FEDOT Tutorial - Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e0122b0-a6f5-4332-a5c1-718f09a46ec5",
   "metadata": {
    "tags": []
   },
   "source": [
    "<center><img src=https://github.com/nccr-itmo/FEDOT/raw/master/docs/fedot_logo.png></img></center>\n",
    "\n",
    "# <center>Решение задачи регрессии при помощи фреймворка [FEDOT](https://github.com/nccr-itmo/FEDOT)</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d4dee5-b9d3-4398-94ca-3ed762894427",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Введение"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1ee1cdc-3d8c-4645-85c4-8df3474502e3",
   "metadata": {},
   "source": [
    "Для решения любой задачи при помощи методов машинного обучения, исследователю требуется пройти множество шагов: от предобработки данных, выбора наиболее качественных признаков, преобразования признаков, подбора модели и до настройка гиперпараметров для запуска.\n",
    "\n",
    "Концепция задач **автоматического машинного обучения (AutoML)** направлена на уменьшении (или даже в полном исключении) участия человека в процессе решения задач машинного обучения. На основе доступных данных (обучающей выборки) осуществляется поиск оптимального **пайплайна** (последовательности действий над данными и моделей для предсказания) для наиболее лучшего решения задачи.\n",
    "\n",
    "<center><img src=https://habrastorage.org/r/w1560/webt/5r/9t/sd/5r9tsdb-inbszzrmy_jnmpphiwe.png></img></center>\n",
    "\n",
    "**FEDOT** - это фреймворк с открытым исходным кодом автоматического машинного обучения, который способен автоматизировать создание и оптимизацию пайплайнов машинного обучения и их элементов. \n",
    "Фреймворк позволяет компактно и эффективно решать различные задачи моделирования."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fd0baf-eb1c-4789-a60d-a829f180ca30",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Установка FEDOT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393dcef4-2e83-4f75-a1c4-01c8de965755",
   "metadata": {},
   "source": [
    "Наиболее простой способ установки фреймворка **FEDOT** через `pip`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b9155bd-a7db-4f09-a4bb-5f8c0805111c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install fedot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bffc1a90-aba4-4624-a3bc-77977c3b5298",
   "metadata": {},
   "source": [
    "Однако, на данный момент, мы рекомендуем вам его установить напрямую из master ветки репозитория:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd0d37ef-624e-4436-ae59-fb3bb2325844",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade --force-reinstall --no-cache-dir --quiet --no-warn-script-location https://github.com/nccr-itmo/FEDOT/archive/master.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d67ed3a0-9d56-4256-ace2-f1e749e44641",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"><b>Примечание</b>: существуют альтернаитвные способы установки, с которыми можно ознакомится в разделе <b><a href=\"https://fedot.readthedocs.io/en/latest/introduction/tutorial/quickstart.html\">Quickstart</a></b> в нашей документации.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2747768c-a467-49a9-98f4-f4b78a82ea5c",
   "metadata": {},
   "source": [
    "## Подготовка MLflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d87903d4-5e07-47f1-a11e-3782b9921613",
   "metadata": {},
   "source": [
    "**MLflow** — это Open Source-платформа, предназначенная для управления жизненным циклом моделей машинного обучения. В том числе она решает задачи воспроизведения экспериментов, публикации моделей и включает в себя центральный реестр моделей. С помощью **MLflow** можно обернуть свои эксперименты с **FEDOT** и другими фреймворками"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e733a13a-c131-4db5-b86d-603d5e1d6401",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "adbeaa92-b885-4cdc-b05f-c6c197193feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://ml-platform-3b3039c3f6f64a.ml.msk.vkcs.cloud'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlflow.get_tracking_uri()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c3e6e462-12c4-4ae0-9f90-7488ce7156d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Укажите свой, который указан в VK clouds\n",
    "mlflow_url_instance = \"https://ml-platform-3b3039c3f6f64a.ml.msk.vkcs.cloud\"\n",
    "mlflow.set_tracking_uri(mlflow_url_instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8d67b7b-7928-4d50-b26e-9eb819ae1e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://ml-platform-3b3039c3f6f64a.ml.msk.vkcs.cloud'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlflow.get_tracking_uri()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc3fcca0-7636-49c2-8055-e751dfaf86d5",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Данные"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9821d8a2-784c-4a72-87f8-f5af9375e18d",
   "metadata": {},
   "source": [
    "В качестве примера задачи регрессии возьмем данные из Kaggle соревнования [Molhack](https://www.kaggle.com/competitions/molhack-2022), организованного [AIRI](https://airi.net/ru/). В нем требуется научиться по трехмерной реализации молекулы предсказывать энергию. Предоставляется набор конформаций подвыборки молекул датасета MOSES. Для части конформаций будет так же дано значение энергии.\n",
    "\n",
    "Данные доступны по ссылке на [Google Drive](https://drive.google.com/drive/folders/11UsapfYlYMOsrLoUuYgfTn4c4g94L72B?usp=sharing).\n",
    "\n",
    "В первую очередь, необходимо их загрузить в пространство Jupyter, а затем открыть следующим образом:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cbbbcc88-f98e-4ae6-9f52-8a8a70240670",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reboot kernel after installing\n",
    "!pip install --quiet --no-warn-script-location ase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "13525a99-6660-4c2c-bdf8-c84b32564f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ase.db import connect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f1a72b0c-8c4f-49f6-ae89-0610c890e00e",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = connect(\"/home/jupyter-admin/train.db\")\n",
    "db_test = connect(\"/home/jupyter-admin/test.db\")\n",
    "\n",
    "features = []\n",
    "target = []\n",
    "\n",
    "# Загрузка данных из базы данных\n",
    "for row in db.select():\n",
    "    features.append([row.natoms, list(row.numbers)])\n",
    "    target.append(row.data['energy'])\n",
    "\n",
    "features_test = []\n",
    "\n",
    "for row in db_test.select():\n",
    "    features_test.append([row.natoms, list(row.numbers)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef94dbda-b5d0-4874-9545-08feaad8e583",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from collections import Counter\n",
    "\n",
    "# Векторизация признаков\n",
    "d = DictVectorizer()\n",
    "features_onehot = d.fit_transform([Counter(x[1]) for x in features])\n",
    "features_onehot_test = d.transform([Counter(x[1]) for x in features_test])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d76d31d6-95b0-447e-b9c7-fb3da4020c2a",
   "metadata": {},
   "source": [
    "В данном туториале данные будут открыты через внутренний тип данных в фреймворке **FEDOT** - `InputData()`.\n",
    "\n",
    "Из кусочков соберем данные в `InputData()`:\n",
    "* `idx` - переменная, которая хранит индексы;\n",
    "* `features` - переменная, которая хранит данные признаков;\n",
    "* `target` - переменная, которая хранит значения целевого признака;\n",
    "* `task` - хранит тип решаемой задачи;\n",
    "* `data_type` - хранит типа данных, в нашем случае, табличный;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7306067d-1a0d-471c-aeb3-5fcf80590a86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from fedot.core.data.data import InputData\n",
    "from fedot.core.repository.dataset_types import DataTypesEnum\n",
    "from fedot.core.repository.tasks import Task, TaskTypesEnum\n",
    "\n",
    "# Подготовка данных\n",
    "X_train, X_test, y_train, y_test = train_test_split(features_onehot, target)\n",
    "X_val = features_onehot_test\n",
    "\n",
    "task = Task(TaskTypesEnum.regression)\n",
    "\n",
    "train_input = InputData(\n",
    "    idx=np.arange(0, len(X_train.toarray())),  \n",
    "    features=X_train.toarray(),                \n",
    "    target=np.array(y_train),                  \n",
    "    task=task,                                 \n",
    "    data_type=DataTypesEnum.table              \n",
    ")\n",
    "\n",
    "predict_input = InputData(\n",
    "    idx=np.arange(0, len(X_test.toarray())),\n",
    "    features=X_test.toarray(),\n",
    "    target=np.array(y_test),\n",
    "    task=task,\n",
    "    data_type=DataTypesEnum.table\n",
    ")\n",
    "\n",
    "val_input = InputData(\n",
    "    idx=np.arange(0, len(X_val.toarray())),\n",
    "    features=X_val.toarray(),\n",
    "    target=None,\n",
    "    task=task,\n",
    "    data_type=DataTypesEnum.table\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "053b3284-6fe8-4095-a24e-e5b82c1c4058",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: (150174, 8) \\ Test: (50058, 8)\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "print('Train:', train_input.features.shape, '\\ Test:', predict_input.features.shape)\n",
    "print('-' * 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03298fe4-d2ce-49d3-88c5-0323415c63dc",
   "metadata": {},
   "source": [
    "Эксперименты будем отслеживать через **MLflow**. Для этого зададим общее имя эксперимента через `.set_experiment()` и запустим первый через `start_run()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "22361915-d622-43ad-9ebe-4fddcd3edef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    mlflow.set_experiment(\"automl_regression_airi\")\n",
    "except:\n",
    "    # Если появляется ошибка о сущесвовании экспримента с подобным именем, то удалить его можно следующим образом:\n",
    "    exp_id = mlflow.tracking.MlflowClient().get_experiment_by_name(\"automl_regression_airi\").experiment_id\n",
    "    mlflow.tracking.MlflowClient().restore_experiment(exp_id)\n",
    "    mlflow.set_experiment(\"automl_regression_airi\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a818c17-b463-4a00-999c-ca68bf964d01",
   "metadata": {},
   "source": [
    "И еще в конце объявим необходимые модули и методы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d381d41-8466-4497-bccc-da931196eceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pickle\n",
    "\n",
    "from time import time\n",
    "from fedot.api.main import Fedot\n",
    "\n",
    "def convert_json_to_pkl(path_to_json, path_to_pkl):\n",
    "    with open(path_to_json, \"rb\") as json_file:\n",
    "        pipeline = json.load(json_file)\n",
    "        with open(path_to_pkl, \"wb\") as pkl_file:\n",
    "            pickle.dump(pipeline, pkl_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9587005f-43a9-48c9-8adf-70e8143dcf4b",
   "metadata": {},
   "source": [
    "Теперь мы готовы приступить к запуску экспериментов"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad5eb34e-e625-4d4a-9620-199a61ac4b59",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Первый эксперимент - Простая модель"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeaa99f0-c332-4dfe-87c7-dfe7e74c69a2",
   "metadata": {},
   "source": [
    "Возьмем в качестве бейзлайна случайный лес для регрессии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a49572b-c08c-41f4-8260-bb6dfcd8a89b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ActiveRun: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlflow.start_run(run_name=\"baseline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1527ff91-6ba8-48b3-8589-2bc32df53d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_model = Fedot(\n",
    "    problem='regression',\n",
    "    timeout=1,\n",
    "    metric='mae',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25bf4617-ea8f-49c0-a51c-8f6c043b3b57",
   "metadata": {},
   "source": [
    "В базовый класс api фреймворка `Fedot` мы передаем следующие параметры:\n",
    "* `problem = 'regression'` - неообходимо дать понять какую задачу предстоить решать фреймворку\n",
    "* `timeout = 1` - такое малое количество времени, потому что модель уже найдена и композировать нет необходимости\n",
    "* `metric = 'mae'` - необходимая метрика MAE для этой задачи\n",
    "\n",
    "Для запуска обучения пайлпайна состоящего из одной модели достаточно указать в параметре `predefined_model` конкретную модель (можно и набор моделей)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f688005f-57a5-40a6-8dec-f4d22ee9280b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-10-23 12:32:16,975 - FEDOT logger - Final pipeline: {'depth': 1, 'length': 1, 'nodes': [rfr]}\n"
     ]
    }
   ],
   "source": [
    "start_time = time()\n",
    "baseline_model.fit(\n",
    "    features=train_input,\n",
    "    predefined_model=\"rfr\"\n",
    ")\n",
    "end_time = time()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "839f0150-b5f1-4ad5-8d20-3e34e2f9ede8",
   "metadata": {},
   "source": [
    "Теперь сделаем предсказания на тестовых данных и посмотрим на полученный результат по метрикам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "65f5632a-1515-4360-9349-31fa19f5979b",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = baseline_model.predict(features=predict_input)\n",
    "metrics_result = baseline_model.get_metrics(metric_names=['rmse', 'mae', 'r2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "350631d9-b206-40e5-8bb2-a65c23930fb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rmse': 12.597010578428195,\n",
       " 'mae': 0.9039954594262551,\n",
       " 'r2': 0.9998016754921496}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16ba5bcb-9034-4856-8ea0-81ff9e908a87",
   "metadata": {},
   "source": [
    "Отправим логи эксперимента в **MLflow**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0405a85a-7675-4b96-877f-762dad6ec77c",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_local = \"/home/jupyter-admin/\"\n",
    "\n",
    "# логирование парамметров запуска\n",
    "mlflow.log_param(\"preset\", \"rfr\")\n",
    "mlflow.log_param(\"timeout\", baseline_model.params.api_params['timeout'])\n",
    "mlflow.log_param(\"cv_folds\", baseline_model.params.api_params['cv_folds'])\n",
    "\n",
    "# логирование метрик и времени обучения\n",
    "mlflow.log_metric(\"rmse\", metrics_result['rmse'])\n",
    "mlflow.log_metric(\"mae\", metrics_result['mae'])\n",
    "mlflow.log_metric(\"r2\", metrics_result['r2'])\n",
    "mlflow.log_metric(\"training_time\", (end_time - start_time) / 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de629332-f957-4c16-9842-bf65cbc6dcc6",
   "metadata": {},
   "source": [
    "Завершаем логирование данного эксперимента и переходим в **MLflow UI** для просмотра результата"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4a144158-76a7-4a2e-b851-b3d5a8383521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://ml-platform-3b3039c3f6f64a.ml.msk.vkcs.cloud'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlflow.end_run()\n",
    "\n",
    "mlflow.get_tracking_uri()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6856a463-de85-4a4e-98cd-046f24815f51",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Второй эксперимент - fast_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cbf2f46e-c833-40ea-afeb-47aa7cb24501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ActiveRun: >"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlflow.start_run(run_name=\"fast_train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a36a26a8-07c0-41dc-98bf-46d0566ad1a4",
   "metadata": {},
   "source": [
    "**FEDOT** для поиска оптимального пайплайна позволяет отталкиваться от заданных моделей. Для этого необзодимо задать начальный пайплайн, давайте возьмём, например, линейную регрессию, и сделаем ее в качестве отправной точки для генерации новых пайплайнов.\n",
    "\n",
    "Для создания пайплайнов необходим класс `PipelineBuilder()`, в котором есть методы для ручной сборки.\n",
    "\n",
    "Добавим в пайплайн вершину при помощи `.add_node('linear')` и преобразуем переменную в объект пайплайна через метод `to_pipeline()` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "595e7431-d1eb-4e73-96e1-e5d9518f1a7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fedot.core.pipelines.pipeline_builder import PipelineBuilder\n",
    "\n",
    "builder = PipelineBuilder()\n",
    "\n",
    "linear_pipeline = builder.add_node('linear').to_pipeline()\n",
    "\n",
    "model = Fedot(\n",
    "    problem='regression',\n",
    "    timeout=20,\n",
    "    cv_folds=5,\n",
    "    metric='mae',\n",
    "    n_jobs=1,\n",
    "    preset='fast_train',\n",
    "    initial_assumption=linear_pipeline\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5973814-5f00-4ca8-96a4-d2a5728bab71",
   "metadata": {},
   "source": [
    "В базовый класс api фреймворка `Fedot` мы передаем следующие параметры:\n",
    "* `problem = 'regression'` - неообходимо дать понять какую задачу предстоить решать фреймворку\n",
    "* `timeout = 20` - указать сколько у него есть времени в минутах для решения\n",
    "* `cv_folds = 5` - количество фолдов для предовтращения переобучения\n",
    "* `metric = 'mae'` - какую метрику ему улучшать, в нашем случае метрика MAE \n",
    "* `n_jobs = 1` - количество используемых ядер для выполнения распараллеливания процесса поиска оптимального пайплайна\n",
    "* `preset = 'fast_train'` - используемый пресет моделей и операций. В данном случае, используем модели, которые быстро обучаются\n",
    "* `initial_assumption = linear_pipeline` - начальное приближение для улучшения его при помощи AutoML. На вход принимается объекты класса `Pipeline()`\n",
    "\n",
    "<div class=\"alert alert-block alert-info\"><b>Примечание</b>: существуют также и другие параметры для запуска FEDOT. Более подробнее можно ознакомится в разделе <b><a href=\"https://fedot.readthedocs.io/en/latest/api/api.html\">FEDOT API</a></b> в нашей документации.</div>\n",
    "\n",
    "Далее можно приступить к запуску процесса поиска оптимального пайплайна и его обучения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "13818df6-f7c8-4c32-9fe4-365a1b4702eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-10-23 12:32:31,564 - ApiComposer - Initial pipeline was fitted in 2.6 sec.\n",
      "2022-10-23 12:32:31,570 - ApiComposer - AutoML configured. Parameters tuning: True Time limit: 20 min Set of candidate models: ['scaling', 'dtreg', 'sgdr', 'ridge', 'linear', 'ransac_lin_reg', 'knnreg', 'normalization', 'lasso', 'adareg', 'pca', 'ransac_non_lin_reg', 'rfr']\n",
      "2022-10-23 12:32:31,578 - ApiComposer - Pipeline composition started.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generations:   0%|          | 1/10000 [00:00<?, ?gen/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-10-23 12:46:42,273 - GroupedCondition - Optimisation stopped: Time limit is reached\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generations:   0%|          | 1/10000 [14:10<?, ?gen/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-10-23 12:46:42,277 - ApiComposer - Hyperparameters tuning started with 6 min. timeout\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0%|          | 5/100000 [06:42<2235:19:57, 80.48s/trial, best loss: 0.13511218019397564] \n",
      "2022-10-23 12:56:00,206 - ApiComposer - Hyperparameters tuning finished\n",
      "2022-10-23 12:56:00,668 - ApiComposer - Model generation finished\n",
      "2022-10-23 12:56:18,244 - FEDOT logger - Final pipeline was fitted\n",
      "2022-10-23 12:56:18,247 - FEDOT logger - Final pipeline: {'depth': 2, 'length': 2, 'nodes': [linear, rfr]}\n"
     ]
    }
   ],
   "source": [
    "start_time = time()\n",
    "pipeline = model.fit(features=train_input)\n",
    "end_time = time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c72ba1a0-95c7-4ae4-9d9e-9a392a258ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(features=predict_input)\n",
    "metrics_result = model.get_metrics(metric_names=['rmse', 'mae', 'r2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dd59db59-27e5-4f80-af04-b69441319cf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rmse': 9.079863392262085,\n",
       " 'mae': 0.11337840820226754,\n",
       " 'r2': 0.9998969613817331}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4934a4d6-d6ab-4e48-933e-ba7656f594eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'depth': 2, 'length': 2, 'nodes': [linear, rfr]}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.current_pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f4a10e8-07cf-417e-9196-7bde25137339",
   "metadata": {},
   "source": [
    "**FEDOT** позволяет сохранить историю процесса генераци в формате **.json**. Однако **MLflow** принимает файлы **.pkl**, поэтому конвертируем их при помощи метода, который был объявлен в самом начале"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e299185b-bbd5-4732-8049-f18de9b90ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.history.save(\"history.json\");\n",
    "\n",
    "path_to_json = \"/home/jupyter-admin/history.json\"\n",
    "path_to_history_pkl = \"/home/jupyter-admin/history.pkl\"\n",
    "\n",
    "convert_json_to_pkl(path_to_json, path_to_history_pkl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af87af8d-2219-4db4-95e5-46a30e67a5e4",
   "metadata": {},
   "source": [
    "Также поступим и с найденным пайплайном"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3395a822-1774-4b00-96e6-923b23f207f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.current_pipeline.save(\"pipeline.json\", datetime_in_path=False)\n",
    "\n",
    "path_to_json = \"/home/jupyter-admin/pipeline/pipeline.json\"\n",
    "path_to_pipeline_pkl = \"/home/jupyter-admin/pipeline/pipeline.pkl\"\n",
    "\n",
    "convert_json_to_pkl(path_to_json, path_to_pipeline_pkl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eea8572-e59d-4c84-9d28-d6896c579e9f",
   "metadata": {},
   "source": [
    "Сохраним графики"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "74c58a04-d703-434c-956f-7a4377559559",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline.show(\"pipeline_structure.png\", node_size_scale=0.5, dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44bdaf9c-6d0b-45cf-a2d0-acad6538d9db",
   "metadata": {},
   "source": [
    "Теперь приступим к логированнию эксперимента в **MLflow**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7e94cc07-c22b-4248-9043-673d1daebdd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Путь до директории со всем содержимым\n",
    "path_to_local = \"/home/jupyter-admin/\"\n",
    "\n",
    "# логирование парамметров запуска\n",
    "mlflow.log_param(\"preset\", model.params.api_params['preset'])\n",
    "mlflow.log_param(\"timeout\", model.params.api_params['timeout'])\n",
    "mlflow.log_param(\"cv_folds\", model.params.api_params['cv_folds'])\n",
    "\n",
    "# логирование метрик и времени обучения\n",
    "mlflow.log_metric(\"rmse\", metrics_result['rmse'])\n",
    "mlflow.log_metric(\"mae\", metrics_result['mae'])\n",
    "mlflow.log_metric(\"r2\", metrics_result['r2'])\n",
    "mlflow.log_metric(\"training_time\", (end_time - start_time) / 60)\n",
    "\n",
    "# логирование структуры в виде графика\n",
    "mlflow.log_artifact(path_to_local + \"pipeline_structure.png\")\n",
    "\n",
    "# логирование модели и истории\n",
    "mlflow.log_artifact(path_to_pipeline_pkl)\n",
    "mlflow.log_artifact(path_to_history_pkl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "831e624b-022a-4514-8e9e-7f31538ceb88",
   "metadata": {},
   "source": [
    "Завершаем логирование данного запуска и можно перейти в **MLflow UI** для просмотра результата"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9f45899c-263b-4d52-88de-510b08573f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://ml-platform-3b3039c3f6f64a.ml.msk.vkcs.cloud'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlflow.end_run()\n",
    "\n",
    "mlflow.get_tracking_uri()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db5580a7-3e82-4d7b-af73-12a789c6f875",
   "metadata": {},
   "source": [
    "## Третий эксперимент - best_quality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5413cb46-6c0c-482e-b876-0ec003044760",
   "metadata": {},
   "source": [
    "Время для более продвинутых экспериментов. Попробуем найти лучший пайплайн.\n",
    "\n",
    "В этот раз будем использовать весь доступный пул моделей и операций для составления пайплайнов и немного изменим параметры запуска."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3b2544fd-b825-4e75-b05c-452b83217cee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ActiveRun: >"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlflow.start_run(run_name=\"best_quality\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bb56e53c-3cdd-453d-8ab1-a9a5abfe9df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Fedot(\n",
    "    problem='regression',\n",
    "    timeout=120,\n",
    "    cv_folds=10,\n",
    "    metric='mae',\n",
    "    n_jobs=-1,\n",
    "    preset='best_quality'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc0886b-d5cd-427f-a443-56cb438838e2",
   "metadata": {},
   "source": [
    "Изменим следующие параметры:\n",
    "* `timeout = 120` - увеличим время на генерацию пайплайнов\n",
    "* `cv_folds = 7` - увеличим количество фолдов для предовтращения переобучения\n",
    "* `n_jobs = -1` - задействуем все доступные мощности\n",
    "* `preset = 'best_quality'` - для генерации будем использовать все доступные модели и операции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9a1adab0-b3e3-4acf-94d0-7b4f555bca9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-10-23 14:32:36,373 - ApiComposer - Initial pipeline was fitted in 28.6 sec.\n",
      "2022-10-23 14:32:36,381 - ApiComposer - AutoML configured. Parameters tuning: True Time limit: 120 min Set of candidate models: ['gbr', 'scaling', 'treg', 'dtreg', 'sgdr', 'svr', 'ridge', 'normalization', 'ransac_non_lin_reg', 'lgbmreg', 'rfr', 'poly_features', 'fast_ica', 'isolation_forest_reg', 'linear', 'ransac_lin_reg', 'knnreg', 'pca', 'lasso', 'adareg']\n",
      "2022-10-23 14:32:36,518 - ApiComposer - Pipeline composition started.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generations:   0%|          | 1/10000 [00:00<?, ?gen/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-10-23 16:06:09,051 - GroupedCondition - Optimisation stopped: Time limit is reached\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generations:   0%|          | 1/10000 [1:33:32<?, ?gen/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-10-23 16:06:09,063 - ApiComposer - Hyperparameters tuning started with 26 min. timeout\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0%|          | 4/100000 [30:26<12684:30:59, 456.66s/trial, best loss: 0.9600189890728041]\n",
      "2022-10-23 16:50:32,208 - ApiComposer - Hyperparameters tuning finished\n",
      "2022-10-23 16:50:32,803 - ApiComposer - Model generation finished\n",
      "2022-10-23 16:51:18,972 - FEDOT logger - Final pipeline was fitted\n",
      "2022-10-23 16:51:18,974 - FEDOT logger - Final pipeline: {'depth': 3, 'length': 3, 'nodes': [rfr, sgdr, scaling]}\n"
     ]
    }
   ],
   "source": [
    "start_time = time()\n",
    "pipeline = model.fit(features=train_input)\n",
    "end_time = time()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9919a1e-8543-432a-8718-02b9f7aa1e01",
   "metadata": {},
   "source": [
    "Отправляем логи в **MLflow**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e8bb767f-d898-4e4b-8ebe-db6209a2e381",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(features=predict_input)\n",
    "metrics_result = model.get_metrics(metric_names=['rmse', 'mae', 'r2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "95a41e1a-03b0-4f0b-a20b-ce28b14ec899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rmse': 10.9310265670438, 'mae': 0.3004704981355796, 'r2': 0.9998506644187153}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fae6c69-d278-46b5-9287-a9b09812d429",
   "metadata": {},
   "source": [
    "Посмотрим на другие конкурирующие пайплайны"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5ced09c3-8afc-44c9-a1ce-9f33a505d987",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position | Fitness | Generation | Pipeline\n",
      "  0,   0.3324,    g2-i0, ((/n_scaling;)/n_sgdr;)/n_rfr_{'n_jobs': 1}\n",
      " 26,   0.7847,   g2-i26, ((/n_scaling;)/n_dtreg;)/n_rfr_{'n_jobs': 1}\n",
      " 24,   0.7888,   g2-i24, (/n_scaling;)/n_dtreg\n",
      " 22,   0.8196,   g2-i22, ((/n_scaling;)/n_rfr_{'n_jobs': 1};)/n_rfr_{'n_jobs': 1}\n",
      " 16,   0.8236,   g2-i16, (/n_fast_ica_{'whiten': 'arbitrary-variance'};)/n_treg\n",
      " 25,   0.8271,   g2-i25, (((/n_scaling;)/n_rfr_{'n_jobs': 1};)/n_rfr_{'n_jobs': 1};)/n_linear\n",
      " 15,   0.9389,   g2-i15, ((/n_fast_ica_{'whiten': 'arbitrary-variance'};;/n_ransac_lin_reg_{'min_samples': 0.4, 'residual_threshold': 10, 'max_trials': 100, 'max_skips': 1000};)/n_scaling;)/n_rfr_{'n_jobs': 1}\n",
      " 23,   0.9395,   g2-i23, (/n_fast_ica_{'whiten': 'arbitrary-variance'};;/n_ransac_lin_reg_{'min_samples': 0.4, 'residual_threshold': 10, 'max_trials': 100, 'max_skips': 1000};)/n_rfr_{'n_jobs': 1}\n",
      "  5,   1.0812,    g2-i5, (/n_scaling;)/n_rfr_{'n_jobs': 1}\n",
      " 10,   1.0812,   g2-i10, (/n_normalization;)/n_rfr_{'n_jobs': 1}\n",
      " I0  1.0812 | - | (/n_scaling;)/n_rfr_{'n_jobs': 1}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model.history.get_leaderboard())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f28c2219-9586-4489-953c-14d2f8f40f09",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.history.save(\"history.json\");\n",
    "\n",
    "path_to_json = \"/home/jupyter-admin/history.json\"\n",
    "path_to_history_pkl = \"/home/jupyter-admin/history.pkl\"\n",
    "\n",
    "convert_json_to_pkl(path_to_json, path_to_history_pkl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "222b84da-dad3-4073-9dba-c06d896cc3ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.current_pipeline.save(\"pipeline.json\", datetime_in_path=False)\n",
    "\n",
    "path_to_json = \"/home/jupyter-admin/pipeline/pipeline.json\"\n",
    "path_to_pipeline_pkl = \"/home/jupyter-admin/pipeline/pipeline.pkl\"\n",
    "\n",
    "convert_json_to_pkl(path_to_json, path_to_pipeline_pkl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bbab2997-87e9-4717-bc5d-9777b245dc97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 692.173x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.history.show.operations_kde(dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7734d670-bb68-4392-9644-8ba10ccb20ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline.show(\"pipeline_structure.png\", node_size_scale=0.5, dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8470332e-2369-4b71-9fe1-66cd735ddd82",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_local = \"/home/jupyter-admin/\"\n",
    "\n",
    "# Логгирование парамметров запуска\n",
    "mlflow.log_param(\"preset\", model.params.api_params['preset'])\n",
    "mlflow.log_param(\"timeout\", model.params.api_params['timeout'])\n",
    "mlflow.log_param(\"cv_folds\", model.params.api_params['cv_folds'])\n",
    "\n",
    "# Логгирование метрик и времени обучения\n",
    "mlflow.log_metric(\"rmse\", metrics_result['rmse'])\n",
    "mlflow.log_metric(\"mae\", metrics_result['mae'])\n",
    "mlflow.log_metric(\"r2\", metrics_result['r2'])\n",
    "mlflow.log_metric(\"training_time\", (end_time - start_time) / 60)\n",
    "\n",
    "# логирование структуры в виде графика\n",
    "mlflow.log_artifact(path_to_local + \"pipeline_structure.png\")\n",
    "\n",
    "# логирование модели и истории\n",
    "mlflow.log_artifact(path_to_pipeline_pkl)\n",
    "mlflow.log_artifact(path_to_history_pkl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "54ce507d-16a2-44f7-88cd-254941e343bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://ml-platform-3b3039c3f6f64a.ml.msk.vkcs.cloud'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlflow.end_run()\n",
    "\n",
    "mlflow.get_tracking_uri()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36ab8a63-0bd8-4b76-9059-1a9a3b69bd34",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Послесловие"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa1bf5b6-0305-4444-b01f-34e6c89c654e",
   "metadata": {},
   "source": [
    "В данном блокноте мы показали вам как запустить фреймворк **FEDOT** совместно с инструментом логирования **MLflow** для экспериментов и решения задачи регрессии.\n",
    "\n",
    "Теперь вы можете попробовать провести свои эксперименты.\n",
    "\n",
    "<i class=\"fa fa-github\" style=\"font-size:24px\"></i> - <a href=\"https://github.com/nccr-itmo/FEDOT\"> Наш репозиторий </a>\n",
    "\n",
    "<i class=\"fa fa-telegram\" style=\"font-size:24px;color:blue\"></i> - <a href=\"https://t.me/FEDOT_helpdesk\">Telegram канал поддержки</a>\n",
    "\n",
    "<i class=\"fa fa-youtube-play\" style=\"font-size:24px;color:red\"></i> - <a href=\"https://www.youtube.com/channel/UC4K9QWaEUpT_p3R4FeDp5jA\">Наш канал на Youtube</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7184383-1223-4759-8938-7ee5b912e3d7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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