notebooks/4-measuring-creativity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "435033eb",
   "metadata": {},
   "source": [
    "# Measuring creativity\n",
    "\n",
    "Once you've trained a pass selection, pass outcome and pass value model, they can be combined to measure the creativity of a pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "84a0e37d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from functools import partial\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import mlflow\n",
    "from scipy.ndimage import zoom\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "435cbe12",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d3e26b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from unxpass.databases import SQLiteDatabase\n",
    "from unxpass.datasets import PassesDataset\n",
    "from unxpass.components import pass_selection, pass_value, pass_success\n",
    "from unxpass.components.utils import load_model\n",
    "from unxpass.visualization import plot_action\n",
    "from unxpass.ratings import CreativeDecisionRating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "462583e9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plt_settings = {\"cmap\": \"magma\", \"vmin\": 0, \"vmax\": 1, \"interpolation\": \"bilinear\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aae3ed4",
   "metadata": {},
   "source": [
    "## Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "75cda90d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "STORES_FP = Path(\"../stores\")\n",
    "\n",
    "db = SQLiteDatabase(STORES_FP / \"database.sql\")\n",
    "\n",
    "dataset_test = partial(PassesDataset, path=STORES_FP / \"datasets\" / \"euro2020\" / \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b01f0dd8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Select an example pass\n",
    "SAMPLE = (3795506, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a6cca79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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></th>\n",
       "      <th>original_event_id</th>\n",
       "      <th>period_id</th>\n",
       "      <th>time_seconds</th>\n",
       "      <th>team_id</th>\n",
       "      <th>player_id</th>\n",
       "      <th>start_x</th>\n",
       "      <th>start_y</th>\n",
       "      <th>end_x</th>\n",
       "      <th>end_y</th>\n",
       "      <th>bodypart_id</th>\n",
       "      <th>type_id</th>\n",
       "      <th>result_id</th>\n",
       "      <th>possession_team_id</th>\n",
       "      <th>play_pattern_name</th>\n",
       "      <th>under_pressure</th>\n",
       "      <th>extra</th>\n",
       "      <th>visible_area_360</th>\n",
       "      <th>in_visible_area_360</th>\n",
       "      <th>freeze_frame_360</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3795506</th>\n",
       "      <th>4</th>\n",
       "      <td>7cc5e22d-caff-46b1-8adf-fb1f3e3bdc1e</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>914</td>\n",
       "      <td>11514</td>\n",
       "      <td>33.705882</td>\n",
       "      <td>9.98481</td>\n",
       "      <td>21.088235</td>\n",
       "      <td>28.663291</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>914.0</td>\n",
       "      <td>From Kick Off</td>\n",
       "      <td>0</td>\n",
       "      <td>{'pass': {'recipient': {'id': 7173, 'name': 'L...</td>\n",
       "      <td>[[53.155008354104645, 0.0], [32.85311409175197...</td>\n",
       "      <td>1</td>\n",
       "      <td>[{'teammate': True, 'actor': False, 'keeper': ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              original_event_id period_id time_seconds  \\\n",
       "3795506 4  7cc5e22d-caff-46b1-8adf-fb1f3e3bdc1e         1          5.0   \n",
       "\n",
       "          team_id player_id    start_x  start_y      end_x      end_y  \\\n",
       "3795506 4     914     11514  33.705882  9.98481  21.088235  28.663291   \n",
       "\n",
       "          bodypart_id type_id result_id possession_team_id play_pattern_name  \\\n",
       "3795506 4           5       0         1              914.0     From Kick Off   \n",
       "\n",
       "          under_pressure                                              extra  \\\n",
       "3795506 4              0  {'pass': {'recipient': {'id': 7173, 'name': 'L...   \n",
       "\n",
       "                                            visible_area_360  \\\n",
       "3795506 4  [[53.155008354104645, 0.0], [32.85311409175197...   \n",
       "\n",
       "          in_visible_area_360  \\\n",
       "3795506 4                   1   \n",
       "\n",
       "                                            freeze_frame_360  \n",
       "3795506 4  [{'teammate': True, 'actor': False, 'keeper': ...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the selected example\n",
    "ex_action = db.actions(game_id=SAMPLE[0]).loc[SAMPLE]\n",
    "display(ex_action.to_frame().T)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "ax = plot_action(ex_action, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbdb70da-3aa9-4115-9f40-b59b03fdc32b",
   "metadata": {},
   "source": [
    "Note that this is probably not a creative pass. It looks like a typical pass and it is not very useful."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f497b078-8ea0-41f6-b23c-592b1cd4bb11",
   "metadata": {},
   "source": [
    "## Load models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21089b22",
   "metadata": {},
   "source": [
    "### Pass selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "df46b4c3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[07/17/23 11:59:08] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Loading dataset from ..<span style=\"color: #800080; text-decoration-color: #800080\">/stores/datasets/euro2020/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">test</span>                  <a href=\"file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">rank_zero.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py#32\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">32</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[07/17/23 11:59:08]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loading dataset from ..\u001b[35m/stores/datasets/euro2020/\u001b[0m\u001b[95mtest\u001b[0m                  \u001b]8;id=107425;file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py\u001b\\\u001b[2mrank_zero.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=584300;file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py#32\u001b\\\u001b[2m32\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'log_loss': 5.850149801707092,\n",
       " 'brier': 0.9885089131955397,\n",
       " 'acc': 0.5454545454545454}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_pass_selection = pass_selection.SoccerMapComponent(\n",
    "    model=mlflow.pytorch.load_model(\n",
    "        #'runs:/8a4c034f5d2046039e95393c06fe8969/model', map_location='cpu'\n",
    "        'runs:/788ec5a232af46e59ac984d50ecfc1d5/model', map_location='cpu'\n",
    "    )\n",
    ")\n",
    "model_pass_selection.test(dataset_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "042b58cb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[07/17/23 12:00:42] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Loading dataset from ..<span style=\"color: #800080; text-decoration-color: #800080\">/stores/datasets/euro2020/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">test</span>                  <a href=\"file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">rank_zero.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py#32\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">32</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[07/17/23 12:00:42]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loading dataset from ..\u001b[35m/stores/datasets/euro2020/\u001b[0m\u001b[95mtest\u001b[0m                  \u001b]8;id=407289;file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py\u001b\\\u001b[2mrank_zero.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=492629;file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py#32\u001b\\\u001b[2m32\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting DataLoader 0: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 594/594 [00:08<00:00, 66.29it/s]\n"
     ]
    }
   ],
   "source": [
    "surface_pass_selection = model_pass_selection.predict_surface(dataset_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0c2b8b63",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax = plot_action(\n",
    "    ex_action, surface=surface_pass_selection[SAMPLE[0]][SAMPLE[1]], \n",
    "    show_visible_area=False, show_action=False,\n",
    "    ax=ax, surface_kwargs={**plt_settings, \"vmin\": None, \"vmax\": None, \"cmap\": \"Greens\"}\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b40fba",
   "metadata": {},
   "source": [
    "### Pass success"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a5c8839",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[07/17/23 12:03:02] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Loading dataset from ..<span style=\"color: #800080; text-decoration-color: #800080\">/stores/datasets/euro2020/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">test</span>                  <a href=\"file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">rank_zero.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py#32\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">32</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[07/17/23 12:03:02]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loading dataset from ..\u001b[35m/stores/datasets/euro2020/\u001b[0m\u001b[95mtest\u001b[0m                  \u001b]8;id=965482;file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py\u001b\\\u001b[2mrank_zero.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=32931;file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py#32\u001b\\\u001b[2m32\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'precision': 0.9617486338797814,\n",
       " 'recall': 0.9795918367346939,\n",
       " 'f1': 0.9705882352941176,\n",
       " 'log_loss': 0.1561886008002896,\n",
       " 'brier': 0.045388154333901594,\n",
       " 'roc_auc': 0.9426210153482881}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_pass_success = load_model('runs:/f0d0458824324fbbb257550bf09d924a/component')\n",
    "model_pass_success.test(dataset_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0cbfc87",
   "metadata": {},
   "source": [
    "### Pass value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ec70fd83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[07/17/23 12:03:03] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Loading dataset from ..<span style=\"color: #800080; text-decoration-color: #800080\">/stores/datasets/euro2020/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">test</span>                  <a href=\"file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">rank_zero.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py#32\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">32</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[07/17/23 12:03:03]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loading dataset from ..\u001b[35m/stores/datasets/euro2020/\u001b[0m\u001b[95mtest\u001b[0m                  \u001b]8;id=602946;file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py\u001b\\\u001b[2mrank_zero.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=387168;file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py#32\u001b\\\u001b[2m32\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[07/17/23 12:03:04] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Loading dataset from ..<span style=\"color: #800080; text-decoration-color: #800080\">/stores/datasets/euro2020/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">test</span>                  <a href=\"file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">rank_zero.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py#32\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">32</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[07/17/23 12:03:04]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loading dataset from ..\u001b[35m/stores/datasets/euro2020/\u001b[0m\u001b[95mtest\u001b[0m                  \u001b]8;id=117996;file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py\u001b\\\u001b[2mrank_zero.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=727981;file:///cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccer-unxpass-public/.venv/lib/python3.9/site-packages/lightning_utilities/core/rank_zero.py#32\u001b\\\u001b[2m32\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'offensive': {'brier': 0.00913297383356254,\n",
       "  'log_loss': 0.04838631026974633,\n",
       "  'roc_auc': 0.8361678004535148},\n",
       " 'defensive': {'brier': 0.0016870561834701665,\n",
       "  'log_loss': 0.01366890293054475,\n",
       "  'roc_auc': 0.9763912310286678}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_pass_value = load_model(\n",
    "    'runs:/f4f4efb5f0534f03a1d513141e06c962/component',\n",
    ")\n",
    "model_pass_value.test(dataset_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829e792f-05fb-4a24-a610-91ed83d38561",
   "metadata": {},
   "source": [
    "## Value creativity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "05ba0d05-6cc7-4fff-9c6e-2ba0c6fec31e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rater = CreativeDecisionRating(\n",
    "    pass_selection_component=model_pass_selection,\n",
    "    pass_success_component=model_pass_success,\n",
    "    pass_value_component=model_pass_value\n",
    ")"
   ]
  },
  {
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   "execution_count": 16,
   "id": "b1f0fb24-2cd8-444a-9c7f-d4309cc4e801",
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       "start_x                  33.705882\n",
       "start_y                    9.98481\n",
       "true_end_x               21.088235\n",
       "true_end_y               28.663291\n",
       "success                       True\n",
       "typical_end_x              45.9375\n",
       "typical_end_y                  0.5\n",
       "true_p_success            0.998893\n",
       "typical_p_success         0.992608\n",
       "true_value_success        0.002047\n",
       "typical_value_success     0.002685\n",
       "true_value_fail          -0.022069\n",
       "typical_value_fail        -0.00076\n",
       "CDR                      -0.000651\n",
       "Name: (3795506, 4), dtype: object"
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     "execution_count": 16,
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    "df_ratings = rater.rate(db, dataset_test)\n",
    "df_ratings.loc[SAMPLE]"
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