(Model 3) WordEmbedding_Character_representation .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DS = pd.read_csv(\"ObjectSubset150SentenceLength.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DS['tag'].fillna('text', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = list(set(DS[\"word\"].values))\n",
    "n_words = len(words)\n",
    "tags = list(set(DS[\"tag\"].values))\n",
    "n_tags = len(tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SentenceGetter(object):\n",
    "    def __init__(self, data):\n",
    "        self.n_sent = 1\n",
    "        self.data = data\n",
    "        self.empty = False\n",
    "        agg_func = lambda s: [(w, t) for w, t in zip(s[\"word\"].values.tolist(),\n",
    "                                                           s[\"tag\"].values.tolist())]\n",
    "        self.grouped = self.data.groupby(\"text_ID\").apply(agg_func)\n",
    "        self.sentences = [s for s in self.grouped]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "getter = SentenceGetter(DS)\n",
    "sentences = getter.sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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_ID</th>\n",
       "      <th>word</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46809</td>\n",
       "      <td>RR</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46809</td>\n",
       "      <td>130/80</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46809</td>\n",
       "      <td>mmHg.</td>\n",
       "      <td>text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47482</td>\n",
       "      <td>RR142/89mmHg,</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47482</td>\n",
       "      <td>HR</td>\n",
       "      <td>text</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   text_ID           word     tag\n",
       "0    46809             RR  object\n",
       "1    46809         130/80  object\n",
       "2    46809          mmHg.    text\n",
       "3    47482  RR142/89mmHg,  object\n",
       "4    47482             HR    text"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Pt.', 'text'),\n",
       " ('tehtud', 'text'),\n",
       " ('3', 'text'),\n",
       " ('plasmaferees,', 'text'),\n",
       " ('talus', 'text'),\n",
       " ('hästi.', 'text'),\n",
       " ('Eemaldatud', 'text'),\n",
       " ('700', 'text'),\n",
       " ('ml', 'text'),\n",
       " ('plasmat.', 'text'),\n",
       " ('RR', 'object'),\n",
       " ('112/60', 'object'),\n",
       " ('mmHg.', 'text'),\n",
       " ('Tgasi', 'text'),\n",
       " ('22.10.09.', 'text')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_len = 150\n",
    "max_len_char = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tag2idx = {t: i + 1 for i, t in enumerate(tags)}\n",
    "tag2idx[\"PAD\"] = 0\n",
    "idx2tag = {i: w for w, i in tag2idx.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'object': 1, 'text': 2, 'PAD': 0}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag2idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "chars = set([w_i for w in words for w_i in w])\n",
    "n_chars = len(chars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "char2idx = {c: i + 2 for i, c in enumerate(chars)}\n",
    "char2idx[\"UNK\"] = 1 #For predictions\n",
    "char2idx[\"PAD\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx2char = {i: w for w, i in char2idx.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_char = []\n",
    "for sentence in sentences:\n",
    "    sent_seq = []\n",
    "    for i in range(max_len):\n",
    "        word_seq = []\n",
    "        for j in range(max_len_char):\n",
    "            try:\n",
    "                word_seq.append(char2idx.get(sentence[i][0][j]))\n",
    "            except:\n",
    "                word_seq.append(char2idx.get(\"PAD\"))\n",
    "        sent_seq.append(word_seq)\n",
    "    X_char.append(np.array(sent_seq))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = [[tag2idx[w[1]] for w  in s] for s in sentences]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13643"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pad_sequences(maxlen=max_len, sequences=y, value=tag2idx[\"PAD\"], padding='post', truncating='post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 10)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_char[1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'object': 1, 'text': 2, 'PAD': 0}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag2idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_char_tr, X_char_te, y_tr,  y_te= train_test_split(X_char, y, test_size=0.2, random_state=2018)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ü': 2,\n",
       " '+': 3,\n",
       " '–': 4,\n",
       " 'U': 5,\n",
       " '/': 6,\n",
       " ';': 7,\n",
       " 'o': 8,\n",
       " '0': 9,\n",
       " '�': 10,\n",
       " 'T': 11,\n",
       " '´': 12,\n",
       " 'W': 13,\n",
       " 'Õ': 14,\n",
       " 'y': 15,\n",
       " ')': 16,\n",
       " 'x': 17,\n",
       " 'O': 18,\n",
       " 'Y': 19,\n",
       " '2': 20,\n",
       " 'ä': 21,\n",
       " 'V': 22,\n",
       " 'w': 23,\n",
       " 'l': 24,\n",
       " 'm': 25,\n",
       " 'H': 26,\n",
       " 'n': 27,\n",
       " 'ö': 28,\n",
       " '>': 29,\n",
       " '½': 30,\n",
       " 'p': 31,\n",
       " 'e': 32,\n",
       " 's': 33,\n",
       " 'Ä': 34,\n",
       " 'r': 35,\n",
       " 'z': 36,\n",
       " '.': 37,\n",
       " '1': 38,\n",
       " '7': 39,\n",
       " '%': 40,\n",
       " '5': 41,\n",
       " 'j': 42,\n",
       " 'i': 43,\n",
       " '=': 44,\n",
       " 'g': 45,\n",
       " '~': 46,\n",
       " 'A': 47,\n",
       " 'E': 48,\n",
       " 'M': 49,\n",
       " 'ˇ': 50,\n",
       " '[': 51,\n",
       " 'k': 52,\n",
       " 'D': 53,\n",
       " '(': 54,\n",
       " 'd': 55,\n",
       " 't': 56,\n",
       " '!': 57,\n",
       " 'S': 58,\n",
       " '9': 59,\n",
       " 'q': 60,\n",
       " 'G': 61,\n",
       " 'f': 62,\n",
       " 'c': 63,\n",
       " '8': 64,\n",
       " 'ü': 65,\n",
       " '?': 66,\n",
       " '_': 67,\n",
       " 'Q': 68,\n",
       " 'h': 69,\n",
       " 'L': 70,\n",
       " '#': 71,\n",
       " 'X': 72,\n",
       " ']': 73,\n",
       " '&': 74,\n",
       " 'Ö': 75,\n",
       " '*': 76,\n",
       " '\"': 77,\n",
       " 'F': 78,\n",
       " ',': 79,\n",
       " 'N': 80,\n",
       " '3': 81,\n",
       " 'J': 82,\n",
       " '6': 83,\n",
       " 'B': 84,\n",
       " '<': 85,\n",
       " '\\\\': 86,\n",
       " 'R': 87,\n",
       " 'I': 88,\n",
       " 'b': 89,\n",
       " 'š': 90,\n",
       " '°': 91,\n",
       " '4': 92,\n",
       " 'Z': 93,\n",
       " '-': 94,\n",
       " 'C': 95,\n",
       " '`': 96,\n",
       " \"'\": 97,\n",
       " 'a': 98,\n",
       " 'P': 99,\n",
       " ':': 100,\n",
       " 'ž': 101,\n",
       " 'K': 102,\n",
       " 'õ': 103,\n",
       " 'u': 104,\n",
       " 'v': 105,\n",
       " 'UNK': 1,\n",
       " 'PAD': 0}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char2idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[99, 56, 37, ...,  0,  0,  0],\n",
       "       [56, 32, 69, ...,  0,  0,  0],\n",
       "       [81,  0,  0, ...,  0,  0,  0],\n",
       "       ...,\n",
       "       [ 0,  0,  0, ...,  0,  0,  0],\n",
       "       [ 0,  0,  0, ...,  0,  0,  0],\n",
       "       [ 0,  0,  0, ...,  0,  0,  0]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_char[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10914, 150)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_len_char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model, Input\n",
    "from keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Conv1D\n",
    "from keras.layers import Flatten, Bidirectional, concatenate, SpatialDropout1D, GlobalMaxPooling1D\n",
    "\n",
    "\n",
    "# input and embeddings for characters\n",
    "char_in = Input(shape=(max_len,max_len_char))\n",
    "\n",
    "emb = TimeDistributed(Embedding(input_dim=n_chars + 2, output_dim=20,\n",
    "                           input_length=max_len_char))(input)\n",
    "enc = TimeDistributed(LSTM(units=20, return_sequences=False,recurrent_dropout=0.5))(emb)\n",
    "x = SpatialDropout1D(0.3)(enc)\n",
    "model = Bidirectional(LSTM(units=20, return_sequences=True,\n",
    "                               recurrent_dropout=0.5))(x)\n",
    "\n",
    "out = TimeDistributed(Dense(n_tags + 1, activation=\"softmax\"))(model)\n",
    "\n",
    "model = Model( char_in, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "____________________________________________________________________________________________________\n",
      "Layer (type)                                 Output Shape                            Param #        \n",
      "====================================================================================================\n",
      "input_1 (InputLayer)                         (None, 150, 10)                         0              \n",
      "____________________________________________________________________________________________________\n",
      "time_distributed_1 (TimeDistributed)         (None, 150, 10, 20)                     2120           \n",
      "____________________________________________________________________________________________________\n",
      "time_distributed_2 (TimeDistributed)         (None, 150, 20)                         3280           \n",
      "____________________________________________________________________________________________________\n",
      "spatial_dropout1d_1 (SpatialDropout1D)       (None, 150, 20)                         0              \n",
      "____________________________________________________________________________________________________\n",
      "bidirectional_1 (Bidirectional)              (None, 150, 40)                         6560           \n",
      "____________________________________________________________________________________________________\n",
      "time_distributed_3 (TimeDistributed)         (None, 150, 3)                          123            \n",
      "====================================================================================================\n",
      "Total params: 12,083\n",
      "Trainable params: 12,083\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"acc\"])\n",
    "model.summary(line_length=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from keras.utils.vis_utils import plot_model\n",
    "plot_model(model, to_file='CharsEmbedding_Model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array(X_char_tr).reshape((len(X_char_tr), max_len, max_len_char))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10914, 150, 10)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 10)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_char_tr[1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10914, 150, 10)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(X_char_tr).reshape((len(X_char_tr), max_len, max_len_char)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kasutaja\\Anaconda3\\envs\\myEnv\\lib\\site-packages\\tensorflow_core\\python\\framework\\indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9822 samples, validate on 1092 samples\n",
      "Epoch 1/10\n",
      "9822/9822 [==============================] - 77s 8ms/step - loss: 0.1363 - acc: 0.9700 - val_loss: 0.0276 - val_acc: 0.9912\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.99120, saving model to CharacterEmbeddingObject-0.99.hdf5\n",
      "Epoch 2/10\n",
      "9822/9822 [==============================] - 77s 8ms/step - loss: 0.0203 - acc: 0.9933 - val_loss: 0.0131 - val_acc: 0.9962\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.99120 to 0.99615, saving model to CharacterEmbeddingObject-1.00.hdf5\n",
      "Epoch 3/10\n",
      "9822/9822 [==============================] - 78s 8ms/step - loss: 0.0112 - acc: 0.9969 - val_loss: 0.0093 - val_acc: 0.9980\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.99615 to 0.99797, saving model to CharacterEmbeddingObject-1.00.hdf5\n",
      "Epoch 4/10\n",
      "9822/9822 [==============================] - 77s 8ms/step - loss: 0.0092 - acc: 0.9979 - val_loss: 0.0079 - val_acc: 0.9982\n",
      "\n",
      "Epoch 00004: val_acc improved from 0.99797 to 0.99822, saving model to CharacterEmbeddingObject-1.00.hdf5\n",
      "Epoch 5/10\n",
      "9822/9822 [==============================] - 77s 8ms/step - loss: 0.0079 - acc: 0.9983 - val_loss: 0.0065 - val_acc: 0.9986\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.99822 to 0.99858, saving model to CharacterEmbeddingObject-1.00.hdf5\n",
      "Epoch 6/10\n",
      "9822/9822 [==============================] - 77s 8ms/step - loss: 0.0070 - acc: 0.9985 - val_loss: 0.0062 - val_acc: 0.9987\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.99858 to 0.99869, saving model to CharacterEmbeddingObject-1.00.hdf5\n",
      "Epoch 7/10\n",
      "9822/9822 [==============================] - 77s 8ms/step - loss: 0.0063 - acc: 0.9986 - val_loss: 0.0055 - val_acc: 0.9988\n",
      "\n",
      "Epoch 00007: val_acc improved from 0.99869 to 0.99879, saving model to CharacterEmbeddingObject-1.00.hdf5\n",
      "Epoch 8/10\n",
      "9822/9822 [==============================] - 76s 8ms/step - loss: 0.0058 - acc: 0.9987 - val_loss: 0.0050 - val_acc: 0.9988\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.99879\n",
      "Epoch 9/10\n",
      "9822/9822 [==============================] - 76s 8ms/step - loss: 0.0054 - acc: 0.9987 - val_loss: 0.0044 - val_acc: 0.9988\n",
      "\n",
      "Epoch 00009: val_acc improved from 0.99879 to 0.99883, saving model to CharacterEmbeddingObject-1.00.hdf5\n",
      "Epoch 10/10\n",
      "9822/9822 [==============================] - 76s 8ms/step - loss: 0.0055 - acc: 0.9987 - val_loss: 0.0042 - val_acc: 0.9989\n",
      "\n",
      "Epoch 00010: val_acc improved from 0.99883 to 0.99888, saving model to CharacterEmbeddingObject-1.00.hdf5\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "# Saving the best model only\n",
    "filepath=\"CharacterEmbeddingObject-{val_acc:.2f}.hdf5\"\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
    "callbacks_list = [checkpoint]\n",
    "\n",
    "# Fit the best model\n",
    "history = model.fit(np.array(X_char_tr).reshape((len(X_char_tr), max_len, max_len_char)),\n",
    "                    np.array(y_tr).reshape(len(y_tr), max_len, 1),\n",
    "                    batch_size=32, epochs=10, validation_split=0.1, verbose=1,callbacks=callbacks_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist = pd.DataFrame(history.history)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.plot(hist[\"acc\"])\n",
    "plt.plot(hist[\"val_acc\"])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(\n",
    "                        np.array(X_char_te).reshape((len(X_char_te),\n",
    "                                                     max_len, max_len_char)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.9943741e-05, 1.8112497e-02, 9.8184752e-01],\n",
       "       [1.7106719e-05, 6.0757278e-03, 9.9390715e-01],\n",
       "       [2.1197138e-05, 1.5815673e-02, 9.8416317e-01],\n",
       "       [1.6907528e-05, 8.2747685e-03, 9.9170834e-01],\n",
       "       [4.2534346e-05, 5.2456460e-03, 9.9471188e-01],\n",
       "       [1.6567769e-04, 9.9800497e-01, 1.8293320e-03],\n",
       "       [7.2796924e-05, 9.8159480e-01, 1.8332453e-02],\n",
       "       [3.9142353e-05, 2.4532879e-02, 9.7542793e-01],\n",
       "       [2.6610998e-05, 1.2065326e-02, 9.8790807e-01],\n",
       "       [3.6429519e-05, 1.9053036e-02, 9.8091060e-01],\n",
       "       [5.7651781e-05, 6.8175369e-03, 9.9312478e-01],\n",
       "       [9.9939859e-01, 5.2519474e-04, 7.6203571e-05],\n",
       "       [9.9978966e-01, 2.0152378e-04, 8.8305314e-06],\n",
       "       [9.9992204e-01, 7.5326192e-05, 2.6756513e-06],\n",
       "       [9.9997211e-01, 2.6536500e-05, 1.3129011e-06],\n",
       "       [9.9998915e-01, 1.0068708e-05, 8.1943114e-07],\n",
       "       [9.9999475e-01, 4.6804907e-06, 5.9709498e-07],\n",
       "       [9.9999678e-01, 2.6868729e-06, 4.8095535e-07],\n",
       "       [9.9999785e-01, 1.8153404e-06, 4.1358899e-07],\n",
       "       [9.9999821e-01, 1.3872400e-06, 3.7296454e-07],\n",
       "       [9.9999845e-01, 1.1641997e-06, 3.4870612e-07],\n",
       "       [9.9999857e-01, 1.0440161e-06, 3.3448282e-07],\n",
       "       [9.9999869e-01, 9.7734699e-07, 3.2618041e-07],\n",
       "       [9.9999869e-01, 9.3921096e-07, 3.2127107e-07],\n",
       "       [9.9999869e-01, 9.1667317e-07, 3.1829825e-07],\n",
       "       [9.9999869e-01, 9.0289706e-07, 3.1644109e-07],\n",
       "       [9.9999869e-01, 8.9419257e-07, 3.1524348e-07],\n",
       "       [9.9999881e-01, 8.8851596e-07, 3.1444634e-07],\n",
       "       [9.9999881e-01, 8.8469920e-07, 3.1389985e-07],\n",
       "       [9.9999881e-01, 8.8206241e-07, 3.1351541e-07],\n",
       "       [9.9999881e-01, 8.8019351e-07, 3.1323745e-07],\n",
       "       [9.9999881e-01, 8.7883643e-07, 3.1303290e-07],\n",
       "       [9.9999881e-01, 8.7783121e-07, 3.1287831e-07],\n",
       "       [9.9999881e-01, 8.7707144e-07, 3.1275985e-07],\n",
       "       [9.9999881e-01, 8.7648692e-07, 3.1266771e-07],\n",
       "       [9.9999881e-01, 8.7602984e-07, 3.1259466e-07],\n",
       "       [9.9999881e-01, 8.7566900e-07, 3.1253623e-07],\n",
       "       [9.9999881e-01, 8.7537677e-07, 3.1248854e-07],\n",
       "       [9.9999881e-01, 8.7514138e-07, 3.1244983e-07],\n",
       "       [9.9999881e-01, 8.7494942e-07, 3.1241794e-07],\n",
       "       [9.9999881e-01, 8.7478924e-07, 3.1239111e-07],\n",
       "       [9.9999881e-01, 8.7465577e-07, 3.1236877e-07],\n",
       "       [9.9999881e-01, 8.7454316e-07, 3.1234973e-07],\n",
       "       [9.9999881e-01, 8.7444727e-07, 3.1233364e-07],\n",
       "       [9.9999881e-01, 8.7436717e-07, 3.1231994e-07],\n",
       "       [9.9999881e-01, 8.7429800e-07, 3.1230832e-07],\n",
       "       [9.9999881e-01, 8.7423876e-07, 3.1229789e-07],\n",
       "       [9.9999881e-01, 8.7418545e-07, 3.1228896e-07],\n",
       "       [9.9999881e-01, 8.7414122e-07, 3.1228151e-07],\n",
       "       [9.9999881e-01, 8.7410206e-07, 3.1227526e-07],\n",
       "       [9.9999881e-01, 8.7406789e-07, 3.1226929e-07],\n",
       "       [9.9999881e-01, 8.7403788e-07, 3.1226395e-07],\n",
       "       [9.9999881e-01, 8.7401122e-07, 3.1225946e-07],\n",
       "       [9.9999881e-01, 8.7398786e-07, 3.1225559e-07],\n",
       "       [9.9999881e-01, 8.7396705e-07, 3.1225201e-07],\n",
       "       [9.9999881e-01, 8.7394869e-07, 3.1224906e-07],\n",
       "       [9.9999881e-01, 8.7393204e-07, 3.1224667e-07],\n",
       "       [9.9999881e-01, 8.7391703e-07, 3.1224428e-07],\n",
       "       [9.9999881e-01, 8.7390367e-07, 3.1224189e-07],\n",
       "       [9.9999881e-01, 8.7389202e-07, 3.1224010e-07],\n",
       "       [9.9999881e-01, 8.7388037e-07, 3.1223831e-07],\n",
       "       [9.9999881e-01, 8.7387201e-07, 3.1223712e-07],\n",
       "       [9.9999881e-01, 8.7386366e-07, 3.1223593e-07],\n",
       "       [9.9999881e-01, 8.7385536e-07, 3.1223476e-07],\n",
       "       [9.9999881e-01, 8.7384871e-07, 3.1223416e-07],\n",
       "       [9.9999881e-01, 8.7384200e-07, 3.1223297e-07],\n",
       "       [9.9999881e-01, 8.7383620e-07, 3.1223237e-07],\n",
       "       [9.9999881e-01, 8.7383199e-07, 3.1223178e-07],\n",
       "       [9.9999881e-01, 8.7382620e-07, 3.1223118e-07],\n",
       "       [9.9999881e-01, 8.7382205e-07, 3.1223058e-07],\n",
       "       [9.9999881e-01, 8.7381704e-07, 3.1223030e-07],\n",
       "       [9.9999881e-01, 8.7381369e-07, 3.1222999e-07],\n",
       "       [9.9999881e-01, 8.7381034e-07, 3.1222939e-07],\n",
       "       [9.9999881e-01, 8.7380704e-07, 3.1222939e-07],\n",
       "       [9.9999881e-01, 8.7380369e-07, 3.1222939e-07],\n",
       "       [9.9999881e-01, 8.7380204e-07, 3.1222939e-07],\n",
       "       [9.9999881e-01, 8.7379868e-07, 3.1222939e-07],\n",
       "       [9.9999881e-01, 8.7379703e-07, 3.1222910e-07],\n",
       "       [9.9999881e-01, 8.7379453e-07, 3.1222879e-07],\n",
       "       [9.9999881e-01, 8.7379368e-07, 3.1222939e-07],\n",
       "       [9.9999881e-01, 8.7379203e-07, 3.1222967e-07],\n",
       "       [9.9999881e-01, 8.7378953e-07, 3.1222939e-07],\n",
       "       [9.9999881e-01, 8.7378783e-07, 3.1222939e-07],\n",
       "       [9.9999881e-01, 8.7378703e-07, 3.1222999e-07],\n",
       "       [9.9999881e-01, 8.7378618e-07, 3.1223030e-07],\n",
       "       [9.9999881e-01, 8.7378618e-07, 3.1223058e-07],\n",
       "       [9.9999881e-01, 8.7378453e-07, 3.1223087e-07],\n",
       "       [9.9999881e-01, 8.7378532e-07, 3.1223149e-07],\n",
       "       [9.9999881e-01, 8.7378532e-07, 3.1223237e-07],\n",
       "       [9.9999881e-01, 8.7378703e-07, 3.1223325e-07],\n",
       "       [9.9999881e-01, 8.7378868e-07, 3.1223385e-07],\n",
       "       [9.9999881e-01, 8.7378953e-07, 3.1223476e-07],\n",
       "       [9.9999881e-01, 8.7379368e-07, 3.1223624e-07],\n",
       "       [9.9999881e-01, 8.7379533e-07, 3.1223712e-07],\n",
       "       [9.9999881e-01, 8.7380033e-07, 3.1223951e-07],\n",
       "       [9.9999881e-01, 8.7380704e-07, 3.1224130e-07],\n",
       "       [9.9999881e-01, 8.7381369e-07, 3.1224337e-07],\n",
       "       [9.9999881e-01, 8.7382284e-07, 3.1224667e-07],\n",
       "       [9.9999881e-01, 8.7383285e-07, 3.1224963e-07],\n",
       "       [9.9999881e-01, 8.7384700e-07, 3.1225380e-07],\n",
       "       [9.9999881e-01, 8.7386366e-07, 3.1225886e-07],\n",
       "       [9.9999881e-01, 8.7388366e-07, 3.1226452e-07],\n",
       "       [9.9999881e-01, 8.7390868e-07, 3.1227168e-07],\n",
       "       [9.9999881e-01, 8.7393869e-07, 3.1228001e-07],\n",
       "       [9.9999881e-01, 8.7397370e-07, 3.1229015e-07],\n",
       "       [9.9999881e-01, 8.7401702e-07, 3.1230235e-07],\n",
       "       [9.9999881e-01, 8.7407039e-07, 3.1231696e-07],\n",
       "       [9.9999881e-01, 8.7413542e-07, 3.1233452e-07],\n",
       "       [9.9999881e-01, 8.7421211e-07, 3.1235598e-07],\n",
       "       [9.9999881e-01, 8.7430550e-07, 3.1238190e-07],\n",
       "       [9.9999881e-01, 8.7441720e-07, 3.1241257e-07],\n",
       "       [9.9999881e-01, 8.7455567e-07, 3.1244983e-07],\n",
       "       [9.9999881e-01, 8.7472165e-07, 3.1249570e-07],\n",
       "       [9.9999881e-01, 8.7492270e-07, 3.1255055e-07],\n",
       "       [9.9999881e-01, 8.7516474e-07, 3.1261703e-07],\n",
       "       [9.9999881e-01, 8.7545857e-07, 3.1269784e-07],\n",
       "       [9.9999881e-01, 8.7581429e-07, 3.1279566e-07],\n",
       "       [9.9999881e-01, 8.7624710e-07, 3.1291412e-07],\n",
       "       [9.9999881e-01, 8.7677199e-07, 3.1305828e-07],\n",
       "       [9.9999881e-01, 8.7740773e-07, 3.1323327e-07],\n",
       "       [9.9999881e-01, 8.7818290e-07, 3.1344754e-07],\n",
       "       [9.9999881e-01, 8.7912474e-07, 3.1370863e-07],\n",
       "       [9.9999881e-01, 8.8027747e-07, 3.1402769e-07],\n",
       "       [9.9999881e-01, 8.8168395e-07, 3.1441905e-07],\n",
       "       [9.9999881e-01, 8.8340761e-07, 3.1489918e-07],\n",
       "       [9.9999881e-01, 8.8552645e-07, 3.1549075e-07],\n",
       "       [9.9999881e-01, 8.8814153e-07, 3.1622275e-07],\n",
       "       [9.9999881e-01, 8.9137615e-07, 3.1712997e-07],\n",
       "       [9.9999869e-01, 8.9540600e-07, 3.1826303e-07],\n",
       "       [9.9999869e-01, 9.0045063e-07, 3.1968395e-07],\n",
       "       [9.9999869e-01, 9.0681829e-07, 3.2148012e-07],\n",
       "       [9.9999869e-01, 9.1493081e-07, 3.2376923e-07],\n",
       "       [9.9999869e-01, 9.2538295e-07, 3.2671713e-07],\n",
       "       [9.9999869e-01, 9.3904083e-07, 3.3056082e-07],\n",
       "       [9.9999869e-01, 9.5719656e-07, 3.3564507e-07],\n",
       "       [9.9999869e-01, 9.8184250e-07, 3.4249359e-07],\n",
       "       [9.9999857e-01, 1.0161356e-06, 3.5190965e-07],\n",
       "       [9.9999857e-01, 1.0652910e-06, 3.6517895e-07],\n",
       "       [9.9999845e-01, 1.1382630e-06, 3.8442050e-07],\n",
       "       [9.9999845e-01, 1.2511870e-06, 4.1326672e-07],\n",
       "       [9.9999809e-01, 1.4348878e-06, 4.5821722e-07],\n",
       "       [9.9999774e-01, 1.7526168e-06, 5.3150762e-07],\n",
       "       [9.9999690e-01, 2.3459586e-06, 6.5750658e-07],\n",
       "       [9.9999559e-01, 3.5661972e-06, 8.8795576e-07],\n",
       "       [9.9999225e-01, 6.3863740e-06, 1.3398600e-06],\n",
       "       [9.9998403e-01, 1.3753273e-05, 2.2883332e-06],\n",
       "       [9.9996078e-01, 3.4847824e-05, 4.3733544e-06],\n",
       "       [9.9989367e-01, 9.7248900e-05, 9.0562426e-06],\n",
       "       [9.9969959e-01, 2.8023019e-04, 2.0242564e-05],\n",
       "       [9.9916852e-01, 7.7582826e-04, 5.5680819e-05]], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30, 59, 41, 59, 59, 41, 82, 30, 59, 59])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_char_te[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def charindx2word(Tword):\n",
    "    word = []\n",
    "    for i in range(len(Tword)):\n",
    "        if(Tword[i] !=0):\n",
    "            word.append(idx2char[Tword[i]])\n",
    "    return \"\".join(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word           ||True ||Pred\n",
      "==============================\n",
      "Üldseisnud     : text  text\n",
      "rahuldav.S     : text  text\n",
      "regulaarne     : object text\n",
      "mm             : text  text\n",
      "Hg.Vesikul     : text  text\n",
      "hingamiska     : text  text\n",
      "pehme,valu     : text  text\n"
     ]
    }
   ],
   "source": [
    "i = 1000\n",
    "p = np.argmax(y_pred[i], axis=-1)\n",
    "print(\"{:15}||{:5}||{}\".format(\"Word\", \"True\", \"Pred\"))\n",
    "print(30 * \"=\")\n",
    "for w, t, pred in zip(X_char_te[i], y_te[i], p):\n",
    "    if t != 0: #PAD\n",
    "        print(\"{:15}: {:5} {}\".format(charindx2word(w), idx2tag[t], idx2tag[pred]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2729"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wrong Tags\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word           ||True ||Pred\n",
      "==============================\n",
      "puhtad.RR1     : object text\n",
      "langeb         : text  object\n",
      "ebaregulaa     : object text\n",
      "Pikkus         : object text\n",
      "157            : object text\n",
      "Pikkus         : object text\n",
      "165            : object text\n",
      "170/110        : object text\n",
      "p/s)RR-130     : object text\n",
      "piim)RR-15     : object text\n",
      "KORRAS.        : object text\n",
      "KOORMUSTES     : object text\n",
      "väga           : text  object\n",
      "155/95         : object text\n",
      "Pikkus-174     : object text\n",
      "holter         : text  object\n",
      "Nõustamine     : object text\n",
      "suhtes.RR1     : object text\n",
      "=              : text  object\n",
      "105/60         : object text\n",
      "2.3.RR         : object text\n",
      "114/           : object text\n",
      "kõikumised     : object text\n",
      "140-90mmhg     : object text\n",
      "130/82         : object text\n",
      "130/95         : object text\n",
      "sööki.RR-1     : object text\n",
      "sööki-6,6m     : object text\n",
      "regulaarne     : object text\n",
      "Pikkus-        : object text\n",
      "178            : object text\n",
      "langeb         : text  object\n",
      "kaebusteta     : object text\n",
      "Holter         : text  object\n",
      "Holter         : text  object\n",
      "Holter         : text  object\n",
      "maks           : text  object\n",
      "min.RR         : object text\n",
      "130/86         : object text\n",
      "regulaarne     : object text\n",
      "Pikkus:        : object text\n",
      "162,7cm        : object text\n",
      "Pikkus:        : object text\n",
      "162,7cm        : object text\n",
      "tarvitanud     : object text\n",
      "135/82         : object text\n",
      "min.RR         : object text\n",
      "135/70         : object text\n",
      "monitoorin     : text  object\n",
      "mmol/l.RR-     : object text\n",
      "monitoorin     : text  object\n",
      "regul,RR       : object text\n",
      "140/80         : object text\n",
      "Pikkus         : object text\n",
      "164            : object text\n",
      "135/92//85     : text  object\n",
      "ECG____RR      : object text\n",
      "145/70         : object text\n",
      "Pikkus         : object text\n",
      "162            : object text\n",
      "kahin.RR       : object text\n",
      "112/81         : object text\n",
      "ole.RR         : object text\n",
      "120/80.Rö      : object text\n",
      "Kirjeldus:     : text  object\n",
      "Pikkus         : object text\n",
      "184cm          : object text\n",
      "Pikkus         : object text\n",
      "184cm          : object text\n",
      "frekv.RR-1     : object text\n",
      "110,9--109     : text  object\n",
      "Holter         : text  object\n",
      "maks           : text  object\n",
      "ravi           : text  object\n",
      "köha.RR        : object text\n",
      "130,           : object text\n",
      "min.Pikkus     : object text\n",
      "189            : object text\n",
      "min.Pikkus     : object text\n",
      "189            : object text\n",
      "Kordusrets     : object text\n",
      "150/90         : object text\n",
      "Pikkus         : object text\n",
      "46cm,          : object text\n",
      "esita.RR       : object text\n",
      "125/70.        : object text\n",
      "Analüüsid.     : object text\n",
      "184/88         : object text\n",
      "Analüüsid.     : object text\n",
      "184/88         : object text\n",
      "regulaarse     : object text\n",
      "Pikkus         : object text\n",
      "175            : object text\n",
      "Pikkus         : object text\n",
      "175            : object text\n",
      "x1min,RR       : object text\n",
      "125            : object text\n",
      "x1min,RR       : object text\n",
      "125            : object text\n",
      "Pikkus         : object text\n",
      "170            : object text\n",
      "Pikkus         : object text\n",
      "170            : object text\n",
      "on             : text  object\n",
      "Pikkus         : object text\n",
      "165            : object text\n",
      "Pikkus         : object text\n",
      "165            : object text\n",
      "170            : object text\n",
      "Ogj.RR         : object text\n",
      "130/90         : object text\n",
      "retsepti.R     : object text\n",
      "156/93         : object text\n",
      "Pikkus         : object text\n",
      "154            : object text\n",
      "ja             : text  object\n",
      "Pikkus         : object text\n",
      "157cm.         : object text\n",
      "Pikkus         : object text\n",
      "157cm.         : object text\n",
      "on             : text  object\n",
      "monitoorin     : text  object\n",
      "Pikkus-        : object text\n",
      "150            : object text\n",
      "ravi           : text  object\n",
      "retsepti.R     : object text\n",
      "160\\90         : object text\n",
      "sööki-4,3m     : object text\n",
      "rr140/100      : object text\n",
      "kordusvisi     : text  object\n",
      "Pikkus         : object text\n",
      "183,           : object text\n",
      "monitoorin     : text  object\n",
      "tuhmid,kah     : object text\n",
      "130/82         : object text\n",
      "kõrgem         : text  object\n",
      "RR-monitoo     : object text\n",
      "väärtusi.      : text  object\n",
      "min,RR         : object text\n",
      "140            : object text\n",
      "114/73         : object text\n",
      "114/73         : object text\n",
      "114/73         : object text\n",
      "regulaarne     : object text\n",
      "98%,fr.93x     : object text\n",
      "140/90         : object text\n",
      "Pikkus-        : object text\n",
      "159            : object text\n",
      "Holter         : text  object\n",
      "maks.          : text  object\n",
      "sööki8-7,8     : text  object\n",
      "Pikkus-165     : object text\n",
      "Pikkus-165     : object text\n",
      "min,RR         : object text\n",
      "120/80         : object text\n",
      "Pikkus         : object text\n",
      "175            : object text\n",
      "Pikkus         : object text\n",
      "175            : object text\n",
      "kõrge          : text  object\n",
      "kõrge          : text  object\n",
      "86x´,regul     : object text\n",
      "130/90         : object text\n",
      "Tervisekon     : object text\n",
      "Holter         : text  object\n",
      "maks           : text  object\n",
      "arvi           : text  object\n",
      "aja.RR         : object text\n",
      "134/92         : object text\n",
      "halvasti.R     : object text\n",
      "sööki.RR-1     : object text\n",
      "min.Pikkus     : object text\n",
      "162            : object text\n",
      "min.Pikkus     : object text\n",
      "162            : object text\n",
      "õhupuudus,     : object text\n",
      "118/88         : object text\n",
      "sääki-14,0     : object text\n",
      "130/80mm/h     : text  object\n",
      "monitoorin     : text  object\n",
      "52Pikkus       : object text\n",
      "166            : object text\n",
      "52Pikkus       : object text\n",
      "166            : object text\n",
      "cor:akts.r     : object text\n",
      "120/80mmhg     : object text\n",
      "-              : text  object\n",
      "123/73,        : object text\n",
      "mõõtma.        : text  object\n",
      "mõõtma.        : text  object\n",
      "regulaarne     : object text\n",
      "holteri        : text  object\n",
      "rr150/90       : object text\n",
      "Pikkus         : object text\n",
      "160            : object text\n",
      "Pikkus         : object text\n",
      "160            : object text\n",
      "Pikkus         : object text\n",
      "161cm.         : object text\n",
      "Pikkus         : object text\n",
      "161cm.         : object text\n",
      "Pikkus         : object text\n",
      "174            : object text\n",
      "Pikkus         : object text\n",
      "174            : object text\n",
      "Pikkus         : object text\n",
      "168            : object text\n",
      "Pikkus         : object text\n",
      "168            : object text\n",
      "regulaarne     : object text\n",
      "Pikkus-192     : object text\n",
      "Pikkus-192     : object text\n",
      "ole.RR         : object text\n",
      "125/80.Rö      : object text\n",
      "südamepeks     : object text\n",
      "Pikkus-        : object text\n",
      "173            : object text\n",
      "Pikkus-        : object text\n",
      "162            : object text\n",
      "Pikkus-175     : object text\n",
      "kontrollil     : object text\n",
      "166/97         : object text\n",
      "Analüüsid.     : object text\n",
      "118/80         : object text\n",
      "Pikkus         : object text\n",
      "166            : object text\n",
      "Pikkus         : object text\n",
      "157,5          : object text\n",
      "Pikkus         : object text\n",
      "157,5          : object text\n",
      "Pikkus         : object text\n",
      "168            : object text\n",
      "Pikkus         : object text\n",
      "168            : object text\n",
      "140            : object text\n",
      "157/94         : object text\n",
      "Nõustamine     : object text\n",
      "120            : object text\n",
      "rr100/60       : object text\n",
      "ravimid        : text  object\n",
      "kontrollil     : text  object\n",
      "normi          : text  object\n",
      "mõõta          : text  object\n",
      "fr79*---RR     : object text\n",
      "134/67         : object text\n",
      "mõõta          : text  object\n",
      "134/67         : object text\n",
      "Pikkus         : object text\n",
      "170            : object text\n",
      "Pikkus         : object text\n",
      "170            : object text\n",
      "min.Pikkus     : object text\n",
      "170            : object text\n",
      "min.Pikkus     : object text\n",
      "170            : object text\n",
      "RR150/90.S     : object text\n",
      "regRR140-9     : object text\n",
      "võileib).R     : object text\n",
      "rahus-22/m     : text  object\n",
      "170/100        : object text\n",
      "Pikkus         : object text\n",
      "175            : object text\n",
      "*26.05.201     : text  object\n",
      "RR             : object PAD\n",
      "ees.RR133-     : object text\n",
      "140/94--&g     : object text\n",
      "140/90         : object text\n",
      "kompenseer     : object text\n",
      "140/70         : object text\n",
      "rohtud         : text  object\n",
      "de             : text  object\n",
      "Pikkus         : object text\n",
      "177,5,         : object text\n",
      "retsepti       : text  object\n",
      "leia,RR        : object text\n",
      "130/80         : object text\n",
      "s=             : text  object\n",
      "sööki-5,8/     : text  object\n",
      "110/80.Bor     : object text\n",
      "pearinglus     : object text\n",
      "monitoorin     : text  object\n",
      "retsepti.R     : object text\n",
      "148/93         : object text\n",
      "Obj:RR         : object text\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150/90         : object text\n",
      "140/70         : object text\n",
      "ravim          : text  object\n",
      "Pikkus         : object text\n",
      "49cm.          : object text\n",
      "monitoorin     : text  object\n",
      "170cm          : object text\n",
      "Pikkus-        : object text\n",
      "174            : object text\n",
      "min.RR         : object text\n",
      "140/80         : object text\n",
      "155.95         : object text\n",
      "tervisetõe     : object text\n",
      "kaal           : text  object\n",
      "Pikkus-        : object text\n",
      "175            : object text\n",
      "kontroll-      : text  object\n",
      "Pikkus-165     : object text\n",
      "2.3.RR         : object text\n",
      "114/           : object text\n",
      "13-101035-     : object text\n",
      "13-101035-     : object text\n",
      "13-101035-     : object text\n",
      "13-101035-     : object text\n",
      "13-101035-     : object text\n",
      "13-101035-     : object text\n",
      "13-101035-     : object text\n",
      "13-101035-     : object text\n",
      "Pikkus         : object text\n",
      "160            : object text\n",
      "RR-le          : object text\n",
      "sööki-6,3m     : object text\n",
      "Pikkus-        : object text\n",
      "163            : object text\n",
      "Holter         : text  object\n",
      "maks.          : text  object\n",
      "Rvai           : text  object\n",
      "156/86         : object text\n",
      "156/86         : object text\n",
      "156/86         : object text\n",
      "114/73,        : object text\n",
      "Pikkus-        : object text\n",
      "180            : object text\n",
      "Amb.m/õ,te     : object text\n",
      "id=\"0\"         : object text\n",
      "140            : object text\n",
      "Hba1c-7,0%     : object text\n",
      "-              : text  object\n",
      "<ANONYM        : text  object\n",
      "maks           : text  object\n",
      "120/70         : object text\n",
      "kg.Pikkus      : object text\n",
      "163            : object text\n",
      "120/70         : object text\n",
      "kg.Pikkus      : object text\n",
      "163            : object text\n",
      "mmHg.fr.88     : text  object\n",
      "120/70         : object text\n",
      "kg.Pikkus      : object text\n",
      "163            : object text\n",
      "Pingepeava     : object text\n",
      "118/75         : object text\n",
      "Pikkus:        : object text\n",
      "161            : object text\n",
      "Pikkus:        : object text\n",
      "161            : object text\n",
      "RRmonitoor     : object text\n",
      "110            : object text\n",
      "110            : object text\n",
      "110            : object text\n",
      "ebaregulaa     : object text\n",
      "regulaarne     : object text\n",
      "Holter         : text  object\n",
      "max171/94      : text  object\n",
      "arviei         : text  object\n",
      "kuni           : text  object\n",
      "150-160/90     : text  object\n",
      "Pikkus         : object text\n",
      "157            : object text\n",
      "Pikkus         : object text\n",
      "157            : object text\n",
      "03.06.2010     : object text\n",
      "127            : object text\n",
      "uimnasust.     : object text\n",
      "140/85.Cor     : object text\n",
      "120x1          : object text\n",
      "120x1          : object text\n",
      "rütm.,RR=      : object text\n",
      "120/80         : object text\n",
      "rr140/90       : object text\n",
      "RR128-82.      : object text\n",
      "Rr130/80       : text  object\n",
      "Nõustamine     : object text\n",
      "-              : text  object\n",
      "114/74         : object text\n",
      "RR90/60        : object text\n",
      "monitoorin     : text  object\n",
      "monitoorin     : text  object\n",
      "=              : text  object\n",
      "120/80         : object text\n",
      "rahuldav.R     : object text\n",
      "reg.puhtad     : object text\n",
      "Pikkus         : object text\n",
      "166cm.V/s      : object text\n",
      "Pikkus         : object text\n",
      "166cm.V/s      : object text\n",
      "Turseteta.     : object text\n",
      "statiinrav     : object text\n",
      "RR95/65        : object text\n",
      "holteril       : text  object\n",
      "normotooni     : text  object\n",
      "min-.RR        : object text\n",
      "120/80         : object text\n",
      "kanged.RR      : object text\n",
      "110/72         : object text\n",
      "retsepti.R     : object text\n",
      "122/77         : object text\n",
      "transparen     : object text\n",
      "125.80         : object text\n",
      "120kg          : object text\n",
      "RR-monitoo     : object text\n",
      "alanes         : text  object\n",
      "135/93         : object text\n",
      "Fa.RR          : object text\n",
      "130/90         : object text\n",
      "raviks         : text  object\n",
      "-              : text  object\n",
      "154/72         : object text\n",
      "Pikkus         : object text\n",
      "170            : object text\n",
      "Pikkus         : object text\n",
      "170            : object text\n",
      "*10.06.201     : object text\n",
      "Pikkus         : object text\n",
      "170            : object text\n",
      "Pikkus         : object text\n",
      "170            : object text\n",
      "Pikkus-        : object text\n",
      "164            : object text\n",
      "RR-monitoo     : object text\n",
      "väärtusi.      : text  object\n",
      "Pikkus         : object text\n",
      "172            : object text\n",
      "rägivad.Tu     : object text\n",
      "küll           : text  object\n",
      "x1mi.RR        : object text\n",
      "130/           : object text\n",
      "puhtad.RR      : object text\n",
      "150/90         : object text\n",
      "150/90         : object text\n",
      "150/90         : object text\n",
      "min.RR         : object text\n",
      "115/70mmHg     : object text\n",
      "Pikkus-        : object text\n",
      "163            : object text\n",
      "Pikkus         : object text\n",
      "156            : object text\n",
      "Pikkus         : object text\n",
      "156            : object text\n",
      "RR130/80_      : object text\n",
      "retsepti.R     : object text\n",
      "144/95         : object text\n",
      "=              : text  object\n",
      "100/50         : object text\n",
      "Ülekaaluli     : object text\n",
      "135/80         : object text\n",
      "VEK,RR         : object text\n",
      "162/103        : object text\n",
      "Pikkus-159     : object text\n",
      "Pikkus-159     : object text\n",
      "128/90         : object text\n",
      "120/75         : object text\n",
      "ujulast.Hb     : object text\n",
      "26.10.2015     : object text\n",
      "160/80         : object text\n",
      "05.11.2015     : object text\n",
      "26.10.2015     : object text\n",
      "160/80         : object text\n",
      "05.11.2015     : object text\n",
      "RR97/68mmH     : object text\n",
      "rahuldav,R     : object text\n",
      "148/68         : object text\n",
      "retsepti.R     : object text\n",
      "160\\80         : object text\n",
      "meeleolula     : object text\n",
      "100/70,        : object text\n",
      "Hba1c-5,7%     : object text\n",
      "söönuna--1     : object text\n",
      "Holter         : text  object\n",
      "max            : text  object\n",
      "ravi           : text  object\n",
      "üldseisund     : object text\n",
      "120/70.        : object text\n",
      "120x           : object text\n",
      "p/s-12,5kg     : object text\n",
      "120,1          : object text\n",
      "*12.03.201     : object text\n",
      "kahin.RR-1     : object text\n",
      "Pikkus         : object text\n",
      "166cm,         : object text\n",
      "rr140/90       : object text\n",
      "Holter         : text  object\n",
      "maks           : text  object\n",
      "madal          : text  object\n",
      "monitoorin     : text  object\n",
      "145;           : object text\n",
      "regulaarne     : object text\n",
      "120,2kg        : object text\n",
      "l/min.RR       : object text\n",
      "130/80         : object text\n",
      "Pikkus         : object text\n",
      "170            : object text\n",
      "RR119/84.      : object text\n",
      "/              : text  object\n"
     ]
    }
   ],
   "source": [
    "j = 0\n",
    "print(\"{:15}||{:5}||{}\".format(\"Word\", \"True\", \"Pred\"))\n",
    "print(30 * \"=\")\n",
    "\n",
    "for i in range(len(y_pred)):\n",
    "    p = np.argmax(y_pred[i], axis=-1)\n",
    "    for w, t, pred in zip(X_char_te[i], y_te[i], p):\n",
    "        if charindx2word(w) != \"\": #PAD\n",
    "            if idx2tag[t] != idx2tag[pred]:\n",
    "                print(\"{:15}: {:5} {}\".format(charindx2word(w), idx2tag[t], idx2tag[pred]))\n",
    "                j = j+1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "498"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word           ||True ||Pred\n",
      "==============================\n",
      "01.11.2011     : text  text\n",
      "-              : text  text\n",
      "118            : text  text\n",
      "kg,164         : text  text\n",
      "cm,BMI         : text  text\n",
      "43,RR          : object object\n",
      "150/90         : object object\n",
      "mmHg,FR        : text  text\n",
      "~125           : text  text\n",
      "x´,ebaregu     : text  text\n",
      ".              : text  text\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "p = np.argmax(y_pred[i], axis=-1)\n",
    "print(\"{:15}||{:5}||{}\".format(\"Word\", \"True\", \"Pred\"))\n",
    "print(30 * \"=\")\n",
    "for w, t, pred in zip(X_char_te[i], y_te[i], p):\n",
    "    if t != 0: #PAD\n",
    "        print(\"{:15}: {:5} {}\".format(charindx2word(w), idx2tag[t], idx2tag[pred]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score: 89.5%\n",
      "           precision    recall  f1-score   support\n",
      "\n",
      "     text       0.92      0.88      0.90      4461\n",
      "   object       0.95      0.83      0.88      2104\n",
      "\n",
      "micro avg       0.93      0.86      0.89      6565\n",
      "macro avg       0.93      0.86      0.89      6565\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from seqeval.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
    "\n",
    "def pred2label(pred):\n",
    "    out = []\n",
    "    for pred_i in pred:\n",
    "        out_i = []\n",
    "        for p in pred_i:\n",
    "            p_i = np.argmax(p)\n",
    "            out_i.append(idx2tag[p_i].replace(\"PAD\", \"text\"))\n",
    "        out.append(out_i)\n",
    "    return out\n",
    "\n",
    "def test2label(pred):\n",
    "    out = []\n",
    "    for pred_i in pred:\n",
    "        out_i = []\n",
    "        for p in pred_i:\n",
    "            out_i.append(idx2tag[p].replace(\"PAD\", \"text\"))\n",
    "        out.append(out_i)\n",
    "    return out\n",
    "\n",
    "pred_labels = pred2label(y_pred)\n",
    "test_labels = test2label(y_te)\n",
    "\n",
    "print(\"F1-score: {:.1%}\".format(f1_score(test_labels, pred_labels)))\n",
    "print(classification_report(test_labels, pred_labels))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13643"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9987834371564676"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(test_labels, pred_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      object       0.97      0.89      0.93      3653\n",
      "        text       1.00      1.00      1.00    405697\n",
      "\n",
      "    accuracy                           1.00    409350\n",
      "   macro avg       0.99      0.94      0.96    409350\n",
      "weighted avg       1.00      1.00      1.00    409350\n",
      "\n",
      "Accuracy 0.9987834371564676\n"
     ]
    }
   ],
   "source": [
    "from sklearn_crfsuite.metrics import flat_classification_report,flat_accuracy_score\n",
    "report = flat_classification_report(y_pred=pred_labels, y_true=test_labels)\n",
    "print(report)\n",
    "print(\"Accuracy\",flat_accuracy_score(y_pred=pred_labels, y_true=test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(sentences)"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}