04 - IALS_Recommender.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "1fe752be",
   "metadata": {},
   "source": [
    "# IALS Recommender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84543c01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_kaggle(run_on_kaggle, secret_name):\n",
    "    if run_on_kaggle:\n",
    "        from kaggle_secrets import UserSecretsClient\n",
    "        user_secrets = UserSecretsClient()\n",
    "        personal_token = user_secrets.get_secret(secret_name)\n",
    "        \n",
    "        import subprocess\n",
    "        subprocess.run([\"git\", \"clone\", \"https://\" + personal_token + \"@github.com/alecontuIT/rec_sys_challenge2022.git\"])\n",
    "        \n",
    "        import os\n",
    "        os.chdir(\"./rec_sys_challenge2022\")\n",
    "        \n",
    "        subprocess.run([\"pip\", \"install\", \"-r\", \"requirements.txt\"])\n",
    "        return True\n",
    "    \n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfde2b95",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_version = \"interactions-all-ones\"\n",
    "kaggle = init_kaggle(False, \"recsys_git_token\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29bf2ac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils\n",
    "from recmodels import IALSRec\n",
    "from scipy.stats import loguniform\n",
    "from Evaluation.Evaluator import EvaluatorHoldout\n",
    "from skopt.space import Real, Integer, Categorical\n",
    "from HyperparameterTuning.SearchAbstractClass import SearchInputRecommenderArgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b33aca83",
   "metadata": {},
   "outputs": [],
   "source": [
    "recommender_class = IALSRec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff623f25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "URM_all, URM_train, URM_val, ICM = utils.get_data_global_sample(dataset_version=dataset_version, \n",
    "                                                                train_percentage=0.7,\n",
    "                                                                setSeed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49f8760f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "evaluator_validation = EvaluatorHoldout(URM_val, cutoff_list=[10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f42a74c1",
   "metadata": {},
   "source": [
    "## Hyperparameter Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11eed6fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparameters_range_dictionary = {\n",
    "    \"epochs\": Categorical([300]),\n",
    "    \"num_factors\" : Integer(1, 200),\n",
    "    \"confidence_scaling\" : Categorical([\"linear\", \"log\"]),\n",
    "    \"alpha\": Real(low = 1e-3, high = 50.0, prior = 'log-uniform'),\n",
    "    \"epsilon\": Real(low = 1e-3, high = 10.0, prior = 'log-uniform'),\n",
    "    \"reg\": Real(low = 1e-5, high = 1e-2, prior = 'log-uniform')\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e4a2b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystopping_keywargs = {\n",
    "    \"validation_every_n\": 5,\n",
    "    \"stop_on_validation\": True,\n",
    "    \"evaluator_object\": evaluator_validation,\n",
    "    \"lower_validations_allowed\": 5,\n",
    "    \"validation_metric\": \"MAP\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb951489",
   "metadata": {},
   "outputs": [],
   "source": [
    "recommender_input_args = SearchInputRecommenderArgs(\n",
    "    CONSTRUCTOR_POSITIONAL_ARGS = [URM_train],\n",
    "    CONSTRUCTOR_KEYWORD_ARGS = {},\n",
    "    FIT_POSITIONAL_ARGS = [],\n",
    "    FIT_KEYWORD_ARGS = {},\n",
    "    EARLYSTOPPING_KEYWORD_ARGS = earlystopping_keywargs,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e3fbd2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "utils.bayesian_search(\n",
    "    recommender_class, \n",
    "    recommender_input_args, \n",
    "    hyperparameters_range_dictionary, \n",
    "    evaluator_validation,\n",
    "    dataset_version=dataset_version,\n",
    "    n_cases=70,\n",
    "    perc_random_starts=0.3,\n",
    "    resume_from_saved=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dac1b16",
   "metadata": {},
   "source": [
    "## Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01f09c0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from HyperparameterTuning.SearchSingleCase import SearchSingleCase\n",
    "output_folder_path = utils.get_hyperparams_search_output_folder(recommender_class, dataset_version=dataset_version)\n",
    "   \n",
    "fit_hyperparams = {\"epochs\": 250, \"num_factors\": 59, \"confidence_scaling\": \"linear\", \"alpha\": 5.8267337801306525, \"epsilon\": 0.818204727509452, \"reg\": 3.6211485102456735e-05}\n",
    "\n",
    "recommender_input_args = SearchInputRecommenderArgs(\n",
    "    CONSTRUCTOR_POSITIONAL_ARGS = [URM_train],    \n",
    "    CONSTRUCTOR_KEYWORD_ARGS = {},\n",
    "    FIT_POSITIONAL_ARGS = [],\n",
    "    FIT_KEYWORD_ARGS = {},\n",
    "    EARLYSTOPPING_KEYWORD_ARGS = earlystopping_keywargs, \n",
    ")\n",
    "urm = recommender_input_args.CONSTRUCTOR_POSITIONAL_ARGS[0]\n",
    "hyperparameterSearch = SearchSingleCase(recommender_class, evaluator_validation=evaluator_validation)\n",
    "hyperparameterSearch.search(recommender_input_args,\n",
    "                                   fit_hyperparameters_values=fit_hyperparams,\n",
    "                                   metric_to_optimize = \"MAP\",\n",
    "                                   cutoff_to_optimize = 10,\n",
    "                                   output_folder_path = output_folder_path,\n",
    "                                   output_file_name_root = recommender_class.RECOMMENDER_NAME,\n",
    "                                   resume_from_saved = False,\n",
    "                                   save_model = \"best\"\n",
    "                                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df4f90b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "recommender = utils.fit_best_recommender(recommender_class, URM_all, dataset_version)\n",
    "#utils.submission(recommender, dataset_version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3d8c48c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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