{
 "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[\"hour\"] = X[\"intime\"].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 7,
   "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=(1,2), max_features=500)),\n",
    "        (\"tf-idf\", TfidfTransformer())\n",
    "    ]), \"chiefcomplaint\"),\n",
    "])\n",
    "\n",
    "full_preprocessing = Pipeline([\n",
    "    (\"features\", features_preprocessing),\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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocesser = full_preprocessing.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'torch' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_16440/4052523635.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX_train_preprocess\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreprocesser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0my_train_preprocess\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mX_test_preprocess\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreprocesser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0my_test_preprocess\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'torch' is not defined"
     ]
    }
   ],
   "source": [
    "X_train_preprocess = torch.tensor(preprocesser.transform(X_train), dtype=torch.float32)\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",
    "y_test_preprocess = torch.tensor(y_test.iloc[:,1:].values, dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn, optim\n",
    "import torch"
   ]
  },
  {
   "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, 500),\n",
    "            nn.BatchNorm1d(500),\n",
    "            nn.LeakyReLU(0.1),\n",
    "            nn.Dropout(0.2),\n",
    "            nn.Linear(500, 200),\n",
    "            nn.BatchNorm1d(200),\n",
    "            nn.LeakyReLU(0.1),\n",
    "            nn.Dropout(0.2),\n",
    "            nn.Linear(200, 200),\n",
    "            nn.BatchNorm1d(200),\n",
    "            nn.LeakyReLU(0.1),\n",
    "            nn.Linear(200, n_outputs),\n",
    "            nn.Sigmoid()\n",
    "        ])\n",
    "\n",
    "        self.loss = nn.CrossEntropyLoss()\n",
    "        self.optimizer = optim.Adam(self.parameters(), lr=5e-4)\n",
    "\n",
    "    def forward(self, x):\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.predict(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])"
   ]
  },
  {
   "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=4096)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 - loss : 8.175965949742482\n",
      "Epoch 1 - loss : 7.978885087099942\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_22104/2367511407.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[0my_tensor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_train_preprocess\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m         \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnetwork\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_tensor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m         \u001b[0mlosses\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_22104/223089245.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y)\u001b[0m\n\u001b[0;32m     37\u001b[0m         \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_hat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m         \u001b[0mloss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\torch\\_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    305\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m                 inputs=inputs)\n\u001b[1;32m--> 307\u001b[1;33m         \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    308\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    309\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\torch\\autograd\\__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    152\u001b[0m         \u001b[0mretain_graph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 154\u001b[1;33m     Variable._execution_engine.run_backward(\n\u001b[0m\u001b[0;32m    155\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    156\u001b[0m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "n_epochs = 100\n",
    "n_epoch_print = 1\n",
    "\n",
    "for i in range(n_epochs):\n",
    "\n",
    "    losses = []\n",
    "\n",
    "    j = 0\n",
    "    for indices in data_loader:\n",
    "        X_tensor = torch.tensor(X_train_preprocess[indices,:], dtype=torch.float32)\n",
    "        y_tensor = torch.tensor(y_train_preprocess[indices,:], dtype=torch.float32)\n",
    "\n",
    "        loss = network.fit(X_tensor, y_tensor).detach().cpu().item()\n",
    "        losses.append(loss)\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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, f1_score, recall_score, precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = network.predict(X_test)\n",
    "y_true = y_test.iloc[:,1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x2160 with 29 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titles = y_train.columns.tolist()[1:]\n",
    "\n",
    "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": 463,
   "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>Albumine</th>\n",
       "      <td>0.54</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BU</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPK</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRP</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calcemie</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cardiaque</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coagulation</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Dimeres</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gazometrie</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycemie_Sanguine</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hepato-Biliaire</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IonoC</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lipase</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFS</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   precision  recall  f1_score\n",
       "Albumine                0.54    0.33      0.41\n",
       "BU                      0.53    0.35      0.42\n",
       "CPK                     0.40    0.01      0.03\n",
       "CRP                     0.38    0.02      0.05\n",
       "Calcemie                0.43    0.14      0.21\n",
       "Cardiaque               0.67    0.40      0.50\n",
       "Coagulation             0.62    0.48      0.54\n",
       "D-Dimeres               0.00    0.00      0.00\n",
       "Gazometrie              0.71    0.70      0.70\n",
       "Glycemie_Sanguine       0.71    0.70      0.71\n",
       "Hepato-Biliaire         0.55    0.35      0.43\n",
       "IonoC                   0.71    0.70      0.71\n",
       "Lipase                  0.53    0.28      0.37\n",
       "NFS                     0.71    0.70      0.70"
      ]
     },
     "execution_count": 463,
     "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)"
   ]
  }
 ],
 "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}