"data row9 col1\" >0.674647</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col2\" class=\"data row9 col2\" >0.038321</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col3\" class=\"data row9 col3\" >0.728061</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col4\" class=\"data row9 col4\" >0.684173</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col5\" class=\"data row9 col5\" >0.704362</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col6\" class=\"data row9 col6\" >0.658324</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col7\" class=\"data row9 col7\" >0.718671</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col8\" class=\"data row9 col8\" >0.687396</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col9\" class=\"data row9 col9\" >0.695011</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col10\" class=\"data row9 col10\" >0.666735</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col11\" class=\"data row9 col11\" >0.718738</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row9_col12\" class=\"data row9 col12\" >0.676608</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10 <= selected text wordCnt < 30</th>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col0\" class=\"data row10 col0\" >0.866126</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col1\" class=\"data row10 col1\" >0.853333</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col2\" class=\"data row10 col2\" >0.012794</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col3\" class=\"data row10 col3\" >0.870583</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col4\" class=\"data row10 col4\" >0.854093</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col5\" class=\"data row10 col5\" >0.870069</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col6\" class=\"data row10 col6\" >0.856924</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col7\" class=\"data row10 col7\" >0.866798</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col8\" class=\"data row10 col8\" >0.865270</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col9\" class=\"data row10 col9\" >0.854989</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col10\" class=\"data row10 col10\" >0.843278</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col11\" class=\"data row10 col11\" >0.868192</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row10_col12\" class=\"data row10 col12\" >0.847097</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >30 <= selected text wordCnt < 100</th>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col0\" class=\"data row11 col0\" >0.894772</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.886240</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col2\" class=\"data row11 col2\" >0.008532</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col3\" class=\"data row11 col3\" >0.923796</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col4\" class=\"data row11 col4\" >0.839930</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col5\" class=\"data row11 col5\" >0.915328</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col6\" class=\"data row11 col6\" >0.791973</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col7\" class=\"data row11 col7\" >0.890605</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col8\" class=\"data row11 col8\" >0.901795</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col9\" class=\"data row11 col9\" >0.884210</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col10\" class=\"data row11 col10\" >0.908811</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col11\" class=\"data row11 col11\" >0.859920</td>\n","                        <td id=\"T_e2363c56_9960_11ea_bf5f_0242ac1c0002row11_col12\" class=\"data row11 col12\" >0.988690</td>\n","            </tr>\n","    </tbody></table>"],"text/plain":["<pandas.io.formats.style.Styler at 0x7f13ea609320>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"yP3xG-Cenvry","colab_type":"text"},"source":["# Visualize predictions"]},{"cell_type":"code","metadata":{"_kg_hide-input":true,"id":"cGxcMVRMnvrz","colab_type":"code","outputId":"afd0e23b-d8f4-480e-968c-ce2d2c8ad057","executionInfo":{"status":"ok","timestamp":1589845198551,"user_tz":180,"elapsed":13433,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}},"colab":{"base_uri":"https://localhost:8080/","height":738}},"source":["display(k_fold[[c for c in k_fold.columns if not (c.startswith('textID') or \n","                                                  c.startswith('text_len') or \n","                                                  c.startswith('selected_text_len') or \n","                                                  c.startswith('text_wordCnt') or \n","                                                  c.startswith('selected_text_wordCnt') or \n","                                                  c.startswith('fold_') or \n","                                                  c.startswith('start_fold_') or \n","                                                  c.startswith('end_fold_'))]].head(15))"],"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>selected_text</th>\n","      <th>sentiment</th>\n","      <th>jaccard</th>\n","      <th>text_tokenCnt</th>\n","      <th>selected_text_tokenCnt</th>\n","      <th>prediction_fold_1</th>\n","      <th>jaccard_fold_1</th>\n","      <th>prediction_fold_2</th>\n","      <th>jaccard_fold_2</th>\n","      <th>prediction_fold_3</th>\n","      <th>jaccard_fold_3</th>\n","      <th>prediction_fold_4</th>\n","      <th>jaccard_fold_4</th>\n","      <th>prediction_fold_5</th>\n","      <th>jaccard_fold_5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>i`d have responded, if i were going</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>10</td>\n","      <td>10</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>1.000000</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>1.000000</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>1.000000</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>1.000000</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>sooo sad i will miss you here in san diego!!!</td>\n","      <td>sooo sad</td>\n","      <td>negative</td>\n","      <td>0.200000</td>\n","      <td>13</td>\n","      <td>3</td>\n","      <td>sooo sad</td>\n","      <td>1.000000</td>\n","      <td>sooo sad</td>\n","      <td>1.000000</td>\n","      <td>sooo sad</td>\n","      <td>1.000000</td>\n","      <td>sooo sad</td>\n","      <td>1.000000</td>\n","      <td>sooo sad</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>my boss is bullying me...</td>\n","      <td>bullying me</td>\n","      <td>negative</td>\n","      <td>0.166667</td>\n","      <td>6</td>\n","      <td>2</td>\n","      <td>bullying</td>\n","      <td>0.500000</td>\n","      <td>bullying me...</td>\n","      <td>0.333333</td>\n","      <td>bullying</td>\n","      <td>0.500000</td>\n","      <td>bullying me...</td>\n","      <td>0.333333</td>\n","      <td>bullying me...</td>\n","      <td>0.333333</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>what interview! leave me alone</td>\n","      <td>leave me alone</td>\n","      <td>negative</td>\n","      <td>0.600000</td>\n","      <td>6</td>\n","      <td>3</td>\n","      <td>leave me alone</td>\n","      <td>1.000000</td>\n","      <td>leave me alone</td>\n","      <td>1.000000</td>\n","      <td>leave me alone</td>\n","      <td>1.000000</td>\n","      <td>leave me alone</td>\n","      <td>1.000000</td>\n","      <td>leave me alone</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>sons of ****, why couldn`t they put them on th...</td>\n","      <td>sons of ****,</td>\n","      <td>negative</td>\n","      <td>0.214286</td>\n","      <td>17</td>\n","      <td>4</td>\n","      <td>sons of ****,</td>\n","      <td>1.000000</td>\n","      <td>sons of ****,</td>\n","      <td>1.000000</td>\n","      <td>****,</td>\n","      <td>0.333333</td>\n","      <td>sons of ****,</td>\n","      <td>1.000000</td>\n","      <td>sons of ****,</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n","      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>27</td>\n","      <td>27</td>\n","      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n","      <td>1.000000</td>\n","      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n","      <td>1.000000</td>\n","      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n","      <td>1.000000</td>\n","      <td>some shameless plugging for the best rangers f...</td>\n","      <td>0.833333</td>\n","      <td>some shameless plugging for the best rangers f...</td>\n","      <td>0.833333</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>2am feedings for the baby are fun when he is a...</td>\n","      <td>fun</td>\n","      <td>positive</td>\n","      <td>0.071429</td>\n","      <td>17</td>\n","      <td>1</td>\n","      <td>fun</td>\n","      <td>1.000000</td>\n","      <td>fun</td>\n","      <td>1.000000</td>\n","      <td>fun</td>\n","      <td>1.000000</td>\n","      <td>fun</td>\n","      <td>1.000000</td>\n","      <td>fun</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>soooo high</td>\n","      <td>soooo high</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>soooo high</td>\n","      <td>1.000000</td>\n","      <td>soooo high</td>\n","      <td>1.000000</td>\n","      <td>soooo high</td>\n","      <td>1.000000</td>\n","      <td>soooo high</td>\n","      <td>1.000000</td>\n","      <td>soooo high</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>both of you</td>\n","      <td>both of you</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>both of you</td>\n","      <td>1.000000</td>\n","      <td>both of you</td>\n","      <td>1.000000</td>\n","      <td>both of you</td>\n","      <td>1.000000</td>\n","      <td>both of you</td>\n","      <td>1.000000</td>\n","      <td>both of you</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>journey!? wow... u just became cooler.  hehe.....</td>\n","      <td>wow... u just became cooler.</td>\n","      <td>positive</td>\n","      <td>0.500000</td>\n","      <td>19</td>\n","      <td>7</td>\n","      <td>wow... u just became cooler.</td>\n","      <td>1.000000</td>\n","      <td>wow... u just became cooler.</td>\n","      <td>1.000000</td>\n","      <td>wow... u just became cooler.</td>\n","      <td>1.000000</td>\n","      <td>wow...</td>\n","      <td>0.200000</td>\n","      <td>wow... u just became cooler.</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>as much as i love to be hopeful, i reckon the ...</td>\n","      <td>as much as i love to be hopeful, i reckon the ...</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>27</td>\n","      <td>27</td>\n","      <td>as much as i love to be hopeful, i reckon the ...</td>\n","      <td>1.000000</td>\n","      <td>as much as i love to be hopeful, i reckon the ...</td>\n","      <td>1.000000</td>\n","      <td>as much as i love to be hopeful, i reckon the ...</td>\n","      <td>1.000000</td>\n","      <td>as much as i love to be hopeful, i reckon the ...</td>\n","      <td>1.000000</td>\n","      <td>as much as i love to be hopeful, i reckon the ...</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>i really really like the song love story by ta...</td>\n","      <td>like</td>\n","      <td>positive</td>\n","      <td>0.100000</td>\n","      <td>12</td>\n","      <td>1</td>\n","      <td>i really really like</td>\n","      <td>0.333333</td>\n","      <td>i really really like</td>\n","      <td>0.333333</td>\n","      <td>like</td>\n","      <td>1.000000</td>\n","      <td>i really really like</td>\n","      <td>0.333333</td>\n","      <td>i really really like</td>\n","      <td>0.333333</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>my sharpie is running dangerously low on ink</td>\n","      <td>dangerously</td>\n","      <td>negative</td>\n","      <td>0.125000</td>\n","      <td>9</td>\n","      <td>1</td>\n","      <td>dangerously</td>\n","      <td>1.000000</td>\n","      <td>dangerously</td>\n","      <td>1.000000</td>\n","      <td>dangerously</td>\n","      <td>1.000000</td>\n","      <td>dangerously low</td>\n","      <td>0.500000</td>\n","      <td>dangerously</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>i want to go to music tonight but i lost my vo...</td>\n","      <td>lost</td>\n","      <td>negative</td>\n","      <td>0.100000</td>\n","      <td>13</td>\n","      <td>1</td>\n","      <td>lost</td>\n","      <td>1.000000</td>\n","      <td>lost</td>\n","      <td>1.000000</td>\n","      <td>lost</td>\n","      <td>1.000000</td>\n","      <td>lost my voice.</td>\n","      <td>0.333333</td>\n","      <td>lost</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>test test from the lg env2</td>\n","      <td>test test from the lg env2</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>test test from the lg env2</td>\n","      <td>1.000000</td>\n","      <td>test test from the lg env2</td>\n","      <td>1.000000</td>\n","      <td>test test from the lg env2</td>\n","      <td>1.000000</td>\n","      <td>test test from the lg env2</td>\n","      <td>1.000000</td>\n","      <td>test test from the lg env2</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                 text  ... jaccard_fold_5\n","0                 i`d have responded, if i were going  ...       1.000000\n","1       sooo sad i will miss you here in san diego!!!  ...       1.000000\n","2                           my boss is bullying me...  ...       0.333333\n","3                      what interview! leave me alone  ...       1.000000\n","4   sons of ****, why couldn`t they put them on th...  ...       1.000000\n","5   http://www.dothebouncy.com/smf - some shameles...  ...       0.833333\n","6   2am feedings for the baby are fun when he is a...  ...       1.000000\n","7                                          soooo high  ...       1.000000\n","8                                         both of you  ...       1.000000\n","9   journey!? wow... u just became cooler.  hehe.....  ...       1.000000\n","10  as much as i love to be hopeful, i reckon the ...  ...       1.000000\n","11  i really really like the song love story by ta...  ...       0.333333\n","12       my sharpie is running dangerously low on ink  ...       1.000000\n","13  i want to go to music tonight but i lost my vo...  ...       1.000000\n","14                         test test from the lg env2  ...       1.000000\n","\n","[15 rows x 16 columns]"]},"metadata":{"tags":[]}}]}]}