{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_parquet(\"./data/features_atc4.parquet\")\n",
    "y = pd.read_parquet(\"./data/labels.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer, MissingIndicator\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OrdinalEncoder, StandardScaler\n",
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[\"hour\"] = X[\"intime\"].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = [\n",
    "    \"gender\",\n",
    "    \"hour\"\n",
    "]+X_train.columns[12:].tolist()\n",
    "\n",
    "continuous_features = [\n",
    "    \"pain\",\n",
    "    \"age\",\n",
    "    \"temperature\",\n",
    "    \"heartrate\",\n",
    "    \"resprate\",\n",
    "    \"o2sat\",\n",
    "    \"sbp\",\n",
    "    \"dbp\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_preprocessing = ColumnTransformer([\n",
    "    (\"binary_encoder\", OrdinalEncoder(), categorical_features),\n",
    "    (\"identity\", StandardScaler(), continuous_features),\n",
    "    (\"missing\", MissingIndicator(), continuous_features),\n",
    "    (\"nlp\", Pipeline([\n",
    "        (\"cv\", CountVectorizer(ngram_range=(1,1), max_features=200)),\n",
    "        (\"tf-idf\", TfidfTransformer())\n",
    "    ]), \"chiefcomplaint\"),\n",
    "])\n",
    "\n",
    "features_preprocessing_without_nlp = ColumnTransformer([\n",
    "    (\"binary_encoder\", OrdinalEncoder(), categorical_features),\n",
    "    (\"identity\", StandardScaler(), continuous_features),\n",
    "    (\"missing\", MissingIndicator(), continuous_features)\n",
    "])\n",
    "\n",
    "full_preprocessing = Pipeline([\n",
    "    (\"features\", features_preprocessing_without_nlp),\n",
    "    (\"imputer\", SimpleImputer())\n",
    "])\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    (\"preprocessing\", full_preprocessing),\n",
    "    (\"mlp\", MLPClassifier(hidden_layer_sizes=(100,20), verbose=True, learning_rate_init=1e-3, batch_size=64, max_iter=100))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocesser = full_preprocessing.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import BertTokenizer, BertModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "biobert_tokenizer = BertTokenizer.from_pretrained(\"dmis-lab/biobert-v1.1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_preprocess = torch.tensor(preprocesser.transform(X_train), dtype=torch.float32)\n",
    "X_train_tokens = biobert_tokenizer(X_train[\"chiefcomplaint\"].tolist())[\"input_ids\"]\n",
    "y_train_preprocess = torch.tensor(y_train.iloc[:,1:].values, dtype=torch.float32)\n",
    "X_test_preprocess = torch.tensor(preprocesser.transform(X_test), dtype=torch.float32)\n",
    "X_test_tokens = biobert_tokenizer(X_test[\"chiefcomplaint\"].tolist())[\"input_ids\"]\n",
    "y_test_preprocess = torch.tensor(y_test.iloc[:,1:].values, dtype=torch.float32)\n",
    "X_train_tokens = [torch.tensor(x) for x in X_train_tokens]\n",
    "X_test_tokens = [torch.tensor(x) for x in X_test_tokens]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn, optim\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence\n",
    "import torch\n",
    "import operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class neural_net (nn.Module):\n",
    "    def __init__(self, n_features, n_outputs):\n",
    "        super().__init__()\n",
    "\n",
    "        self.network = nn.Sequential(*[\n",
    "            nn.Linear(n_features+768, 500),\n",
    "            nn.BatchNorm1d(500),\n",
    "            nn.LeakyReLU(0.1),\n",
    "            nn.Linear(500, 200),\n",
    "            nn.BatchNorm1d(200),\n",
    "            nn.LeakyReLU(0.1),\n",
    "            nn.Linear(200, n_outputs),\n",
    "            nn.Sigmoid()\n",
    "        ])\n",
    "\n",
    "        self.biobert_model = BertModel.from_pretrained(\"dmis-lab/biobert-v1.1\").to(\"cuda:0\")\n",
    "        for x in self.biobert_model.parameters():\n",
    "            x.requires_grad = False\n",
    "            \n",
    "        self.loss = nn.BCELoss()\n",
    "        self.optimizer = optim.Adam(self.parameters(), lr=5e-4)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        x_data = x[0]\n",
    "        x_tokens = x[1]\n",
    "\n",
    "        x_bert = torch.mean(self.biobert_model.embeddings.word_embeddings(x_tokens), axis=1)\n",
    "        x = torch.concat([x_data, x_bert], axis=1)\n",
    "\n",
    "        y_hat = self.network(x)\n",
    "\n",
    "        return y_hat\n",
    "    \n",
    "    def fit(self, x, y):\n",
    "        \n",
    "        self.train()\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        y_hat = self.forward(x)\n",
    "\n",
    "        loss = self.loss(y_hat, y)\n",
    "\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        return loss\n",
    "\n",
    "    def predict(self, x):\n",
    "        \n",
    "        self.eval()\n",
    "        \n",
    "        with torch.no_grad():            \n",
    "            y_hat = self.forward(x)\n",
    "\n",
    "        return y_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = neural_net(X_train_preprocess.shape[1], y_train_preprocess.shape[1])\n",
    "network = network.to(\"cuda:0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader = DataLoader(range(X_train_preprocess.shape[0]), shuffle=True, batch_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.35904401540756226\n",
      "0.3628128885042549\n",
      "0.3626623178892468\n",
      "0.36212184619269894\n",
      "Epoch 0 - loss : 0.36184037493754034\n",
      "0.3660319149494171\n",
      "0.35813640161315996\n",
      "0.35807146732486894\n",
      "0.3580603587667015\n",
      "Epoch 1 - loss : 0.3582924585553664\n",
      "0.3633681833744049\n",
      "0.3539693438180602\n",
      "0.3550527073554139\n",
      "0.35535963062828163\n",
      "Epoch 2 - loss : 0.3559726171855685\n",
      "0.3667027950286865\n",
      "0.35198344186981123\n",
      "0.35242729667407363\n",
      "0.3531943013105678\n",
      "Epoch 3 - loss : 0.35359182267249384\n",
      "0.3803612291812897\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 100\n",
    "n_epoch_print = 1\n",
    "n_batch_print = 50\n",
    "\n",
    "for i in range(n_epochs):\n",
    "\n",
    "    losses = []\n",
    "\n",
    "    j = 0\n",
    "    for indices in data_loader:\n",
    "        X_tensor = X_train_preprocess[indices,:].to(\"cuda:0\")\n",
    "        X_train_tokens_indices = list(operator.itemgetter(*indices)(X_train_tokens))\n",
    "        X_train_tokens_indices = pad_sequence(X_train_tokens_indices, batch_first=True, padding_value=biobert_tokenizer(\"[PAD]\")[\"input_ids\"][1]).to(\"cuda:0\")\n",
    "\n",
    "        y_tensor = y_train_preprocess[indices,:].to(\"cuda:0\")\n",
    "\n",
    "        loss = network.fit((X_tensor, X_train_tokens_indices), y_tensor).detach().cpu().item()\n",
    "        losses.append(loss)\n",
    "\n",
    "        if j%n_batch_print == 0:\n",
    "            print(np.array(losses).mean())\n",
    "        \n",
    "        j += 1\n",
    "\n",
    "    if (i%n_epoch_print) == 0:\n",
    "        mean_loss = np.array(losses).mean()\n",
    "        print(f\"Epoch {i} - loss : {mean_loss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, f1_score, recall_score, precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "\n",
    "y_hats = []\n",
    "\n",
    "for idx in range(0, X_test_preprocess.shape[0], batch_size):\n",
    "    X_test_tensor = X_test_preprocess[idx:idx+batch_size].to(\"cuda:0\")\n",
    "    X_test_tokens_indices = X_test_tokens[idx:idx+batch_size]\n",
    "    X_test_tokens_indices = pad_sequence(X_test_tokens_indices, batch_first=True, padding_value=biobert_tokenizer(\"[PAD]\")[\"input_ids\"][1]).to(\"cuda:0\")\n",
    "\n",
    "    y_hat_ = ((network.predict((X_test_tensor, X_test_tokens_indices)).detach().cpu()) >= 0.5)*1\n",
    "\n",
    "    y_hats.append(y_hat_)\n",
    "\n",
    "y_hat = torch.concat(y_hats, axis=0).numpy()\n",
    "y_true = y_test_preprocess\n",
    "titles = y_train.columns.tolist()[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GnJ1aa3cnubOqnts1nZDkliSXJjmtazstyae7/UuTnFJVC6rq4CQrk1zXLQO4saqOq6pKcmrfOcDUkJ0AfO80iQoqxmxrJhkAAJPJTgAzw68n+euq2iPJbUl+Jb3JuBdX1elJVic5OUlaazdX1cXpDWJtSvKW1trm7jpnpncvkIVJLus2YOrITgBMYoCKMdcnWdnNIrsrySlJ3jjaLgGMwCxdsxcYOtkJIBl5dmqtfT3J1pYBPGEbx5+d5OyttN+Q5Igp7RzQT3YCSEaenWYaS/yRJGmtbUrya0k+l+TbSS5urd082l7BaFXVRUmuSfLcqlrTzcAEANkJtkJ2AmBbZCeYTHYCFVT0aa19NslnR90PmClaa28YdR8YATNZgAHJTjCe7LSbkp2AAclOMJ7stJuSncZRQQUAAAAAAMBQqaACgCe0ZIuZLAAAg5GdAAAGJztNZIAKAMa0pLUto+4FAMDsIDsBAAxOdprEEn8AAAAAAAAMlQoqAOin1BoAYHCyEwDA4GSncVRQQaeqNlfV16vqW1X1N1W1149wrQuq6vXd/l9W1eHbOfb4qnrJU3iNO6pq2aDtE455eCdf6/er6rd3to8AwK5Ldtru8bITADCO7LTd42Un2E0ZoIInPdJaO6q1dkSSx5K8uf/Jqpr7VC7aWvvPrbVbtnPI8Ul2OigA06S16dsAdi2yEyA7AQxOdgJkpwkMUMHWfSnJod0sk3+sqo8luamq5lbVn1TV9VX1zar6L0lSPe+vqluq6h+S7Dd2oar6YlUd0+2fWFVfrapvVNVVVXVQeoHk7d0smpdX1TOq6m+717i+ql7anbtvVV1RVV+rqg8mqR39ElX1d1V1Y1XdXFVnTHjuvV1frqqqZ3Rtz66qy7tzvlRVh03JXxMA2NXJTrITADA42Ul2AuIeVDBJVc1L8tokl3dNxyY5orV2e/dh+1Br7aeqakGSr1TVFUlemOS5SZ6fZP8ktyT50ITrPiPJ/03yiu5aS1tr91fVXyR5uLX2nu64jyX5s9bal6vqx5J8LslPJHlXki+31v6wqn4uybgP/m341e41Fia5vqr+trV2X5JFSb7aWvutqvq97tq/luS8JG9urd1aVS9Kck6Sf/cU/owwO7WWbNky6l4AzCqyk+zEbkx2AthpspPsxG5MdprEABU8aWFVfb3b/1KS89Mrgb6utXZ71/7qJEdWt85vkiVJViZ5RZKLWmubk6ytqi9s5frHJbl67Fqttfu30Y+fSXJ41RMTVRZX1d7da/yH7tx/qKoHBvid3lpVv9jtH9j19b4kW5J8omv/aJJPVtXTut/3b/pee8EArwEA7J5kJ9kJABic7CQ7ARMYoIInPdJaO6q/ofvA/EF/U5Jfb619bsJxP5tkRwt91gDHJL2lN1/cWntkK30ZeDHRqjo+vdDx4tba/6uqLybZcxuHt+51H5z4N4DdzixdsxdgBGQn2QlkJ4DByU6yE8hOE7gHFeyczyU5s6rmJ0lVPaeqFiW5Oskp1Vsr+IAkr9zKudck+emqOrg7d2nXvjHJ3n3HXZFe2XO6447qdq9O8std22uT7LODvi5J8kAXEg5LbybNmDlJxmbjvDG9Eu7vJ7m9qk7uXqOq6gU7eA0AgO2RnQAABic7AbsVFVSwc/4yyUFJvlq9qSX3Jnldkk+lt2buTUn+Nck/TTyxtXZvt5bwJ6tqTpL1SV6V5O+TXFJVJyX59SRvTfKBqvpmeu/Rq9O7oeUfJLmoqr7aXX/1Dvp6eZI3d9f5bpJ/6XvuB0meV1U3JnkoyS917b+c5NyqemeS+Uk+nuQbA/1lYBfRrAUMMJVkJ9jFyU4AU0p2gl2c7DReNSVlAJAkWTJ3WTtu4c9N2/Wv+MGHb2ytHTNtLwAAMESyEwDA4GSnyVRQAcATmrWAAQAGJjsBAAxOdprIPagAAAAAAAAYKhVUADCmJdliJgsAwEBkJwCAwclOkxigAoB+zc0qAQAGJjsBAAxOdhrHEn8AAAAAAAAMlQoqAOi0JE2pNQDAQGQnAIDByU6TqaACAAAAAABgqFRQAcCY1qwFDAAwKNkJAGBwstMkKqgAAAAAAAAYKhVUANDHWsAAAIOTnQAABic7jaeCCgAAAAAAgKFSQQUA/awFDAAwONkJAGBwstM4KqgAAAAAAAAYqmrNmocAkCRVdXmSZdP4EhtaaydO4/UBAIZGdgIAGJzsNJkBKgAAAAAAAIbKEn8AAAAAAAAMlQEqAAAAAAAAhsoAFQAAAAAAAENlgAoAAAAAAIChMkAFAAAAAADAUP1//VG7jqqkFdwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2160x2160 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_cols = 3\n",
    "n_rows = len(titles) // n_cols + int((len(titles)%n_cols) != 0)\n",
    "\n",
    "figs, axs = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=(30,30))\n",
    "axs_flatten = axs.flatten()\n",
    "\n",
    "for i in range(len(titles)):\n",
    "    cm_plot = ConfusionMatrixDisplay(confusion_matrix(y_true[:,i], y_hat[:,i]))\n",
    "    cm_plot.plot(ax=axs_flatten[i])\n",
    "    axs_flatten[i].set_title(titles[i])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cardiaque</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coagulation</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gazometrie</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycemie_Sanguine</th>\n",
       "      <td>0.57</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hepato-Biliaire</th>\n",
       "      <td>0.58</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IonoC</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lipase</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFS</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phospho-Calcique</th>\n",
       "      <td>0.52</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   precision  recall  f1_score\n",
       "Cardiaque               0.61    0.50      0.55\n",
       "Coagulation             0.64    0.46      0.54\n",
       "Gazometrie              0.68    0.76      0.72\n",
       "Glycemie_Sanguine       0.57    0.18      0.27\n",
       "Hepato-Biliaire         0.58    0.32      0.42\n",
       "IonoC                   0.68    0.76      0.72\n",
       "Lipase                  0.55    0.26      0.35\n",
       "NFS                     0.68    0.75      0.72\n",
       "Phospho-Calcique        0.52    0.09      0.16"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(\n",
    "    dict(zip(titles, np.concatenate([\n",
    "        precision_score(y_true, y_hat, average=None, zero_division=0).reshape(1,-1),\n",
    "        recall_score(y_true, y_hat, average=None, zero_division=0).reshape(1,-1),\n",
    "        f1_score(y_true, y_hat, average=None).reshape(1,-1)\n",
    "    ], axis=0).T)),\n",
    "    orient=\"index\",\n",
    "    columns=[\"precision\",\"recall\",\"f1_score\"]\n",
    ").round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "28b293e0c0671e44c7281dde6399c7c7419d3faca031d22494da8635907ada72"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}