demos/OracleAutoMLx_Recommendation.ipynb

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    "\n",
    "<h1><font color=red>Building a Recommender using AutoMLx</font></h1>\n",
    "<p style=\"margin-left:5%; margin-right:5%;\">by the <font color=teal> Oracle AutoMLx Team </font></p>\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Recommendation Demo Notebook.\n",
    "\n",
    "Copyright © 2024, Oracle and/or its affiliates.\n",
    "\n",
    "Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/"
   ]
  },
  {
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   "source": [
    "# Overview of this Notebook\n",
    "\n",
    "In this notebook we will build a recommender using the Oracle AutoMLx tool for the Movielens 100k dataset to predict the next item that users will most likely watch, based on their ratings history.\n",
    "We explore the various options provided by the Oracle AutoMLx tool, allowing the user to control the AutoMLx training process. We finally evaluate the different models trained by AutoMLx. Depending on the machine running this notebook, it can take up to minutes.\n",
    "\n",
    "---\n",
    "## Prerequisites:\n",
    "\n",
    "   - Experience level: Novice (Python and Machine Learning)\n",
    "   - Professional experience: Some industry experience\n",
    "---\n",
    "\n",
    "## Business Use:\n",
    "\n",
    "Data analytics and modeling problems using Machine Learning (ML) are becoming popular and often rely on data science expertise to build accurate ML models. Such modeling tasks primarily involve the following steps:\n",
    "- Preprocess dataset (clean, impute, engineer features, normalize).\n",
    "- Pick an appropriate model for the given dataset and prediction task at hand.\n",
    "- Tune the chosen model’s hyperparameters for the given dataset.\n",
    "\n",
    "All of these steps are significantly time consuming and heavily rely on data scientist expertise. Unfortunately, to make this problem harder, the best feature subset, model, and hyperparameter choice widely varies with the dataset and the prediction task. Hence, there is no one-size-fits-all solution to achieve reasonably good model performance. Using a simple Python API, AutoML can quickly jump-start the datascience process with an accurately-tuned model and appropriate features for a given prediction task.\n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "- <a href='#setup'> Setup</a>\n",
    "- <a href='#load-data'> Load the Movielens 100k dataset</a>\n",
    "  - <a href='#coltypes'> Define the column types</a>\n",
    "  - <a href='#dataset-split'>Splitting the dataset</a>\n",
    "- <a href='#automl'> AutoML</a>\n",
    "  - <a href='#provider'> Create an Instance of AutoMLx</a>\n",
    "  - <a href='#default'> Train a Model using AutoMLx</a>\n",
    "  - <a href='#recommending'> Generate recommendations </a>\n",
    "  - <a href='#analyze'> Analyze the AutoMLx optimization process </a>\n",
    "      - <a href='#algorithm-selection'> Algorithm Selection</a>\n",
    "      - <a href='#hyperparameter-tuning'> Hyperparameter Tuning</a>\n",
    "  - <a href='#modellist'> Advanced AutoMLx Configuration</a>\n",
    "  - <a href='#custom-validation'> Use a custom validation set</a>\n",
    "  - <a href='#evaluation'> Final evaluation of the best model</a>\n",
    "\n",
    "<a id='setup'></a>\n",
    "## Setup\n",
    "\n",
    "Basic setup for the Notebook."
   ]
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      "[2024-09-11 08:30:41,179] [automlx.backend] Overwriting ray session directory to /tmp/kfognjfx/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "import datetime\n",
    "import logging\n",
    "import os\n",
    "import time\n",
    "import urllib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from automlx import AutoRecommender, init\n",
    "\n",
    "# Settings for plots\n",
    "plt.rcParams[\"figure.figsize\"] = [10, 7]\n",
    "plt.rcParams[\"font.size\"] = 15\n",
    "\n",
    "# Silence unnecessary warnings\n",
    "logging.getLogger(\"sanerec.autotuning.parameter\").setLevel(logging.ERROR)\n",
    "\n",
    "# Initialize the parallelization engine of AutoMLx\n",
    "init(engine='ray', engine_opts={\"ray_setup\": {\"log_to_driver\": False}})"
   ]
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    "<a id='load-data'></a>\n",
    "## Load Movielens 100k data\n",
    "Movielens 100k dataset is one of the most common public datasets for movie recommendation. It contains 100k ratings from about 1k users on 1.6k movies, some information about user demographic, and additional movie characteristics. For more information about this dataset, you can visit the [Movielens website](https://grouplens.org/datasets/movielens/100k/).\n",
    "\n",
    "In this demo, we use the ratings to train a movie recommendation model, exploiting AutoMLx to find the best recommendation model and hyperparameters to use in terms of recommendation accuracy.\n",
    "Therefore, we start retrieving and loading the ratings data of the Movielens 100k dataset.\n",
    "To make this notebook lighter and quicker, we also subsample the ratings in the dataset, keeping only 50%."
   ]
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    "\n",
    "\n",
    "get_ipython().system(' wget https://files.grouplens.org/datasets/movielens/ml-100k/u.data --no-check-certificate -q -O ./ml100k_interactions.tsv')"
   ]
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    "\n",
    "\n",
    "dataset = pd.read_csv(\n",
    "    \"./ml100k_interactions.tsv\",\n",
    "    sep=\"\\t\",\n",
    "    names=[\"user_id\", \"movie_id\", \"rating\", \"timestamp\"],\n",
    ").sample(frac=0.5, random_state=1)\n",
    "\n",
    "dataset.head(5)"
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    "In order to be used for the recommendation task, the data must have a timestamp column that is used to infer the temporal order of the samples. We also require to set the timestamp column as index of the dataframes used in our AutoML pipelines.\n",
    "\n",
    "Movielens contains a `timestamp` column that contains the time when a rating was given, so we set it as index of our dataframe."
   ]
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       "           user_id  movie_id  rating\n",
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    "\n",
    "\n",
    "dataset = dataset.set_index(\"timestamp\")\n",
    "dataset.head(5)"
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  {
   "cell_type": "markdown",
   "id": "6040cf36",
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   "source": [
    "<a id='coltypes'></a>\n",
    "### Define types of columns in the dataframe\n",
    "\n",
    "The recommendation task requires to define the two main entities involved in the recommendation:\n",
    "- the `recommendation`, which represents the entity type that is going to be recommended;\n",
    "- the `recommendation_subject`, which represents the entity type that receives the recommendation.\n",
    "\n",
    "For this reason, AutoML requires to indicate what are the columns in the dataset that refer to these two concepts, and, in particular, the two columns that contain their unique identifiers.\n",
    "\n",
    "In our demo we want to recommend movies (`recommendation`), identified by the `movie_id` column, to users (`recommendation_subject`), identified by the `user_id` column. We declare this binding in a python dictionary that we will reuse throughout the demo."
   ]
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    "\n",
    "\n",
    "col_types = {\"movie_id\": \"recommendation\", \"user_id\": \"recommendation_subject\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27cf78cd",
   "metadata": {},
   "source": [
    "<a id='dataset-split'></a>\n",
    "## Splitting the dataset\n",
    "\n",
    "We split the dataset into training and test datasets using a leave-last-out technique.\n",
    "The training set will be used to create a Machine Learning model using Oracle AutoMLx, and the test set will be used to evaluate the model's performance on unseen data.\n",
    "\n",
    "The leave-last-out splitting technique consists in keeping in the test set only the last data sample, as determined by its timestamp, for each `recommendation_subject` (user in this case). All the other samples form the training set. This corresponds to the common next item recommendation use case, where given the history of all the past data concerning a `recommendation_subject` in the training set, we want to predict what should be recommended next to the same subject, and check if it corresponds to the actual sample in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb1a978b",
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   "outputs": [],
   "source": [
    "\n",
    "\n",
    "training_data, test_data = AutoRecommender.train_test_split(data=dataset, col_types=col_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a0679d9",
   "metadata": {},
   "source": [
    "<a id='automl'></a>\n",
    "# AutoML\n",
    "\n",
    "<a id='provider'></a>\n",
    "## Create an instance of Oracle AutoMLx\n",
    "\n",
    "The Oracle AutoMLx solution provides a pipeline that automatically finds a tuned model given a prediction task and a training dataset. In particular, it allows finding a tuned model for any supervised prediction task, for example, classification or regression where the target can be binary, categorical or real-valued.\n",
    "\n",
    "In this demo we want a model that performs a recommendation task, so we create a pipeline of type `AutoRecommender`, and we configure it with default parameters. You can find the complete list of all the available parameters and their meaning in our documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9926b022",
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   "source": [
    "\n",
    "\n",
    "automl_pipeline = AutoRecommender().configure()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab73759",
   "metadata": {},
   "source": [
    "<a id='default'></a>\n",
    "## Train a model using AutoMLx\n",
    "\n",
    "The training data is passed to the `fit()` function which executes the model selection and hyperparameter tuning steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:30:50,830] [automlx.interface] Dataset shape: (49055,3)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:30:50,900] [automlx.process] Running Model Generation\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:30:50,943] [automlx.process] Model Generation completed.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:30:50,987] [automlx.model_selection] Running Model Selection\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:24,607] [automlx.model_selection] Model Selection completed - Took 33.620 sec - Selected models: [['ItemKNNRecommender']]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:24,649] [automlx.trials] Running Model Tuning for ['ItemKNNRecommender']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:39,575] [automlx.trials] Best parameters for ItemKNNRecommender: {'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:39,576] [automlx.trials] Model Tuning completed. Took: 14.926 secs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:40,082] [automlx.interface] Re-fitting pipeline\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:40,092] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_e945b10c-f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:41,009] [automlx.interface] AutoMLx completed.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "automl_pipeline = automl_pipeline.fit(data=training_data, col_types=col_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fece1a03",
   "metadata": {},
   "source": [
    "<a id='recommending'></a>\n",
    "## Generate recommendations\n",
    "\n",
    "Once the AutoML pipeline is completed, we predict 5 recommendations for a random user in the dataset."
   ]
  },
  {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>834</td>\n",
       "      <td>121</td>\n",
       "      <td>27.871420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>834</td>\n",
       "      <td>7</td>\n",
       "      <td>27.642850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>834</td>\n",
       "      <td>50</td>\n",
       "      <td>27.107165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>834</td>\n",
       "      <td>405</td>\n",
       "      <td>26.306330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>834</td>\n",
       "      <td>181</td>\n",
       "      <td>24.635577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id      score\n",
       "0      834       121  27.871420\n",
       "1      834         7  27.642850\n",
       "2      834        50  27.107165\n",
       "3      834       405  26.306330\n",
       "4      834       181  24.635577"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "recommendation_subjects = test_data.sample(1)[['user_id']]\n",
    "automl_pipeline.predict(subjects=recommendation_subjects, n_recommendations=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05487f45",
   "metadata": {},
   "source": [
    "<a id='analyze'></a>\n",
    "## Analyze the AutoMLx optimization process\n",
    "\n",
    "During the Oracle AutoMLx process for recommendation, a summary of the optimization process is logged, containing:\n",
    "- Information about the training data.\n",
    "- Information about the AutoMLx Pipeline, such as:\n",
    "    - Selected algorithm that was the best choice for this data;\n",
    "    - Selected hyperparameters for the selected algorithm.\n",
    "\n",
    "AutoMLx provides a `print_summary` API to output all the different trials performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb434bd0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-11T15:31:41.193728Z",
     "iopub.status.busy": "2024-09-11T15:31:41.193533Z",
     "iopub.status.idle": "2024-09-11T15:31:41.208294Z",
     "shell.execute_reply": "2024-09-11T15:31:41.207696Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<br><div style='text-align: center; font-weight: bold; font-size: 16px;'>General Summary</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>(48114, 4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(941, 4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ManualSplit(Shuffle=False, Seed=7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SanerecMetric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24.3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3.8.19 (default, Mar 20 2024, 19:58:24) \\n[GCC 11.2.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<br><div style='text-align: center; font-weight: bold; font-size: 16px;'>Trials Summary</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Step</th>\n",
       "      <th># Samples</th>\n",
       "      <th># Features</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Hyperparameters</th>\n",
       "      <th>Score (SanerecMetric)</th>\n",
       "      <th>Runtime (Seconds)</th>\n",
       "      <th>Memory Usage (GB)</th>\n",
       "      <th>Finished</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Model Selection</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 100, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0882</td>\n",
       "      <td>1.1723</td>\n",
       "      <td>0.7172</td>\n",
       "      <td>Wed Sep 11 08:31:01 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Selection</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>AlsRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'iterations': 10, 'factors': 16, 'regularization': 0.01, 'cache_users_states': True}</td>\n",
       "      <td>0.0723</td>\n",
       "      <td>3.8354</td>\n",
       "      <td>0.7012</td>\n",
       "      <td>Wed Sep 11 08:31:00 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Selection</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>TRexxRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'embedding_dim': 32, 'sequence_length': 5, 'num_sampled': 100, 'dropout_rate': 0.2, 'num_blocks': 2, 'num_head': 4, 'l2_reg_embedding': 1e-06, 'dnn_activation': 'tanh', 'optimizer_name': 'lazyadam', 'optimizer_learning_rate': 0.01, 'future_blinding': False, 'embeddings_on_cpu': False, 'cache_users_states': False, 'negative_sampling_method': CandidateSamplingMethod.UNIFORM_CANDIDATE_SAMPLING, 'epochs': 10, 'batch_size': 512, 'verbose': 1, 'augment_data': True, 'early_stopping_patience': -1}</td>\n",
       "      <td>0.0542</td>\n",
       "      <td>22.7016</td>\n",
       "      <td>1.1490</td>\n",
       "      <td>Wed Sep 11 08:31:24 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Selection</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>BprRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'iterations': 10, 'factors': 16, 'regularization': 0.01, 'cache_users_states': True}</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.7042</td>\n",
       "      <td>Wed Sep 11 08:31:00 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 505, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>1.4283</td>\n",
       "      <td>0.6178</td>\n",
       "      <td>Wed Sep 11 08:31:36 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>1.1595</td>\n",
       "      <td>0.6175</td>\n",
       "      <td>Wed Sep 11 08:31:38 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>1.6110</td>\n",
       "      <td>0.6170</td>\n",
       "      <td>Wed Sep 11 08:31:36 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 28.25660795027468, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0956</td>\n",
       "      <td>1.5365</td>\n",
       "      <td>0.6080</td>\n",
       "      <td>Wed Sep 11 08:31:34 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 28.26160794927468, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0956</td>\n",
       "      <td>1.2769</td>\n",
       "      <td>0.6095</td>\n",
       "      <td>Wed Sep 11 08:31:36 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 28.26160794927468, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0956</td>\n",
       "      <td>1.5252</td>\n",
       "      <td>0.6092</td>\n",
       "      <td>Wed Sep 11 08:31:34 2024</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.084</td>\n",
       "      <td>1.5141</td>\n",
       "      <td>0.6093</td>\n",
       "      <td>Wed Sep 11 08:31:34 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 21, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.084</td>\n",
       "      <td>1.5630</td>\n",
       "      <td>0.6066</td>\n",
       "      <td>Wed Sep 11 08:31:34 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 505, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0829</td>\n",
       "      <td>1.4142</td>\n",
       "      <td>1.1422</td>\n",
       "      <td>Wed Sep 11 08:31:30 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0829</td>\n",
       "      <td>1.4687</td>\n",
       "      <td>1.1422</td>\n",
       "      <td>Wed Sep 11 08:31:31 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 752, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0818</td>\n",
       "      <td>1.8498</td>\n",
       "      <td>1.1450</td>\n",
       "      <td>Wed Sep 11 08:31:33 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 753, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0818</td>\n",
       "      <td>1.5882</td>\n",
       "      <td>0.6187</td>\n",
       "      <td>Wed Sep 11 08:31:34 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 132, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0797</td>\n",
       "      <td>1.5755</td>\n",
       "      <td>0.6110</td>\n",
       "      <td>Wed Sep 11 08:31:34 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 133, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0797</td>\n",
       "      <td>1.5003</td>\n",
       "      <td>0.6096</td>\n",
       "      <td>Wed Sep 11 08:31:34 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 255, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0797</td>\n",
       "      <td>1.6541</td>\n",
       "      <td>1.1450</td>\n",
       "      <td>Wed Sep 11 08:31:35 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Model Tuning</td>\n",
       "      <td>48114</td>\n",
       "      <td>2</td>\n",
       "      <td>ItemKNNRecommender</td>\n",
       "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 256, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
       "      <td>0.0797</td>\n",
       "      <td>1.3009</td>\n",
       "      <td>0.6101</td>\n",
       "      <td>Wed Sep 11 08:31:35 2024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "automl_pipeline.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd85adfd",
   "metadata": {},
   "source": [
    "We also provide the capability to visualize the results of each stage of the AutoMLx pipeline.\n",
    "\n",
    "<a id='algorithm-selection'></a>\n",
    "### Algorithm Selection\n",
    "\n",
    "The plot below shows the scores predicted by Algorithm Selection for each algorithm. The horizontal line shows the average score across all algorithms. Algorithms below the line are colored turquoise, whereas those with a score higher than the mean are colored teal. The selected algorithm is in orange."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e72a8385",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-09-11T15:31:41.438976Z"
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   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "def plot_model_selection_scores(_pipeline):\n",
    "    # Each trial is a row in a dataframe that contains\n",
    "    # Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features\n",
    "    trials = _pipeline.completed_trials_summary_[\n",
    "        _pipeline.completed_trials_summary_[\"Step\"].str.contains(\"Model Selection\")\n",
    "    ]\n",
    "    name_of_score_column = f\"Score ({_pipeline._inferred_score_metric[0].name})\"\n",
    "    trials.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "    trials.dropna(subset=[name_of_score_column], inplace=True)\n",
    "    scores = trials[name_of_score_column].tolist()\n",
    "    models = trials[\"Algorithm\"].tolist()\n",
    "    \n",
    "    y_margin = 0.10 * (max(scores) - min(scores))\n",
    "    s = pd.Series(scores, index=models).sort_values(ascending=False)\n",
    "    \n",
    "    colors = []\n",
    "    for f in s.keys():\n",
    "        if f.strip() == _pipeline.selected_model_.strip():\n",
    "            colors.append(\"orange\")\n",
    "        elif s[f] >= s.mean():\n",
    "            colors.append(\"teal\")\n",
    "        else:\n",
    "            colors.append(\"turquoise\")\n",
    "    \n",
    "    fig, ax = plt.subplots(1)\n",
    "    ax.set_title(\"Algorithm Selection Trials\")\n",
    "    ax.set_ylim(min(scores) - y_margin, max(scores) + y_margin)\n",
    "    ax.set_ylabel(\"Hit Rate\")\n",
    "    s.plot.bar(ax=ax, color=colors, edgecolor=\"black\")\n",
    "    ax.axhline(y=s.mean(), color=\"black\", linewidth=0.5)\n",
    "    plt.show()\n",
    "\n",
    "plot_model_selection_scores(automl_pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e218bbd",
   "metadata": {},
   "source": [
    "<a id='hyperparameter-tuning'></a>\n",
    "### Hyperparameter Tuning\n",
    "\n",
    "Hyperparameter Tuning is the last stage of the AutoMLx pipeline, and focuses on improving the chosen algorithm's score on the dataset. We use a novel iterative algorithm to search across many hyperparameter dimensions, and converge automatically when optimal hyperparameters are identified. Each trial represents a particular hyperparameter configuration for the selected model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7efa75d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-11T15:31:41.442045Z",
     "iopub.status.busy": "2024-09-11T15:31:41.441659Z",
     "iopub.status.idle": "2024-09-11T15:31:41.661218Z",
     "shell.execute_reply": "2024-09-11T15:31:41.660517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "def plot_hp_tuning_scores(_pipeline):\n",
    "    # Each trial is a row in a dataframe that contains\n",
    "    # Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features\n",
    "    trials = _pipeline.completed_trials_summary_[\n",
    "        _pipeline.completed_trials_summary_[\"Step\"].str.contains(\"Model Tuning\")\n",
    "    ]\n",
    "    name_of_score_column = f\"Score ({_pipeline._inferred_score_metric[0].name})\"\n",
    "    trials.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "    trials.dropna(subset=[name_of_score_column], inplace=True)\n",
    "    trials.drop(trials[trials[\"Finished\"] == -1].index, inplace=True)\n",
    "    trials[\"Finished\"] = trials[\"Finished\"].apply(\n",
    "        lambda x: time.mktime(datetime.datetime.strptime(x, \"%a %b %d %H:%M:%S %Y\").timetuple())\n",
    "    )\n",
    "    trials.sort_values(by=[\"Finished\"], ascending=True, inplace=True)\n",
    "    scores = trials[name_of_score_column].tolist()\n",
    "    score = []\n",
    "    score.append(scores[0])\n",
    "    for i in range(1, len(scores)):\n",
    "        if scores[i] >= score[i - 1]:\n",
    "            score.append(scores[i])\n",
    "        else:\n",
    "            score.append(score[i - 1])\n",
    "    y_margin = 0.10 * (max(score) - min(score))\n",
    "    fig, ax = plt.subplots(1)\n",
    "    ax.set_title(\"Hyperparameter Tuning Trials\")\n",
    "    ax.set_xlabel(\"Iteration $n$\")\n",
    "    ax.set_ylabel(\"Hit Rate\")\n",
    "    ax.grid(color=\"g\", linestyle=\"-\", linewidth=0.1)\n",
    "    ax.set_ylim(min(score) - y_margin, max(score) + y_margin)\n",
    "    ax.plot(range(1, len(trials) + 1), score, \"k:\", marker=\"s\", color=\"teal\", markersize=3)\n",
    "    plt.show()\n",
    "\n",
    "plot_hp_tuning_scores(automl_pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c01574",
   "metadata": {},
   "source": [
    "<a id='modellist'></a>\n",
    "## Advanced AutoMLx Configuration\n",
    "\n",
    "You can also configure the AutoRecommender pipeline with suitable parameters according to your needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89bf115e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-11T15:31:41.663689Z",
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   "source": [
    "\n",
    "\n",
    "custom_pipeline = AutoRecommender().configure(\n",
    "    model_list=[  # Specify the models you want the AutoMLx to consider\n",
    "        \"ItemKNNRecommender\",\n",
    "        \"AlsRecommender\",\n",
    "        \"BprRecommender\",\n",
    "    ],\n",
    "    n_algos_tuned=2,  # Choose how many models to tune\n",
    "    search_space={  # You can specify the hyperparameters and ranges we search for each model\n",
    "        \"ItemKNNRecommender\": {\"num_of_neighbors\": {\"range\": [10, 30], \"type\": \"continuous\"}}\n",
    "    },\n",
    "    max_tuning_trials=20,  # The maximum number of tuning trials. Can be integer or Dict (max number for each model)\n",
    "    score_metric=\"recall\",  # Any of the metrics available, see the documentation for a list of supported values\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d926ee4a",
   "metadata": {},
   "source": [
    "<a id='custom-validation'></a>\n",
    "## Use a custom validation set\n",
    "\n",
    "You can specify a custom validation set that you want AutoMLx to use to evaluate the quality of models and configurations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5aef060",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-11T15:31:41.671728Z",
     "iopub.status.busy": "2024-09-11T15:31:41.671387Z",
     "iopub.status.idle": "2024-09-11T15:31:41.848551Z",
     "shell.execute_reply": "2024-09-11T15:31:41.847876Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "training_data, validation_data = AutoRecommender.train_test_split(data=training_data, col_types=col_types)\n",
    "\n",
    "\n",
    "# We run again the AutoML pipeline with the custom training/validation split we just created, and some advanced settings that we can specify directly in the fit method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eaf9d445",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-11T15:31:41.851125Z",
     "iopub.status.busy": "2024-09-11T15:31:41.850753Z",
     "iopub.status.idle": "2024-09-11T15:31:53.850619Z",
     "shell.execute_reply": "2024-09-11T15:31:53.849865Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:42,012] [automlx.interface] Dataset shape: (49055,3)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:42,080] [automlx.process] Running Model Generation\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:42,128] [automlx.process] Model Generation completed.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:42,160] [automlx.model_selection] Running Model Selection\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:43,815] [automlx.model_selection] Model Selection completed - Took 1.654 sec - Selected models: [['ItemKNNRecommender', 'AlsRecommender']]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:43,923] [automlx.trials] Running Model Tuning for ['ItemKNNRecommender']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:47,504] [automlx.trials] Best parameters for ItemKNNRecommender: {'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.010099998000000002, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:47,506] [automlx.trials] Model Tuning completed. Took: 3.583 secs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:47,640] [automlx.trials] Running Model Tuning for ['AlsRecommender']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:51,820] [automlx.trials] Best parameters for AlsRecommender: {'n_recommendations': 10, 'iterations': 10, 'factors': 16, 'regularization': 0.00044721247746457157, 'cache_users_states': True}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:51,821] [automlx.trials] Model Tuning completed. Took: 4.181 secs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:52,277] [automlx.interface] Re-fitting pipeline\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:52,287] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_06ccaa75-4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-09-11 08:31:53,683] [automlx.interface] AutoMLx completed.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "custom_pipeline = custom_pipeline.fit(\n",
    "    training_data,\n",
    "    col_types,\n",
    "    validation_data,\n",
    "    time_budget=20,  # Specify time budget in seconds\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a30a757",
   "metadata": {},
   "source": [
    "Now that the custom AutoML pipeline is completed, we can generate recommendations.\n",
    "Note that the pipeline's `recommend` method is equivalent to `predict`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2f759ce2",
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    "\n",
    "\n",
    "custom_pipeline.recommend(subjects=recommendation_subjects, n_recommendations=5)"
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  {
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    "<a id='evaluation'></a>\n",
    "## Final evaluation of the best model\n",
    "\n",
    "Finally, we evaluate the best model found on the test data we have. If no metric is specified, the pipeline computes the score using the same metric used to run the Hyperparameter Tuning, which in this case is the Recall, as we defined at pipeline creation.\n",
    "\n",
    "In this example, instead, we ask the pipeline to perform the evaluation using Normalized Discounted Cumulative Gain (NDCG), a common ranking metric. Our online documentation provides the list of the available metrics and how they are computed."
   ]
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