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{
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"source": [
"import os\n",
"import warnings\n",
"import tqdm\n",
"import pandas as pd\n",
"warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)"
]
},
{
"cell_type": "code",
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"metadata": {
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"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import socceraction.atomic.vaep.features as fs\n",
"import socceraction.atomic.vaep.labels as lab"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
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"source": [
"# Configure file and folder names\n",
"datafolder = \"../data-fifa\"\n",
"spadl_h5 = os.path.join(datafolder, \"atomic-spadl-statsbomb.h5\")\n",
"features_h5 = os.path.join(datafolder, \"atomic-features.h5\")\n",
"labels_h5 = os.path.join(datafolder, \"atomic-labels.h5\")\n",
"predictions_h5 = os.path.join(datafolder, \"atomic-predictions-one-action.h5\")"
]
},
{
"cell_type": "code",
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"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"nb of games: 64\n"
]
}
],
"source": [
"games = pd.read_hdf(spadl_h5, \"games\")\n",
"print(\"nb of games:\", len(games))\n",
"\n",
"# note: only for the purpose of this example and due to the small dataset,\n",
"# we use the same data for training and evaluation\n",
"traingames = games\n",
"testgames = games"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
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"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Selecting features: 100%|██████████████████████████████████████████████████████████████| 64/64 [00:02<00:00, 26.67it/s]\n",
"Selecting label: 100%|████████████████████████████████████████████████████████████████| 64/64 [00:00<00:00, 210.79it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"X: ['actiontype_pass_a0', 'actiontype_cross_a0', 'actiontype_throw_in_a0', 'actiontype_freekick_crossed_a0', 'actiontype_freekick_short_a0', 'actiontype_corner_crossed_a0', 'actiontype_corner_short_a0', 'actiontype_take_on_a0', 'actiontype_foul_a0', 'actiontype_tackle_a0', 'actiontype_interception_a0', 'actiontype_shot_a0', 'actiontype_shot_penalty_a0', 'actiontype_shot_freekick_a0', 'actiontype_keeper_save_a0', 'actiontype_keeper_claim_a0', 'actiontype_keeper_punch_a0', 'actiontype_keeper_pick_up_a0', 'actiontype_clearance_a0', 'actiontype_bad_touch_a0', 'actiontype_non_action_a0', 'actiontype_dribble_a0', 'actiontype_goalkick_a0', 'actiontype_receival_a0', 'actiontype_out_a0', 'actiontype_offside_a0', 'actiontype_goal_a0', 'actiontype_owngoal_a0', 'actiontype_yellow_card_a0', 'actiontype_red_card_a0', 'actiontype_corner_a0', 'actiontype_freekick_a0', 'bodypart_foot_a0', 'bodypart_head_a0', 'bodypart_other_a0', 'bodypart_head/other_a0', 'goalscore_team', 'goalscore_opponent', 'goalscore_diff', 'x_a0', 'y_a0', 'dist_to_goal_a0', 'angle_to_goal_a0', 'dx_a0', 'dy_a0', 'period_id_a0', 'time_seconds_a0', 'time_seconds_overall_a0']\n",
"Y: ['scores', 'concedes']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# 1. Select feature set X\n",
"xfns = [\n",
" #fs.actiontype,\n",
" fs.actiontype_onehot,\n",
" #fs.bodypart,\n",
" fs.bodypart_onehot,\n",
" fs.goalscore,\n",
" fs.location,\n",
" fs.polar,\n",
" fs.direction,\n",
" fs.team,\n",
" fs.time,\n",
" fs.time_delta\n",
"]\n",
"nb_prev_actions = 1\n",
"\n",
"Xcols = fs.feature_column_names(xfns, nb_prev_actions)\n",
"\n",
"def getXY(games, Xcols):\n",
" # generate the columns of the selected feature\n",
" X = []\n",
" for game_id in tqdm.tqdm(games.game_id, desc=\"Selecting features\"):\n",
" Xi = pd.read_hdf(features_h5, f\"game_{game_id}\")\n",
" X.append(Xi[Xcols])\n",
" X = pd.concat(X).reset_index(drop=True)\n",
"\n",
" # 2. Select label Y\n",
" Ycols = [\"scores\", \"concedes\"]\n",
" Y = []\n",
" for game_id in tqdm.tqdm(games.game_id, desc=\"Selecting label\"):\n",
" Yi = pd.read_hdf(labels_h5, f\"game_{game_id}\")\n",
" Y.append(Yi[Ycols])\n",
" Y = pd.concat(Y).reset_index(drop=True)\n",
" return X, Y\n",
"\n",
"X,Y = getXY(traingames, Xcols)\n",
"print(\"X:\", list(X.columns))\n",
"print(\"Y:\", list(Y.columns))\n",
"X = X.fillna(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train a model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"scores\n",
"concedes\n",
"CPU times: user 5.38 s, sys: 123 ms, total: 5.5 s\n",
"Wall time: 1.5 s\n"
]
}
],
"source": [
"%%time\n",
"# train classifiers F(X) = Y\n",
"import xgboost\n",
"\n",
"Y_hat = pd.DataFrame()\n",
"models = {}\n",
"for col in list(Y.columns):\n",
" print(col)\n",
" model = xgboost.XGBClassifier(n_estimators=50, max_depth=3, n_jobs=-3, verbosity=1, enable_categorical=True)\n",
" model.fit(X, Y[col])\n",
" models[col] = model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluate the model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-30T16:25:47.733065Z",
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"### Y: scores ###\n",
" Brier score: 0.00583 (0.75235)\n",
" log loss score: 0.02777 (0.60796)\n",
" ROC AUC: 0.93296\n",
"### Y: concedes ###\n",
" Brier score: 0.00113 (0.63887)\n",
" log loss score: 0.00603 (0.46528)\n",
" ROC AUC: 0.97023\n"
]
}
],
"source": [
"from sklearn.metrics import brier_score_loss, roc_auc_score, log_loss\n",
"\n",
"testX, testY = X, Y\n",
"\n",
"def evaluate(y, y_hat):\n",
" p = sum(y) / len(y)\n",
" base = [p] * len(y)\n",
" brier = brier_score_loss(y, y_hat)\n",
" print(f\" Brier score: %.5f (%.5f)\" % (brier, brier / brier_score_loss(y, base)))\n",
" ll = log_loss(y, y_hat)\n",
" print(f\" log loss score: %.5f (%.5f)\" % (ll, ll / log_loss(y, base)))\n",
" print(f\" ROC AUC: %.5f\" % roc_auc_score(y, y_hat))\n",
"\n",
"for col in testY.columns:\n",
" Y_hat[col] = [p[1] for p in models[col].predict_proba(testX)]\n",
" print(f\"### Y: {col} ###\")\n",
" evaluate(testY[col], Y_hat[col])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save predictions"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-30T16:25:49.090602Z",
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading actions of each game: 100%|███████████████████████████████████████████████████| 64/64 [00:00<00:00, 145.54it/s]\n",
"Saving predictions per game: 100%|█████████████████████████████████████████████████████| 64/64 [00:06<00:00, 9.17it/s]\n"
]
}
],
"source": [
"# get rows with game id per action\n",
"A = []\n",
"for game_id in tqdm.tqdm(testgames.game_id, \"Loading actions of each game\"):\n",
" Ai = pd.read_hdf(spadl_h5, f\"atomic_actions/game_{game_id}\")\n",
" A.append(Ai[[\"game_id\"]])\n",
"A = pd.concat(A)\n",
"A = A.reset_index(drop=True)\n",
"\n",
"# concatenate action game id rows with predictions and save per game\n",
"grouped_predictions = pd.concat([A, Y_hat], axis=1).groupby(\"game_id\")\n",
"for k,df in tqdm.tqdm(grouped_predictions, desc=\"Saving predictions per game\"):\n",
" df = df.reset_index(drop=True)\n",
" df[Y_hat.columns].to_hdf(predictions_h5, f\"game_{int(k)}\")"
]
}
],
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