02-Model-Training-Experiment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8093fc9a",
   "metadata": {},
   "source": [
    "# Train Machine learning on non-public Medical data\n",
    "\n",
    "In this notebook we will learn how to train Machine learning models on remote non-public data, using **Scikit-learn** and **PySyft**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d6940ef-5525-4e70-bfb9-b42c06040e02",
   "metadata": {},
   "source": [
    "## Step 1. Login to datasites as **External Researcher**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f642b926-3485-4620-a7f7-9afe2cc2634b",
   "metadata": {},
   "source": [
    "Launch Datasites if not running:\n",
    "```bash\n",
    "$ python launch_datasites.py\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c366189-de89-4a76-9375-c4cffe8e6bdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import syft as sy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07d0657a-c5d7-46b6-a503-3709260116ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logged into <Cleveland Clinic: High-side Datasite> as GUEST\n",
      "Logged into <Hungarian Inst. of Cardiology: High-side Datasite> as GUEST\n",
      "Logged into <Univ. Hospitals Zurich and Basel: High-side Datasite> as GUEST\n",
      "Logged into <V.A. Medical Center: High-side Datasite> as GUEST\n"
     ]
    }
   ],
   "source": [
    "from datasites import DATASITE_URLS\n",
    "\n",
    "datasites = {}\n",
    "for name, url in DATASITE_URLS.items():\n",
    "    datasites[name] = sy.login_as_guest(url=url)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ced8378-283c-44cd-ba89-1074199e40b7",
   "metadata": {},
   "source": [
    "## Step 2. Get Mock data and test the model training code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac67e486-18db-4512-b503-d2272fc4a458",
   "metadata": {},
   "outputs": [],
   "source": [
    "mock_data = datasites[\"Cleveland Clinic\"].datasets[\"Heart Disease Dataset\"].assets[\"Heart Study Data\"].mock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d2366ad3-7ad7-4cfd-9f11-2061b814a482",
   "metadata": {},
   "outputs": [
    {
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       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
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       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
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       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
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       "\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
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       "\n",
       "/* Serial-specific style estimator block */\n",
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       "\n",
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       "\n",
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       "\n",
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       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
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       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
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       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
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       "\n",
       "/* Estimator-specific */\n",
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       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
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       "\n",
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       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
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       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=12345)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=12345)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=12345)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DS/ML libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "# Input data is not \"ready\" for ML experiments, so we need to \n",
    "# (1) extract features and labels; (2) train/test split data \n",
    "# before training our model.\n",
    "def by_demographics(data):\n",
    "    # NO age stratification as data is too skew \n",
    "    # (see notebook 01-Compare-Demographics.ipynb)\n",
    "    sex = data[\"sex\"].map(lambda v: '0' if v == 0 else '1')\n",
    "    target = data[\"num\"].map(lambda v: '0' if v == 0 else '1')\n",
    "    return (sex+target).values\n",
    "\n",
    "# 1. get features and labels\n",
    "X = mock_data.drop(columns=[\"age\", \"sex\", \"num\"], axis=1)\n",
    "y = mock_data[\"num\"].map(lambda v: 0 if v == 0 else 1)\n",
    "# 2. partition data\n",
    "X_train, _, y_train, _ = train_test_split(\n",
    "    X, y, random_state=12345, stratify=by_demographics(mock_data)\n",
    ")\n",
    "# 3. train model: Tree-based model as its invariant to feature scale, and allows data sparsity\n",
    "model = RandomForestClassifier(random_state=12345)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2325373-76af-4428-b5c1-c970abf790c0",
   "metadata": {},
   "source": [
    "## Step 3. Submit Experiment to each datasite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d36b59f-2236-4a07-bd3d-dd34cee62cae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasite: Cleveland Clinic\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"alert-success\" style=\"padding:5px;\"><strong>SyftSuccess</strong>: <pre class=\"alert-success\" style=\"display:inline; font-family:inherit;\">Syft function 'train' successfully created. To add a code request, please create a project using `project = syft.Project(...)`, then use command `project.create_code_request`.</pre></div><br/>"
      ],
      "text/plain": [
       "SyftSuccess: Syft function 'train' successfully created. To add a code request, please create a project using `project = syft.Project(...)`, then use command `project.create_code_request`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasite: Hungarian Inst. of Cardiology\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"alert-success\" style=\"padding:5px;\"><strong>SyftSuccess</strong>: <pre class=\"alert-success\" style=\"display:inline; font-family:inherit;\">Syft function 'train' successfully created. To add a code request, please create a project using `project = syft.Project(...)`, then use command `project.create_code_request`.</pre></div><br/>"
      ],
      "text/plain": [
       "SyftSuccess: Syft function 'train' successfully created. To add a code request, please create a project using `project = syft.Project(...)`, then use command `project.create_code_request`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasite: Univ. Hospitals Zurich and Basel\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"alert-success\" style=\"padding:5px;\"><strong>SyftSuccess</strong>: <pre class=\"alert-success\" style=\"display:inline; font-family:inherit;\">Syft function 'train' successfully created. To add a code request, please create a project using `project = syft.Project(...)`, then use command `project.create_code_request`.</pre></div><br/>"
      ],
      "text/plain": [
       "SyftSuccess: Syft function 'train' successfully created. To add a code request, please create a project using `project = syft.Project(...)`, then use command `project.create_code_request`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasite: V.A. Medical Center\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"alert-success\" style=\"padding:5px;\"><strong>SyftSuccess</strong>: <pre class=\"alert-success\" style=\"display:inline; font-family:inherit;\">Syft function 'train' successfully created. To add a code request, please create a project using `project = syft.Project(...)`, then use command `project.create_code_request`.</pre></div><br/>"
      ],
      "text/plain": [
       "SyftSuccess: Syft function 'train' successfully created. To add a code request, please create a project using `project = syft.Project(...)`, then use command `project.create_code_request`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, datasite in datasites.items():\n",
    "    print(f\"Datasite: {name}\")\n",
    "    # 1. Get data asset from datasite\n",
    "    data_asset = datasite.datasets[\"Heart Disease Dataset\"].assets[\"Heart Study Data\"]\n",
    "    \n",
    "    @sy.syft_function_single_use(data=data_asset)\n",
    "    def train(data):\n",
    "        # DS/ML libraries\n",
    "        from sklearn.model_selection import train_test_split\n",
    "        from sklearn.ensemble import RandomForestClassifier\n",
    "        # Extra dependencies for model persistance (see 4.)\n",
    "        import joblib\n",
    "        from io import BytesIO\n",
    "        \n",
    "        def by_demographics(data):\n",
    "            sex = data[\"sex\"].map(lambda v: '0' if v == 0 else '1')\n",
    "            target = data[\"num\"].map(lambda v: '0' if v == 0 else '1')\n",
    "            return (sex+target).values\n",
    "        \n",
    "        # 1. get features and labels\n",
    "        X = data.drop(columns=[\"age\", \"sex\", \"num\"], axis=1)\n",
    "        y = data[\"num\"].map(lambda v: 0 if v == 0 else 1)\n",
    "        # 2. partition data\n",
    "        X_train, _, y_train, _ = train_test_split(\n",
    "            X, y, random_state=12345, stratify=by_demographics(data)\n",
    "        )\n",
    "        # 3. train model\n",
    "        model = RandomForestClassifier(random_state=12345)\n",
    "        model.fit(X_train, y_train)\n",
    "        # 4. model persistance - return model serialised \n",
    "        serialised_model = BytesIO()\n",
    "        joblib.dump(model, serialised_model)\n",
    "\n",
    "        return serialised_model\n",
    "    \n",
    "    ml_training_project = sy.Project(\n",
    "        name=\"Traning RandomForest Classifier on Heart Study Data\",\n",
    "        description=\"\"\"I would like to train a classifier on the Heart Study data.\n",
    "        The code will partition the dataset using sex and target, and will train \n",
    "        a RandomForest classifier, that will be returned serialised.\n",
    "        \"\"\",\n",
    "        members=[datasite],\n",
    "    )\n",
    "    ml_training_project.create_code_request(train, datasite)\n",
    "    project = ml_training_project.send()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe0a4550-328c-4519-a1e3-e5fa0376f235",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasite: Cleveland Clinic\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"alert-success\" style=\"padding:5px;\"><strong>SyftSuccess</strong>: <pre class=\"alert-success\" style=\"display:inline; font-family:inherit;\"> approved</pre></div><br/>"
      ],
      "text/plain": [
       "SyftSuccess: <class 'syft.service.code.user_code.UserCodeStatusCollection'> approved"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasite: Hungarian Inst. of Cardiology\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"alert-success\" style=\"padding:5px;\"><strong>SyftSuccess</strong>: <pre class=\"alert-success\" style=\"display:inline; font-family:inherit;\"> approved</pre></div><br/>"
      ],
      "text/plain": [
       "SyftSuccess: <class 'syft.service.code.user_code.UserCodeStatusCollection'> approved"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasite: Univ. Hospitals Zurich and Basel\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"alert-success\" style=\"padding:5px;\"><strong>SyftSuccess</strong>: <pre class=\"alert-success\" style=\"display:inline; font-family:inherit;\"> approved</pre></div><br/>"
      ],
      "text/plain": [
       "SyftSuccess: <class 'syft.service.code.user_code.UserCodeStatusCollection'> approved"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasite: V.A. Medical Center\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"alert-success\" style=\"padding:5px;\"><strong>SyftSuccess</strong>: <pre class=\"alert-success\" style=\"display:inline; font-family:inherit;\"> approved</pre></div><br/>"
      ],
      "text/plain": [
       "SyftSuccess: <class 'syft.service.code.user_code.UserCodeStatusCollection'> approved"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils import check_status_last_code_requests\n",
    "\n",
    "check_status_last_code_requests(datasites)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf09fc91-fecb-4ef7-b928-18941ecfda4e",
   "metadata": {},
   "source": [
    "## Step 4. Train Models on all datasites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f707f236-993c-4b17-b5f1-d8a7dc5ef9e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import dump_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f020ffc1-879b-4183-8f8d-0189badf1a2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasite: Cleveland Clinic\n",
      "Model saved in models/cleveland_clinic_model.jbl\n",
      "Datasite: Hungarian Inst. of Cardiology\n",
      "Model saved in models/hungarian_inst_of_cardiology_model.jbl\n",
      "Datasite: Univ. Hospitals Zurich and Basel\n",
      "Model saved in models/univ_hospitals_zurich_and_basel_model.jbl\n",
      "Datasite: V.A. Medical Center\n",
      "Model saved in models/va_medical_center_model.jbl\n"
     ]
    }
   ],
   "source": [
    "for name, datasite in datasites.items():\n",
    "    print(f\"Datasite: {name}\")\n",
    "    data_asset = datasite.datasets[\"Heart Disease Dataset\"].assets[\"Heart Study Data\"]\n",
    "    serialised_model = datasite.code.train(data=data_asset).get_from(datasite)\n",
    "    print(dump_model(datasite_name=name, model_buffer=serialised_model))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6bfdbb3-9bc4-4956-a97c-4936dfd0978b",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "We have trained and stored independently **four** Random Forest Classifiers, using the (non-public) data in each hospital, and <u>_without seeing nor downloading the training data_</u>! "
   ]
  }
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