workflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNS Drug Development"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.data.dataset import CNSDataset\n",
    "from src.descriptors import DescriptorGenerator, AVAILABLE_DESCRIPTORS\n",
    "\n",
    "from torch.utils.data import DataLoader, random_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_DATASET = \"dataset\\mol_train.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEST_DATASET = \"dataset\\mol_test.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, validation = random_split(\n",
    "    CNSDataset(TRAIN_DATASET, transform=DescriptorGenerator(AVAILABLE_DESCRIPTORS)),\n",
    "    [0.7, 0.3],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.models import LogisticRegressionModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = len(train)\n",
    "train_loader = DataLoader(train, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = next(iter(train_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([490, 8])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegressionModel(max_iter=1000)\n",
    "model.fit(train_dataset[0].numpy(), train_dataset[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import Tensor\n",
    "from torcheval.metrics.functional import binary_f1_score\n",
    "from sklearn.metrics import fbeta_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = len(validation)\n",
    "validation_loader = DataLoader(validation, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_dataset = next(iter(validation_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = model.predict(validation_dataset[0].numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F1 score: 0.7397260665893555'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"F1 score: {binary_f1_score(Tensor(result), validation_dataset[1])}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F2 score: 0.733695652173913'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"F2 score: {fbeta_score(result, validation_dataset[1], beta=2)}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.models import AVAILABLE_MODELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'src.models.skmodels.LogisticRegressionModel'>\n",
      "F2 score: 0.733695652173913\n",
      "<class 'src.models.skmodels.LinearRegressionModel'>\n",
      "F2 score: 0.734375\n",
      "<class 'src.models.skmodels.RidgeRegressionModel'>\n",
      "F2 score: 0.734375\n",
      "<class 'src.models.skmodels.LassoRegressionModel'>\n",
      "F2 score: 0.625\n",
      "<class 'src.models.skmodels.ElasticNetRegressionModel'>\n",
      "F2 score: 0.6009615384615385\n",
      "<class 'src.models.skmodels.BayesianRidgeRegressionModel'>\n",
      "F2 score: 0.7305194805194806\n",
      "<class 'src.models.skmodels.SGDClassifierModel'>\n",
      "F2 score: 0.4587765957446809\n",
      "<class 'src.models.skmodels.KernelRidgeModel'>\n",
      "F2 score: 0.6041666666666666\n",
      "<class 'src.models.skmodels.SVCModel'>\n",
      "F2 score: 0.6382978723404255\n",
      "<class 'src.models.skmodels.KNNModel'>\n",
      "F2 score: 0.59375\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Gardevoir\\miniconda3\\envs\\jaxff\\Lib\\site-packages\\sklearn\\linear_model\\_ridge.py:248: LinAlgWarning: Ill-conditioned matrix (rcond=4.91217e-11): result may not be accurate.\n",
      "  dual_coef = linalg.solve(K, y, assume_a=\"pos\", overwrite_a=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'src.models.skmodels.KmeansModel'>\n",
      "F2 score: 0.04807692307692308\n",
      "<class 'src.models.skmodels.GaussianmixtureModel'>\n",
      "F2 score: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Gardevoir\\miniconda3\\envs\\jaxff\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Gardevoir\\miniconda3\\envs\\jaxff\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "for available_model in AVAILABLE_MODELS:\n",
    "    model = available_model()\n",
    "    model.fit(train_dataset[0].numpy(), train_dataset[1])\n",
    "    result = model.predict(validation_dataset[0].numpy())\n",
    "    print(available_model)\n",
    "    result[result > 0.5] = 1\n",
    "    result[result <= 0.5] = 0\n",
    "    print(f\"F2 score: {fbeta_score(result, validation_dataset[1], beta=2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = CNSDataset(TEST_DATASET, transform=DescriptorGenerator(AVAILABLE_DESCRIPTORS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = len(test)\n",
    "test_loader = DataLoader(test, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = next(iter(test_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = model.predict(dataset[0].numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1,\n",
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       "       1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv(TEST_DATASET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['TARGET'] = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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