{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of model selection using cross-validation with RecTools\n",
    "\n",
    "- CV split\n",
    "- Training a variety of models\n",
    "- Measuring a variety of metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from implicit.nearest_neighbours import TFIDFRecommender, BM25Recommender\n",
    "from implicit.als import AlternatingLeastSquares\n",
    "\n",
    "from rectools import Columns\n",
    "from rectools.dataset import Dataset\n",
    "from rectools.metrics import Precision, Recall, MeanInvUserFreq, Serendipity, calc_metrics\n",
    "from rectools.models import ImplicitItemKNNWrapperModel, RandomModel, PopularModel\n",
    "from rectools.model_selection import TimeRangeSplitter, cross_validate\n",
    "from rectools.visuals import MetricsApp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-05T13:55:02.641036Z",
     "iopub.status.busy": "2022-12-05T13:55:02.641036Z",
     "iopub.status.idle": "2022-12-05T13:55:04.735725Z",
     "shell.execute_reply": "2022-12-05T13:55:04.734722Z",
     "shell.execute_reply.started": "2022-12-05T13:55:02.641036Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  ml-1m.zip\n",
      "  inflating: ml-1m/movies.dat        \n",
      "  inflating: ml-1m/ratings.dat       \n",
      "  inflating: ml-1m/README            \n",
      "  inflating: ml-1m/users.dat         \n",
      "CPU times: user 144 ms, sys: 49.7 ms, total: 194 ms\n",
      "Wall time: 4.35 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "!wget -q https://files.grouplens.org/datasets/movielens/ml-1m.zip -O ml-1m.zip\n",
    "!unzip -o ml-1m.zip\n",
    "!rm ml-1m.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-05T13:55:04.738725Z",
     "iopub.status.busy": "2022-12-05T13:55:04.737727Z",
     "iopub.status.idle": "2022-12-05T13:55:09.954460Z",
     "shell.execute_reply": "2022-12-05T13:55:09.953459Z",
     "shell.execute_reply.started": "2022-12-05T13:55:04.738725Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000209, 4)\n",
      "CPU times: user 7.42 s, sys: 222 ms, total: 7.64 s\n",
      "Wall time: 7.67 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>item_id</th>\n",
       "      <th>weight</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
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       "    <tr>\n",
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       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  weight   datetime\n",
       "0        1     1193       5  978300760\n",
       "1        1      661       3  978302109\n",
       "2        1      914       3  978301968\n",
       "3        1     3408       4  978300275\n",
       "4        1     2355       5  978824291"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "ratings = pd.read_csv(\n",
    "    \"ml-1m/ratings.dat\",\n",
    "    sep=\"::\",\n",
    "    engine=\"python\",  # Because of 2-chars separators\n",
    "    header=None,\n",
    "    names=[Columns.User, Columns.Item, Columns.Weight, Columns.Datetime],\n",
    ")\n",
    "print(ratings.shape)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-05T13:55:09.955463Z",
     "iopub.status.busy": "2022-12-05T13:55:09.954460Z",
     "iopub.status.idle": "2022-12-05T13:55:09.985460Z",
     "shell.execute_reply": "2022-12-05T13:55:09.984462Z",
     "shell.execute_reply.started": "2022-12-05T13:55:09.955463Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6040, 3706)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[\"user_id\"].nunique(), ratings[\"item_id\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-05T13:55:09.987463Z",
     "iopub.status.busy": "2022-12-05T13:55:09.986461Z",
     "iopub.status.idle": "2022-12-05T13:55:10.000460Z",
     "shell.execute_reply": "2022-12-05T13:55:10.000460Z",
     "shell.execute_reply.started": "2022-12-05T13:55:09.987463Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weight\n",
       "4    348971\n",
       "3    261197\n",
       "5    226310\n",
       "2    107557\n",
       "1     56174\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[\"weight\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-05T13:55:10.002461Z",
     "iopub.status.busy": "2022-12-05T13:55:10.001460Z",
     "iopub.status.idle": "2022-12-05T13:55:10.175459Z",
     "shell.execute_reply": "2022-12-05T13:55:10.174460Z",
     "shell.execute_reply.started": "2022-12-05T13:55:10.002461Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time period\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Timestamp('2000-04-25 23:05:32'), Timestamp('2003-02-28 17:49:50'))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[\"datetime\"] = pd.to_datetime(ratings[\"datetime\"] * 10 ** 9)\n",
    "print(\"Time period\")\n",
    "ratings[\"datetime\"].min(), ratings[\"datetime\"].max()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create `Dataset` class. It's a wrapper for interactions. User and item features can also be added (see next examples for details)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-05T13:55:10.176459Z",
     "iopub.status.busy": "2022-12-05T13:55:10.176459Z",
     "iopub.status.idle": "2022-12-05T13:55:10.207463Z",
     "shell.execute_reply": "2022-12-05T13:55:10.206460Z",
     "shell.execute_reply.started": "2022-12-05T13:55:10.176459Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 78.5 ms, sys: 3.88 ms, total: 82.4 ms\n",
      "Wall time: 90.2 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dataset = Dataset.construct(ratings)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare cross-validation splitter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use last 3 periods of 2 weeks to validate our models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_splits = 3\n",
    "\n",
    "splitter = TimeRangeSplitter(\n",
    "    test_size=\"14D\",\n",
    "    n_splits=n_splits,\n",
    "    filter_already_seen=True,\n",
    "    filter_cold_items=True,\n",
    "    filter_cold_users=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(Timestamp('2003-01-18 00:00:00'), Timestamp('2003-02-01 00:00:00')),\n",
       " (Timestamp('2003-02-01 00:00:00'), Timestamp('2003-02-15 00:00:00')),\n",
       " (Timestamp('2003-02-15 00:00:00'), Timestamp('2003-03-01 00:00:00'))]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitter.get_test_fold_borders(dataset.interactions)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For test folds left border is always included in fold and the right one is excluded.\n",
    "\n",
    "Train folds don't have left border, and the right one is always excluded."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-05T13:55:10.240459Z",
     "iopub.status.busy": "2022-12-05T13:55:10.240459Z",
     "iopub.status.idle": "2022-12-05T13:55:10.255462Z",
     "shell.execute_reply": "2022-12-05T13:55:10.254459Z",
     "shell.execute_reply.started": "2022-12-05T13:55:10.240459Z"
    }
   },
   "outputs": [],
   "source": [
    "# Take few simple models to compare\n",
    "models = {\n",
    "    \"random\": RandomModel(random_state=42),\n",
    "    \"popular\": PopularModel(),\n",
    "    \"most_rated\": PopularModel(popularity=\"sum_weight\"),\n",
    "    \"tfidf_k=5\": ImplicitItemKNNWrapperModel(model=TFIDFRecommender(K=5)),\n",
    "    \"tfidf_k=10\": ImplicitItemKNNWrapperModel(model=TFIDFRecommender(K=10)),\n",
    "    \"bm25_k=10_k1=0.05_b=0.1\": ImplicitItemKNNWrapperModel(model=BM25Recommender(K=5, K1=0.05, B=0.1)),\n",
    "}\n",
    "\n",
    "# We will calculate several classic (precision@k and recall@k) and \"beyond accuracy\" metrics\n",
    "metrics = {\n",
    "    \"prec@1\": Precision(k=1),\n",
    "    \"prec@10\": Precision(k=10),\n",
    "    \"recall@10\": Recall(k=10),\n",
    "    \"novelty@10\": MeanInvUserFreq(k=10),\n",
    "    \"serendipity@10\": Serendipity(k=10),\n",
    "}\n",
    "\n",
    "K_RECS = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-05T13:55:10.256554Z",
     "iopub.status.busy": "2022-12-05T13:55:10.256554Z",
     "iopub.status.idle": "2022-12-05T13:55:18.438419Z",
     "shell.execute_reply": "2022-12-05T13:55:18.437419Z",
     "shell.execute_reply.started": "2022-12-05T13:55:10.256554Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 27 s, sys: 208 ms, total: 27.2 s\n",
      "Wall time: 14.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# For each fold generate train and test part of dataset\n",
    "# Then fit every model, generate recommendations and calculate metrics\n",
    "\n",
    "cv_results = cross_validate(\n",
    "    dataset=dataset,\n",
    "    splitter=splitter,\n",
    "    models=models,\n",
    "    metrics=metrics,\n",
    "    k=K_RECS,\n",
    "    filter_viewed=True,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get some split stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i_split</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>train</th>\n",
       "      <th>train_users</th>\n",
       "      <th>train_items</th>\n",
       "      <th>test</th>\n",
       "      <th>test_users</th>\n",
       "      <th>test_items</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2003-01-18</td>\n",
       "      <td>2003-02-01</td>\n",
       "      <td>998083</td>\n",
       "      <td>6040</td>\n",
       "      <td>3706</td>\n",
       "      <td>630</td>\n",
       "      <td>75</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2003-02-01</td>\n",
       "      <td>2003-02-15</td>\n",
       "      <td>998713</td>\n",
       "      <td>6040</td>\n",
       "      <td>3706</td>\n",
       "      <td>899</td>\n",
       "      <td>57</td>\n",
       "      <td>704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2003-02-15</td>\n",
       "      <td>2003-03-01</td>\n",
       "      <td>999612</td>\n",
       "      <td>6040</td>\n",
       "      <td>3706</td>\n",
       "      <td>597</td>\n",
       "      <td>66</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   i_split      start        end   train  train_users  train_items  test  \\\n",
       "0        0 2003-01-18 2003-02-01  998083         6040         3706   630   \n",
       "1        1 2003-02-01 2003-02-15  998713         6040         3706   899   \n",
       "2        2 2003-02-15 2003-03-01  999612         6040         3706   597   \n",
       "\n",
       "   test_users  test_items  \n",
       "0          75         540  \n",
       "1          57         704  \n",
       "2          66         501  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(cv_results[\"splits\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the main result is metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>i_split</th>\n",
       "      <th>prec@1</th>\n",
       "      <th>prec@10</th>\n",
       "      <th>recall@10</th>\n",
       "      <th>novelty@10</th>\n",
       "      <th>serendipity@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>random</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002667</td>\n",
       "      <td>0.000832</td>\n",
       "      <td>6.520987</td>\n",
       "      <td>0.000422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>popular</td>\n",
       "      <td>0</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.037410</td>\n",
       "      <td>1.580736</td>\n",
       "      <td>0.000123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>most_rated</td>\n",
       "      <td>0</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.026667</td>\n",
       "      <td>0.042251</td>\n",
       "      <td>1.592543</td>\n",
       "      <td>0.000151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tfidf_k=5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.021333</td>\n",
       "      <td>0.023866</td>\n",
       "      <td>2.361189</td>\n",
       "      <td>0.000465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tfidf_k=10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.026667</td>\n",
       "      <td>0.021333</td>\n",
       "      <td>0.039926</td>\n",
       "      <td>2.137451</td>\n",
       "      <td>0.000327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bm25_k=10_k1=0.05_b=0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.026667</td>\n",
       "      <td>0.029333</td>\n",
       "      <td>0.046645</td>\n",
       "      <td>1.781881</td>\n",
       "      <td>0.000271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>random</td>\n",
       "      <td>1</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.005263</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>6.490783</td>\n",
       "      <td>0.000605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>popular</td>\n",
       "      <td>1</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.057895</td>\n",
       "      <td>0.015707</td>\n",
       "      <td>1.588414</td>\n",
       "      <td>0.000183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>most_rated</td>\n",
       "      <td>1</td>\n",
       "      <td>0.035088</td>\n",
       "      <td>0.056140</td>\n",
       "      <td>0.009919</td>\n",
       "      <td>1.600628</td>\n",
       "      <td>0.000155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tfidf_k=5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.057895</td>\n",
       "      <td>0.048591</td>\n",
       "      <td>2.326116</td>\n",
       "      <td>0.002616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>tfidf_k=10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.010033</td>\n",
       "      <td>2.143504</td>\n",
       "      <td>0.000917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bm25_k=10_k1=0.05_b=0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>0.059649</td>\n",
       "      <td>0.010321</td>\n",
       "      <td>1.809416</td>\n",
       "      <td>0.000341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>random</td>\n",
       "      <td>2</td>\n",
       "      <td>0.015152</td>\n",
       "      <td>0.003030</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>6.336690</td>\n",
       "      <td>0.000579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>popular</td>\n",
       "      <td>2</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.042424</td>\n",
       "      <td>0.039984</td>\n",
       "      <td>1.656638</td>\n",
       "      <td>0.000450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>most_rated</td>\n",
       "      <td>2</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.039394</td>\n",
       "      <td>0.024521</td>\n",
       "      <td>1.668108</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>tfidf_k=5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.039606</td>\n",
       "      <td>2.378988</td>\n",
       "      <td>0.001500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>tfidf_k=10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.051515</td>\n",
       "      <td>0.053531</td>\n",
       "      <td>2.206921</td>\n",
       "      <td>0.001346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>bm25_k=10_k1=0.05_b=0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.039394</td>\n",
       "      <td>0.038426</td>\n",
       "      <td>1.901316</td>\n",
       "      <td>0.000397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      model  i_split    prec@1   prec@10  recall@10  \\\n",
       "0                    random        0  0.000000  0.002667   0.000832   \n",
       "1                   popular        0  0.053333  0.024000   0.037410   \n",
       "2                most_rated        0  0.053333  0.026667   0.042251   \n",
       "3                 tfidf_k=5        0  0.053333  0.021333   0.023866   \n",
       "4                tfidf_k=10        0  0.026667  0.021333   0.039926   \n",
       "5   bm25_k=10_k1=0.05_b=0.1        0  0.026667  0.029333   0.046645   \n",
       "6                    random        1  0.017544  0.005263   0.000732   \n",
       "7                   popular        1  0.052632  0.057895   0.015707   \n",
       "8                most_rated        1  0.035088  0.056140   0.009919   \n",
       "9                 tfidf_k=5        1  0.052632  0.057895   0.048591   \n",
       "10               tfidf_k=10        1  0.052632  0.052632   0.010033   \n",
       "11  bm25_k=10_k1=0.05_b=0.1        1  0.070175  0.059649   0.010321   \n",
       "12                   random        2  0.015152  0.003030   0.000877   \n",
       "13                  popular        2  0.045455  0.042424   0.039984   \n",
       "14               most_rated        2  0.045455  0.039394   0.024521   \n",
       "15                tfidf_k=5        2  0.090909  0.050000   0.039606   \n",
       "16               tfidf_k=10        2  0.060606  0.051515   0.053531   \n",
       "17  bm25_k=10_k1=0.05_b=0.1        2  0.090909  0.039394   0.038426   \n",
       "\n",
       "    novelty@10  serendipity@10  \n",
       "0     6.520987        0.000422  \n",
       "1     1.580736        0.000123  \n",
       "2     1.592543        0.000151  \n",
       "3     2.361189        0.000465  \n",
       "4     2.137451        0.000327  \n",
       "5     1.781881        0.000271  \n",
       "6     6.490783        0.000605  \n",
       "7     1.588414        0.000183  \n",
       "8     1.600628        0.000155  \n",
       "9     2.326116        0.002616  \n",
       "10    2.143504        0.000917  \n",
       "11    1.809416        0.000341  \n",
       "12    6.336690        0.000579  \n",
       "13    1.656638        0.000450  \n",
       "14    1.668108        0.000332  \n",
       "15    2.378988        0.001500  \n",
       "16    2.206921        0.001346  \n",
       "17    1.901316        0.000397  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(cv_results[\"metrics\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now aggregate metrics by folds and compare models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4c571_row0_col0, #T_4c571_row0_col1, #T_4c571_row0_col2, #T_4c571_row1_col3, #T_4c571_row2_col4 {\n",
       "  background-color: lightcoral;\n",
       "}\n",
       "#T_4c571_row0_col3, #T_4c571_row3_col0, #T_4c571_row3_col1, #T_4c571_row3_col2, #T_4c571_row3_col4 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4c571\">\n",
       "  <caption>Mean values of metrics</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4c571_level0_col0\" class=\"col_heading level0 col0\" >prec@1</th>\n",
       "      <th id=\"T_4c571_level0_col1\" class=\"col_heading level0 col1\" >prec@10</th>\n",
       "      <th id=\"T_4c571_level0_col2\" class=\"col_heading level0 col2\" >recall@10</th>\n",
       "      <th id=\"T_4c571_level0_col3\" class=\"col_heading level0 col3\" >novelty@10</th>\n",
       "      <th id=\"T_4c571_level0_col4\" class=\"col_heading level0 col4\" >serendipity@10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >model</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4c571_level0_row0\" class=\"row_heading level0 row0\" >random</th>\n",
       "      <td id=\"T_4c571_row0_col0\" class=\"data row0 col0\" >0.010898</td>\n",
       "      <td id=\"T_4c571_row0_col1\" class=\"data row0 col1\" >0.003653</td>\n",
       "      <td id=\"T_4c571_row0_col2\" class=\"data row0 col2\" >0.000813</td>\n",
       "      <td id=\"T_4c571_row0_col3\" class=\"data row0 col3\" >6.449487</td>\n",
       "      <td id=\"T_4c571_row0_col4\" class=\"data row0 col4\" >0.000535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c571_level0_row1\" class=\"row_heading level0 row1\" >popular</th>\n",
       "      <td id=\"T_4c571_row1_col0\" class=\"data row1 col0\" >0.050473</td>\n",
       "      <td id=\"T_4c571_row1_col1\" class=\"data row1 col1\" >0.041440</td>\n",
       "      <td id=\"T_4c571_row1_col2\" class=\"data row1 col2\" >0.031034</td>\n",
       "      <td id=\"T_4c571_row1_col3\" class=\"data row1 col3\" >1.608596</td>\n",
       "      <td id=\"T_4c571_row1_col4\" class=\"data row1 col4\" >0.000252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c571_level0_row2\" class=\"row_heading level0 row2\" >most_rated</th>\n",
       "      <td id=\"T_4c571_row2_col0\" class=\"data row2 col0\" >0.044625</td>\n",
       "      <td id=\"T_4c571_row2_col1\" class=\"data row2 col1\" >0.040734</td>\n",
       "      <td id=\"T_4c571_row2_col2\" class=\"data row2 col2\" >0.025564</td>\n",
       "      <td id=\"T_4c571_row2_col3\" class=\"data row2 col3\" >1.620426</td>\n",
       "      <td id=\"T_4c571_row2_col4\" class=\"data row2 col4\" >0.000213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c571_level0_row3\" class=\"row_heading level0 row3\" >tfidf_k=5</th>\n",
       "      <td id=\"T_4c571_row3_col0\" class=\"data row3 col0\" >0.065625</td>\n",
       "      <td id=\"T_4c571_row3_col1\" class=\"data row3 col1\" >0.043076</td>\n",
       "      <td id=\"T_4c571_row3_col2\" class=\"data row3 col2\" >0.037354</td>\n",
       "      <td id=\"T_4c571_row3_col3\" class=\"data row3 col3\" >2.355431</td>\n",
       "      <td id=\"T_4c571_row3_col4\" class=\"data row3 col4\" >0.001527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c571_level0_row4\" class=\"row_heading level0 row4\" >tfidf_k=10</th>\n",
       "      <td id=\"T_4c571_row4_col0\" class=\"data row4 col0\" >0.046635</td>\n",
       "      <td id=\"T_4c571_row4_col1\" class=\"data row4 col1\" >0.041827</td>\n",
       "      <td id=\"T_4c571_row4_col2\" class=\"data row4 col2\" >0.034497</td>\n",
       "      <td id=\"T_4c571_row4_col3\" class=\"data row4 col3\" >2.162626</td>\n",
       "      <td id=\"T_4c571_row4_col4\" class=\"data row4 col4\" >0.000863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c571_level0_row5\" class=\"row_heading level0 row5\" >bm25_k=10_k1=0.05_b=0.1</th>\n",
       "      <td id=\"T_4c571_row5_col0\" class=\"data row5 col0\" >0.062584</td>\n",
       "      <td id=\"T_4c571_row5_col1\" class=\"data row5 col1\" >0.042792</td>\n",
       "      <td id=\"T_4c571_row5_col2\" class=\"data row5 col2\" >0.031797</td>\n",
       "      <td id=\"T_4c571_row5_col3\" class=\"data row5 col3\" >1.830871</td>\n",
       "      <td id=\"T_4c571_row5_col4\" class=\"data row5 col4\" >0.000337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc2baa0b520>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_results = (\n",
    "    pd.DataFrame(cv_results[\"metrics\"])\n",
    "    .drop(columns=\"i_split\")\n",
    "    .groupby([\"model\"], sort=False)\n",
    "    .agg([\"mean\"])\n",
    ")\n",
    "pivot_results.columns = pivot_results.columns.droplevel(1)\n",
    "\n",
    "(\n",
    "    pivot_results.style\n",
    "    .set_caption(\"Mean values of metrics\")\n",
    "    .highlight_min(color='lightcoral', axis=0)\n",
    "    .highlight_max(color='lightgreen', axis=0)\n",
    ")"
   ]
  },
  {
   "attachments": {
    "cv.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![cv.png](attachment:cv.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In RecTools we have interactive `MetricsApp` for detailed analysis of metrics trade-off between different models.\n",
    "`visuals` extension is required to run the path of code below. You can install it with `pip install rectools[visuals]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "metadata_example = {\n",
    "    Columns.Model: [\"random\", \"popular\", \"most_rated\", \"tfidf_k=5\", \"tfidf_k=10\", \"bm25_k=10_k1=0.05_b=0.1\"],\n",
    "    \"k\": [None, None, None, 5, 10, 10]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1317014033ec495ca4ca95794b888cb1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Tab(children=(VBox(children=(HBox(children=(Checkbox(value=True, description='Average folds'), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app = MetricsApp.construct(\n",
    "    models_metrics=pd.DataFrame(cv_results[\"metrics\"]),\n",
    "    models_metadata=pd.DataFrame(metadata_example),  # optional\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you run this notebook, you will get **interactive** widgets with active buttons to select metrics, folds and metadata coloring. For offline presentation we keep **static** screenshots of the actual app."
   ]
  },
  {
   "attachments": {
    "app.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![app.png](attachment:app.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to save static image from your app, you can access `plotly.graph_objs.Figure` and render it as an image. Make sure you have `kaleido` package to process rendering. You can install it with `pip install kaleido==0.2.1` and restart the kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = app.fig\n",
    "fig.update_layout(title=\"Metrics: prec@10 vs prec@1\")\n",
    "fig.show(\"png\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "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": "3.10.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
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