notebooks/latest/7_multimodal_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p13yvATsWY_P"
   },
   "source": [
    "<center><img src=https://github.com/nccr-itmo/FEDOT/raw/master/docs/fedot_logo.png></img></center>\n",
    "\n",
    "# <center>Case of multimodal data classification using the [FEDOT](https://github.com/nccr-itmo/FEDOT) framework</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F5Px2ZxOW0_2"
   },
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FNrNTOz0W4sp"
   },
   "source": [
    "**FEDOT** is an open source automatic machine learning framework that is capable of automating the creation and optimization of machine learning pipelines and their elements.\n",
    "The framework allows you to compactly and efficiently solve various modeling problems.\n",
    "\n",
    "**Multimodal data** is data that has a different nature (tables, text, images, time series). Humans perceive the world in [a multimodal way](https://en.wikipedia.org/wiki/McGurk_effect), so using this approach in machine learning can also work. Indeed, the sharing of several types of data improves the quality of the model at the expense of information that may be contained in one modality and absent in another."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5B5Ujm6UW2sR"
   },
   "source": [
    "### FEDOT installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oAe6x3Luf_eG"
   },
   "source": [
    "Let's install FEDOT of version 0.7.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "QPez9dOVghZb"
   },
   "outputs": [],
   "source": [
    "!pip install fedot==0.7.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "85pKrF1rgoIg"
   },
   "source": [
    "<div class=\"alert alert-block alert-info\"><b>Note</b>: there are alternative ways to install the framework and its dependencies, which can be found in the section <b><a href=\"https://fedot.readthedocs.io/en/latest/introduction/tutorial/quickstart.html\">Quickstart</a></b> in our documentation</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3jnhhbE9gu56"
   },
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "txU5TbALgxVd"
   },
   "source": [
    "As an example, let's take the prepared data from the [Wine Reviews](https://www.kaggle.com/datasets/zynicide/wine-reviews) dataset (winemag-data_first150k). For convenience and speed of work, the number of lines and classes has been reduced, no other preprocessing has been carried out (we will leave this to Fedot)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8_Tnn3TfkFkh"
   },
   "source": [
    "The **description** field contains a textual description of the different varieties of wine, and the target variable is the **variety** field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "vIJMAxW6jc-a",
    "outputId": "ad18fa0a-6002-4a31-9afa-7ea2af604e1f"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>This tremendous 100% varietal wine hails from ...</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>This spent 20 months in 30% new French oak, an...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>96</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>This re-named vineyard was formerly bottled as...</td>\n",
       "      <td>Silice</td>\n",
       "      <td>95</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Chehalem Mountains</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Bergström</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>The producer sources from two blocks of the vi...</td>\n",
       "      <td>Gap's Crown Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>60.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma Coast</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Blue Farm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>From 18-year-old vines, this supple well-balan...</td>\n",
       "      <td>Estate Vineyard Wadensvil Block</td>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Ribbon Ridge</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Patricia Green Cellars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>US</td>\n",
       "      <td>This is a rich, broad-shouldered Pinot Noir, w...</td>\n",
       "      <td>Brown Ranch</td>\n",
       "      <td>88</td>\n",
       "      <td>72.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Carneros</td>\n",
       "      <td>Napa-Sonoma</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Saintsbury</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>US</td>\n",
       "      <td>A delicate, almost fragile wine, this opens wi...</td>\n",
       "      <td>Arbre Vert Vineyard</td>\n",
       "      <td>88</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Siduri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>US</td>\n",
       "      <td>Even with some hours of breathing time, this r...</td>\n",
       "      <td>La Colina Vineyard</td>\n",
       "      <td>88</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Dundee Hills</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Torii Mor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>US</td>\n",
       "      <td>Loaded with scents and flavors of chocolate, m...</td>\n",
       "      <td>Shea Vineyard</td>\n",
       "      <td>88</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Walt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>US</td>\n",
       "      <td>Stewed tomato, graphite and mushrooms in a bro...</td>\n",
       "      <td>Turner Vineyard</td>\n",
       "      <td>88</td>\n",
       "      <td>50.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sta. Rita Hills</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Carr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>779 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    country                                        description  \\\n",
       "0        US  This tremendous 100% varietal wine hails from ...   \n",
       "1        US  This spent 20 months in 30% new French oak, an...   \n",
       "2        US  This re-named vineyard was formerly bottled as...   \n",
       "3        US  The producer sources from two blocks of the vi...   \n",
       "4        US  From 18-year-old vines, this supple well-balan...   \n",
       "..      ...                                                ...   \n",
       "774      US  This is a rich, broad-shouldered Pinot Noir, w...   \n",
       "775      US  A delicate, almost fragile wine, this opens wi...   \n",
       "776      US  Even with some hours of breathing time, this r...   \n",
       "777      US  Loaded with scents and flavors of chocolate, m...   \n",
       "778      US  Stewed tomato, graphite and mushrooms in a bro...   \n",
       "\n",
       "                         designation  points  price    province  \\\n",
       "0                  Martha's Vineyard      96  235.0  California   \n",
       "1                            Reserve      96   65.0      Oregon   \n",
       "2                             Silice      95   65.0      Oregon   \n",
       "3               Gap's Crown Vineyard      95   60.0  California   \n",
       "4    Estate Vineyard Wadensvil Block      95   48.0      Oregon   \n",
       "..                               ...     ...    ...         ...   \n",
       "774                      Brown Ranch      88   72.0  California   \n",
       "775              Arbre Vert Vineyard      88   42.0      Oregon   \n",
       "776               La Colina Vineyard      88   60.0      Oregon   \n",
       "777                    Shea Vineyard      88   65.0      Oregon   \n",
       "778                  Turner Vineyard      88   50.0  California   \n",
       "\n",
       "               region_1           region_2             variety  \\\n",
       "0           Napa Valley               Napa  Cabernet Sauvignon   \n",
       "1     Willamette Valley  Willamette Valley          Pinot Noir   \n",
       "2    Chehalem Mountains  Willamette Valley          Pinot Noir   \n",
       "3          Sonoma Coast             Sonoma          Pinot Noir   \n",
       "4          Ribbon Ridge  Willamette Valley          Pinot Noir   \n",
       "..                  ...                ...                 ...   \n",
       "774            Carneros        Napa-Sonoma          Pinot Noir   \n",
       "775   Willamette Valley  Willamette Valley          Pinot Noir   \n",
       "776        Dundee Hills  Willamette Valley          Pinot Noir   \n",
       "777   Willamette Valley  Willamette Valley          Pinot Noir   \n",
       "778     Sta. Rita Hills      Central Coast          Pinot Noir   \n",
       "\n",
       "                     winery  \n",
       "0                     Heitz  \n",
       "1                     Ponzi  \n",
       "2                 Bergström  \n",
       "3                 Blue Farm  \n",
       "4    Patricia Green Cellars  \n",
       "..                      ...  \n",
       "774              Saintsbury  \n",
       "775                  Siduri  \n",
       "776               Torii Mor  \n",
       "777                    Walt  \n",
       "778                    Carr  \n",
       "\n",
       "[779 rows x 10 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "path = '../data/multimodal_wine.csv'\n",
    "\n",
    "df = pd.read_csv(path)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8GydajEYj8Iw"
   },
   "source": [
    "Data has been loaded. Now you can try several solutions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "klmiJmkQkuKX"
   },
   "source": [
    "## Using FEDOT's multimodal functionality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "agJmvU61k3AD"
   },
   "source": [
    "You can solve this classification problem using FEDOT with a few lines of code. First, we import the necessary modules and classes, and also split the selection into train and test parts.\n",
    "\n",
    "FEDOT will automatically detect the **description** field as a text field and apply various text processing models to it. You should also point out the column with the target variable **variety** and the absence of a separate index column in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "zR-97g5JlNoA"
   },
   "outputs": [],
   "source": [
    "from fedot.api.main import Fedot\n",
    "from fedot.core.data.data_split import train_test_data_setup\n",
    "from fedot.core.data.multi_modal import MultiModalData\n",
    "\n",
    "data = MultiModalData.from_csv(file_path=path, task='classification', target_columns='variety', index_col=None)\n",
    "fit_data, predict_data = train_test_data_setup(data, shuffle_flag=True, split_ratio=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zqZX-fWFtbpE"
   },
   "source": [
    "Let's set the parameters of the AutoML model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "SCQrz6-El7UF"
   },
   "outputs": [],
   "source": [
    "automl_model = Fedot(problem='classification', metric=['roc_auc', 'f1'], timeout=10, seed=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UaUpYiUum6kC"
   },
   "source": [
    "* `problem = 'classification'` - problem solved by the framework\n",
    "* `timeout = 3` - framework time in minutes\n",
    "* `seed = 42` - let's fix the seed for reproducibility\n",
    "\n",
    "<div class=\"alert alert-block alert-info\"><b>Note</b>: there are also other options for running FEDOT. More details can be found in the section <b><a href=\"https://fedot.readthedocs.io/en/latest/api/api.html\">FEDOT API</a></b> in our documentation.</div>\n",
    "\n",
    "Next, you can start the process of finding the optimal pipeline and its training. It is necessary to pass data for training to the `.fit()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7GjZbid3o4ce",
    "outputId": "997e2cf5-c9f5-48e9-9c5e-37a2655a69a1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-10-03 13:14:37,573 - AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 2.4 MiB, max: 15.9 MiB\n",
      "2023-10-03 13:14:37,575 - ApiComposer - Initial pipeline was fitted in 3.3 sec.\n",
      "2023-10-03 13:14:37,576 - AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n",
      "2023-10-03 13:14:37,587 - ApiComposer - AutoML configured. Parameters tuning: False. Time limit: 10 min. Set of candidate models: ['scaling', 'pca', 'dt', 'fast_ica', 'bernb', 'knn', 'lgbm', 'qda', 'rf', 'mlp', 'resample', 'normalization', 'isolation_forest_class', 'logit', 'poly_features'].\n",
      "2023-10-03 13:14:37,592 - 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": [
      "2023-10-03 13:15:05,223 - MultiprocessingDispatcher - 3 individuals out of 3 in previous population were evaluated successfully.\n",
      "2023-10-03 13:15:25,619 - MultiprocessingDispatcher - 21 individuals out of 21 in previous population were evaluated successfully.\n",
      "2023-10-03 13:15:49,047 - MultiprocessingDispatcher - 15 individuals out of 15 in previous population were evaluated successfully.\n",
      "2023-10-03 13:15:56,092 - MultiprocessingDispatcher - 5 individuals out of 5 in previous population were evaluated successfully.\n",
      "2023-10-03 13:16:09,301 - MultiprocessingDispatcher - 20 individuals out of 20 in previous population were evaluated successfully.\n",
      "2023-10-03 13:16:14,117 - MultiprocessingDispatcher - 14 individuals out of 14 in previous population were evaluated successfully.\n",
      "2023-10-03 13:16:38,849 - MultiprocessingDispatcher - 34 individuals out of 34 in previous population were evaluated successfully.\n",
      "2023-10-03 13:17:03,215 - MultiprocessingDispatcher - 28 individuals out of 28 in previous population were evaluated successfully.\n",
      "2023-10-03 13:17:41,903 - MultiprocessingDispatcher - 24 individuals out of 24 in previous population were evaluated successfully.\n",
      "2023-10-03 13:17:41,983 - MultiprocessingDispatcher - 34 individuals out of 34 in previous population were evaluated successfully.\n",
      "2023-10-03 13:18:15,772 - MultiprocessingDispatcher - 23 individuals out of 25 in previous population were evaluated successfully.\n",
      "2023-10-03 13:18:21,705 - MultiprocessingDispatcher - 7 individuals out of 7 in previous population were evaluated successfully.\n",
      "2023-10-03 13:18:34,681 - MultiprocessingDispatcher - 4 individuals out of 4 in previous population were evaluated successfully.\n",
      "2023-10-03 13:18:54,723 - MultiprocessingDispatcher - 27 individuals out of 28 in previous population were evaluated successfully.\n",
      "2023-10-03 13:18:58,029 - MultiprocessingDispatcher - 4 individuals out of 4 in previous population were evaluated successfully.\n",
      "2023-10-03 13:19:21,752 - MultiprocessingDispatcher - 23 individuals out of 23 in previous population were evaluated successfully.\n",
      "2023-10-03 13:19:26,215 - MultiprocessingDispatcher - 8 individuals out of 8 in previous population were evaluated successfully.\n",
      "2023-10-03 13:19:40,086 - MultiprocessingDispatcher - 21 individuals out of 21 in previous population were evaluated successfully.\n",
      "2023-10-03 13:20:03,956 - MultiprocessingDispatcher - 11 individuals out of 11 in previous population were evaluated successfully.\n",
      "2023-10-03 13:20:38,744 - MultiprocessingDispatcher - 24 individuals out of 24 in previous population were evaluated successfully.\n",
      "2023-10-03 13:20:49,772 - MultiprocessingDispatcher - 11 individuals out of 11 in previous population were evaluated successfully.\n",
      "2023-10-03 13:20:49,823 - MultiprocessingDispatcher - 31 individuals out of 31 in previous population were evaluated successfully.\n",
      "2023-10-03 13:21:16,133 - MultiprocessingDispatcher - 26 individuals out of 26 in previous population were evaluated successfully.\n",
      "2023-10-03 13:21:22,461 - MultiprocessingDispatcher - 5 individuals out of 5 in previous population were evaluated successfully.\n",
      "2023-10-03 13:21:51,519 - MultiprocessingDispatcher - 23 individuals out of 23 in previous population were evaluated successfully.\n",
      "2023-10-03 13:21:59,697 - MultiprocessingDispatcher - 7 individuals out of 7 in previous population were evaluated successfully.\n",
      "2023-10-03 13:22:35,610 - MultiprocessingDispatcher - 19 individuals out of 20 in previous population were evaluated successfully.\n",
      "2023-10-03 13:22:48,316 - MultiprocessingDispatcher - 10 individuals out of 10 in previous population were evaluated successfully.\n",
      "2023-10-03 13:23:19,326 - MultiprocessingDispatcher - 20 individuals out of 20 in previous population were evaluated successfully.\n",
      "2023-10-03 13:23:39,333 - MultiprocessingDispatcher - 10 individuals out of 10 in previous population were evaluated successfully.\n",
      "2023-10-03 13:24:10,525 - MultiprocessingDispatcher - 24 individuals out of 24 in previous population were evaluated successfully.\n",
      "2023-10-03 13:24:13,671 - MultiprocessingDispatcher - 3 individuals out of 3 in previous population were evaluated successfully.\n",
      "2023-10-03 13:24:15,585 - MultiprocessingDispatcher - 1 individuals out of 1 in previous population were evaluated successfully.\n",
      "2023-10-03 13:24:15,629 - MultiprocessingDispatcher - 31 individuals out of 31 in previous population were evaluated successfully.\n",
      "2023-10-03 13:24:15,753 - GroupedCondition - Optimisation stopped: Time limit is reached\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generations:   0%|          | 1/10000 [09:38<?, ?gen/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-10-03 13:24:15,858 - ApiComposer - Model generation finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-10-03 13:24:18,772 - FEDOT logger - Final pipeline was fitted\n",
      "2023-10-03 13:24:18,774 - FEDOT logger - Final pipeline: {'depth': 4, 'length': 7, 'nodes': [mlp, pca, scaling, data_source_table, poly_features, tfidf, data_source_text/description]}\n",
      "mlp - {}\n",
      "pca - {'svd_solver': 'full', 'n_components': 0.7}\n",
      "scaling - {}\n",
      "data_source_table - {}\n",
      "poly_features - {}\n",
      "tfidf - {'min_df': 0.04728620316406402, 'max_df': 0.935066958250478, 'ngram_range': (1, 1)}\n",
      "data_source_text/description - {}\n",
      "2023-10-03 13:24:18,775 - MemoryAnalytics - Memory consumption for finish in main session: current 12.7 MiB, max: 35.3 MiB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'depth': 4, 'length': 7, 'nodes': [mlp, pca, scaling, data_source_table, poly_features, tfidf, data_source_text/description]}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "automl_model.fit(features=fit_data,\n",
    "                 target=fit_data.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6DxUQWUxpCxt"
   },
   "source": [
    "Pipeline's structure in text form is shown above. For visualization, we will use the `.show()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 707
    },
    "id": "6avy-REIpS93",
    "outputId": "ed8b5c97-41b5-405a-f381-ff8fdc1647e8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "automl_model.current_pipeline.show(node_size_scale=0.5, dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pk7qeISHprH2"
   },
   "source": [
    "Let's predict the target variable and compare the metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mR1vWU4GHWee",
    "outputId": "a82f1720-35fb-47a0-dd47-530c3e4099f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'roc_auc': 0.5, 'f1': 0.79}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = automl_model.predict(predict_data)\n",
    "\n",
    "metrics = automl_model.get_metrics()\n",
    "\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XajThhNVp37Q"
   },
   "source": [
    "Let's visualize the metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "id": "8CUUYi9VHaf6",
    "outputId": "8d353b53-42c5-429f-ebd0-b04c04fc01d2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "automl_model.plot_prediction()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VHYpyAk_sKoX"
   },
   "source": [
    "Let's save the history of the generation process in JSON format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "g8xJ3mY7sQPy"
   },
   "outputs": [],
   "source": [
    "automl_model.history.save('multimodal_history.json');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "azzd3OXguGi7"
   },
   "source": [
    "Using the history, you can look at the change in the value of the metrics for each stage of generation. FEDOT uses evolutionary algorithms and compares the metrics of several individual pipelines in each generation, so the best visualization method is a boxplot, where the middle line means the median value of the metric in a generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 465
    },
    "id": "1R84rHsbHslH",
    "outputId": "aaffc49f-f961-44a6-89ee-cbd5d153e3f5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "automl_model.history.show.fitness_box(best_fraction=0.5, dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xMQo8xIEyqZ-"
   },
   "source": [
    "You can also look at which specific models dominated the pipelines during the generation process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "P5eR-9xLHvJm",
    "outputId": "f502072b-3ffc-491f-c835-df9ff9532fb9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 751x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "automl_model.history.show.operations_kde(dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n1x32ozP0Lpt"
   },
   "source": [
    "### Baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JawJrtRo0Oel"
   },
   "source": [
    "To compare the quality of the model, we will run FEDOT on the same data, but training only the standard initial pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H3E8PgQY0iBI",
    "outputId": "3beb6bb8-4717-4c87-80c6-97d7b980887a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-10-03 13:24:26,570 - FEDOT logger - Final pipeline: {'depth': 5, 'length': 8, 'nodes': [rf, rf, scaling, tfidf, data_source_text/description, rf, scaling, data_source_table]}\n",
      "rf - {'n_jobs': 1}\n",
      "rf - {'n_jobs': 1}\n",
      "scaling - {}\n",
      "tfidf - {'min_df': 0.1, 'max_df': 0.9}\n",
      "data_source_text/description - {}\n",
      "rf - {'n_jobs': 1}\n",
      "scaling - {}\n",
      "data_source_table - {}\n",
      "2023-10-03 13:24:26,571 - MemoryAnalytics - Memory consumption for finish in main session: current 5.5 MiB, max: 22.4 MiB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'depth': 5, 'length': 8, 'nodes': [rf, rf, scaling, tfidf, data_source_text/description, rf, scaling, data_source_table]}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline_model = Fedot(problem='classification', metric=['roc_auc', 'f1'], seed=42)\n",
    "baseline_model.fit(features=fit_data,\n",
    "                 target=fit_data.target,\n",
    "                 predefined_model='auto')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v3oyvW9it0y4"
   },
   "source": [
    "The `timeout` parameter is not used in this case, so we will remove it. The `predefined_model` parameter specifies that we will not run AutoML, but will simply train the model on the standard initial pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p31QPBDS0z_n"
   },
   "source": [
    "Let's visualize the pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 707
    },
    "id": "POEajqK7H0Cb",
    "outputId": "564e63ef-2e80-42cc-805a-8c30ae9fcf5f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "baseline_model.current_pipeline.show(node_size_scale=0.5, dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EtPJigNz1B_V"
   },
   "source": [
    "Let's get and compare the metrics. It is noticeable that even a small training time significantly improves the quality metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Co8VHZaiH3E7",
    "outputId": "4720e844-b8ac-40fa-c25a-305d93eb1bd3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'roc_auc': 0.5, 'f1': 0.681}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = baseline_model.predict(predict_data)\n",
    "\n",
    "baseline_metrics = baseline_model.get_metrics()\n",
    "\n",
    "baseline_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mjt9k1sg2iyH"
   },
   "source": [
    "### Tabular data only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4Q3CSEjv2quk"
   },
   "source": [
    "To verify the significance of both modalities, let's train the base model only on tabular data. To do this, simply exclude the **description** text field when loading the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "WbNHiF7720hG"
   },
   "outputs": [],
   "source": [
    "table_data = MultiModalData.from_csv(file_path=path, task='classification', target_columns='variety', \n",
    "                                     columns_to_drop=['description'], index_col=None)\n",
    "table_fit_data, table_predict_data = train_test_data_setup(table_data, shuffle_flag=True, split_ratio=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mH-wU3Qv3Izr",
    "outputId": "c98dc297-5438-4bcc-ef25-adccff4a6611"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-10-03 13:24:30,066 - FEDOT logger - Final pipeline: {'depth': 4, 'length': 4, 'nodes': [rf, rf, scaling, data_source_table]}\n",
      "rf - {'n_jobs': 1}\n",
      "rf - {'n_jobs': 1}\n",
      "scaling - {}\n",
      "data_source_table - {}\n",
      "2023-10-03 13:24:30,068 - MemoryAnalytics - Memory consumption for finish in main session: current 4.9 MiB, max: 21.6 MiB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'depth': 4, 'length': 4, 'nodes': [rf, rf, scaling, data_source_table]}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_model = Fedot(problem='classification', metric=['roc_auc', 'f1'], seed=42)\n",
    "table_model.fit(features=table_fit_data,\n",
    "                 target=table_fit_data.target,\n",
    "                 predefined_model='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 707
    },
    "id": "xPEANGRFH8pD",
    "outputId": "27c628d7-39f1-4c8a-a671-5646cc8fc6ca"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_model.current_pipeline.show(node_size_scale=0.5, dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-6-e7tT23VJE"
   },
   "source": [
    "It is noticeable that the metrics of this model are noticeably worse than those of the multimodal one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MT_bKg1oH-60",
    "outputId": "d86a7aeb-749d-4a10-d29f-cbfc7386c370"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'roc_auc': 0.5, 'f1': 0.673}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = table_model.predict(table_predict_data)\n",
    "\n",
    "table_metrics = table_model.get_metrics()\n",
    "\n",
    "table_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RYpMUU5634UW"
   },
   "source": [
    "### Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QeFZ1Wlz36Dz"
   },
   "source": [
    "Let's compare the metrics of the AutoML model, the multimodal baseline, and the tabular baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sFNPZ0vrID-6",
    "outputId": "eae7bfab-3208-4fa0-f265-96d486d2b11c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC-AUC of AutoML model is 0.5\n",
      "ROC-AUC of baseline model is 0.5\n",
      "ROC-AUC of baseline table model is 0.5\n"
     ]
    }
   ],
   "source": [
    "print(f'ROC-AUC of AutoML model is {round(metrics[\"roc_auc\"], 3)}')\n",
    "print(f'ROC-AUC of baseline model is {round(baseline_metrics[\"roc_auc\"], 3)}')\n",
    "print(f'ROC-AUC of baseline table model is {round(table_metrics[\"roc_auc\"], 3)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p6H1zBvgIG4v",
    "outputId": "5653dac4-8aff-4cf1-9a8f-c6a15e5a9ec8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 of AutoML model is 0.79\n",
      "F1 of baseline model is 0.681\n",
      "F1 of baseline table model is 0.673\n"
     ]
    }
   ],
   "source": [
    "print(f'F1 of AutoML model is {round(metrics[\"f1\"], 3)}')\n",
    "print(f'F1 of baseline model is {round(baseline_metrics[\"f1\"], 3)}')\n",
    "print(f'F1 of baseline table model is {round(table_metrics[\"f1\"], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RtS7-wW34U2p"
   },
   "source": [
    "## Afterword"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b1pW50K64WMJ"
   },
   "source": [
    "In this notebook, we showed you how to run the **FEDOT** framework to solve a classification problem using multimodal data using the API. As you can see, this is done quite simply.\n",
    "\n",
    "Now you can try running the **FEDOT** automatic machine learning framework on your data.\n",
    "\n",
    "<i class=\"fa fa-github\" style=\"font-size:24px\"></i> - <a href=\"https://github.com/nccr-itmo/FEDOT\"> Our repository </a>\n",
    "\n",
    "<i class=\"fa fa-telegram\" style=\"font-size:24px;color:blue\"></i> - <a href=\"https://t.me/FEDOT_helpdesk\">Support chat in Telegram</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\">Our Youtube channel</a>"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}