diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..9f6dc03 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,12 @@ +repos: +- repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.14.8 + hooks: + # Run the linter. + - id: ruff-check + types_or: [ python, pyi ] + args: [ --fix ] + # Run the formatter. + - id: ruff-format + types_or: [ python, pyi ] \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..c83fbb9 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,10 @@ +{ + "[python]": { + "editor.formatOnSave": true, + "editor.codeActionsOnSave": { + //"source.fixAll": "explicit", + "source.organizeImports": "explicit" + }, + "editor.defaultFormatter": "charliermarsh.ruff" + } +} \ No newline at end of file diff --git a/README.md b/README.md index b7bca55..e002e0b 100644 --- a/README.md +++ b/README.md @@ -100,9 +100,10 @@ This project uses `uv` for managing the python virtual environment. To install u curl -LsSf https://astral.sh/uv/install.sh | sh ``` -To create and activate the virtual environment, run the following: +To create and activate the virtual environment and setup git hooks (for auto linting and formatting), run the following: ```bash uv sync --locked +uv run pre-commit install ``` ## Run instructions @@ -143,12 +144,40 @@ The last command will start the MLflow GUI at your local host loopback on port 5 ![mlflow_tune](./docs/images/mlflow_tune.png) -## Future Work +# Contributing + +## Linting and Formatting +We are using [ruff](https://docs.astral.sh/ruff/) as a the linter and formatter. Ruff can be used with: + +```bash +# Get check results +uv run ruff check . -For model improvements I'm interested in switching to a flash attention layer which can accelerate training +# Get check results and apply fixes (recommend review these with git diff) +uv run ruff check . --fix -I also want to build up per team models, do ANOVA, then highlight specific players that are giving tells on coverages +# Run formatter +uv run ruff format . +``` -Want to try out the model on a non-binary problem and try distinguishing between distinct coverage types +Note, if you followed instructions above to setup the git hook, ruff will run the linter (with auto fixing on) and formatting as a pre-commit git hook. -See if I can identify blitzer (is the blitzer player identified in the dataset?) \ No newline at end of file +## Future Work +- Data + - Incoporate the additional NGS data which likely will have the largest returns in increasing accuracy + - Look into additional data augmentation techniques +- Model + - Consider model improvements in transformer arch + - Build per team model, do ANOVA, then highlight specific players that are giving tells on coverages + - Build multiclass output model instead of binary classification + - See if I can build a model to determine blitzer like red circle AWS at post snap + - Add in confusion matrix, ROC curve, and prediction post snap accuracy to automatic predictions and not just in notebook +- Infra + - Profiler + - Add in PyTorch profiler with new tensorboard (new thing, this is deprecated) traces into Chrome UI, built into CI for profiler + - Optimization + - Switch to flash attention layer which can accelerate training + - General + - Get docker working and implemented in CI + - Add git hook for ruff fix + - Look back on mypy for type \ No newline at end of file diff --git a/notebooks/eda.ipynb b/notebooks/eda.ipynb index 55494b2..598d7d2 100644 --- a/notebooks/eda.ipynb +++ b/notebooks/eda.ipynb @@ -7,11 +7,9 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import matplotlib.pyplot as plt \n", - "import seaborn\n", + "import pandas as pd\n", "\n", "from load_data import RawDataLoader\n", "\n", @@ -29,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "a6f4b6cb", "metadata": {}, "outputs": [ @@ -59,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "855562a0", "metadata": {}, "outputs": [ @@ -72,8 +70,8 @@ } ], "source": [ - "unique_teams = pd.unique(games_df[['homeTeamAbbr', 'visitorTeamAbbr']].values.ravel())\n", - "stats = {t: {'points': {'mean': 0, 'max': 0, 'min': 0, 'std': 0}, 'record': {'W': 0, 'L': 0, 'T': 0, 'G': 0}} for t in unique_teams}\n", + "unique_teams = pd.unique(games_df[[\"homeTeamAbbr\", \"visitorTeamAbbr\"]].values.ravel())\n", + "stats = {t: {\"points\": {\"mean\": 0, \"max\": 0, \"min\": 0, \"std\": 0}, \"record\": {\"W\": 0, \"L\": 0, \"T\": 0, \"G\": 0}} for t in unique_teams}\n", "print(stats)" ] }, @@ -87,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "c6dfeb76", "metadata": {}, "outputs": [ @@ -107,29 +105,29 @@ "games_stats_df = games_df.copy()\n", "winner_list = []\n", "for i in range(len(games_stats_df)):\n", - " home_score = games_stats_df.iloc[i]['homeFinalScore']\n", - " visitor_score = games_stats_df.iloc[i]['visitorFinalScore']\n", - " if home_score > visitor_score:\n", - " winner_list.append(games_stats_df.iloc[i]['homeTeamAbbr'])\n", - " elif home_score < visitor_score:\n", - " winner_list.append(games_stats_df.iloc[i]['visitorTeamAbbr'])\n", - " else:\n", - " winner_list.append(\"tie\")\n", + "\thome_score = games_stats_df.iloc[i][\"homeFinalScore\"]\n", + "\tvisitor_score = games_stats_df.iloc[i][\"visitorFinalScore\"]\n", + "\tif home_score > visitor_score:\n", + "\t\twinner_list.append(games_stats_df.iloc[i][\"homeTeamAbbr\"])\n", + "\telif home_score < visitor_score:\n", + "\t\twinner_list.append(games_stats_df.iloc[i][\"visitorTeamAbbr\"])\n", + "\telse:\n", + "\t\twinner_list.append(\"tie\")\n", "games_stats_df[\"winner\"] = winner_list\n", "\n", "# Determine result stats\n", "home_team_wins = len(games_stats_df[games_stats_df[\"winner\"] == games_stats_df[\"homeTeamAbbr\"]])\n", "visitor_team_wins = len(games_stats_df[games_stats_df[\"winner\"] == games_stats_df[\"visitorTeamAbbr\"]])\n", "ties = len(games_stats_df[games_stats_df[\"winner\"] == \"tie\"])\n", - "print(f'Home team wins: {home_team_wins}')\n", - "print(f'Visitor team wins: {visitor_team_wins}')\n", - "print(f'Ties: {ties}')\n", - "print()\n" + "print(f\"Home team wins: {home_team_wins}\")\n", + "print(f\"Visitor team wins: {visitor_team_wins}\")\n", + "print(f\"Ties: {ties}\")\n", + "print()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "8f05b52e", "metadata": {}, "outputs": [ @@ -176,34 +174,34 @@ ], "source": [ "# Determine inividual schedule and record\n", - "for home, away, winner in games_stats_df[['homeTeamAbbr','visitorTeamAbbr','winner']].itertuples(index=False, name=None):\n", - " # count a game for both teams\n", - " stats[home][\"record\"]['G'] += 1\n", - " stats[away][\"record\"]['G'] += 1\n", + "for home, away, winner in games_stats_df[[\"homeTeamAbbr\", \"visitorTeamAbbr\", \"winner\"]].itertuples(index=False, name=None):\n", + "\t# count a game for both teams\n", + "\tstats[home][\"record\"][\"G\"] += 1\n", + "\tstats[away][\"record\"][\"G\"] += 1\n", "\n", - " # ties\n", - " if winner.strip().lower() == 'tie':\n", - " stats[home][\"record\"]['T'] += 1\n", - " stats[away][\"record\"]['T'] += 1\n", - " continue\n", + "\t# ties\n", + "\tif winner.strip().lower() == \"tie\":\n", + "\t\tstats[home][\"record\"][\"T\"] += 1\n", + "\t\tstats[away][\"record\"][\"T\"] += 1\n", + "\t\tcontinue\n", "\n", - " # wins/losses\n", - " if winner == home:\n", - " stats[home][\"record\"]['W'] += 1\n", - " stats[away][\"record\"]['L'] += 1\n", - " elif winner == away:\n", - " stats[away][\"record\"]['W'] += 1\n", - " stats[home][\"record\"]['L'] += 1\n", - " else:\n", - " print(f\"Skipping row with unknown winner: {winner} with {home}/{away}\")\n", - " pass\n", + "\t# wins/losses\n", + "\tif winner == home:\n", + "\t\tstats[home][\"record\"][\"W\"] += 1\n", + "\t\tstats[away][\"record\"][\"L\"] += 1\n", + "\telif winner == away:\n", + "\t\tstats[away][\"record\"][\"W\"] += 1\n", + "\t\tstats[home][\"record\"][\"L\"] += 1\n", + "\telse:\n", + "\t\tprint(f\"Skipping row with unknown winner: {winner} with {home}/{away}\")\n", + "\t\tpass\n", "\n", "# show records\n", "print(f\"{'Team':<5} {'W':>5} {'L':>5} {'T':>5} {'G':>5}\")\n", "print(\"-\" * 35)\n", "for team in stats.keys():\n", - " r = stats[team]['record']\n", - " print(f\"{team:<5} {r['W']:5d} {r['L']:5d} {r['T']:5d} {r['G']:5d}\")" + "\tr = stats[team][\"record\"]\n", + "\tprint(f\"{team:<5} {r['W']:5d} {r['L']:5d} {r['T']:5d} {r['G']:5d}\")" ] }, { @@ -216,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "fd4b9f45", "metadata": {}, "outputs": [ @@ -268,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "140c59f3", "metadata": {}, "outputs": [ @@ -349,24 +347,23 @@ ], "source": [ "# Create melted df\n", - "cols = ['week', 'homeTeamAbbr', 'visitorTeamAbbr', 'homeFinalScore', 'visitorFinalScore']\n", - "home = games_stats_df[['week', 'homeTeamAbbr', 'homeFinalScore']].rename(columns={'homeTeamAbbr': 'team', 'homeFinalScore': 'points'})\n", - "away = games_stats_df[['week', 'visitorTeamAbbr', 'visitorFinalScore']].rename(columns={'visitorTeamAbbr': 'team', 'visitorFinalScore': 'points'})\n", + "cols = [\"week\", \"homeTeamAbbr\", \"visitorTeamAbbr\", \"homeFinalScore\", \"visitorFinalScore\"]\n", + "home = games_stats_df[[\"week\", \"homeTeamAbbr\", \"homeFinalScore\"]].rename(columns={\"homeTeamAbbr\": \"team\", \"homeFinalScore\": \"points\"})\n", + "away = games_stats_df[[\"week\", \"visitorTeamAbbr\", \"visitorFinalScore\"]].rename(columns={\"visitorTeamAbbr\": \"team\", \"visitorFinalScore\": \"points\"})\n", "team_points = pd.concat([home, away], ignore_index=True)\n", "\n", "# Group by team and week\n", "points_by_team_week = (\n", - " team_points\n", - " .groupby(['team', 'week'], as_index=False)['points']\n", - " .sum() # sum handles any duplicate rows or multiple games in same week\n", - " .sort_values(['team', 'week'])\n", + "\tteam_points.groupby([\"team\", \"week\"], as_index=False)[\"points\"]\n", + "\t.sum() # sum handles any duplicate rows or multiple games in same week\n", + "\t.sort_values([\"team\", \"week\"])\n", ")\n", "points_by_team_week.head()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "12bdc91d", "metadata": {}, "outputs": [ @@ -417,12 +414,12 @@ "print(\"-\" * 35)\n", "\n", "for team in stats.keys():\n", - " stats[team]['points']['mean'] = np.mean(points_by_team_week[points_by_team_week[\"team\"] == team][\"points\"])\n", - " stats[team]['points']['std'] = np.std(points_by_team_week[points_by_team_week[\"team\"] == team][\"points\"])\n", - " stats[team]['points']['max'] = np.max(points_by_team_week[points_by_team_week[\"team\"] == team][\"points\"])\n", - " stats[team]['points']['min'] = np.min(points_by_team_week[points_by_team_week[\"team\"] == team][\"points\"])\n", - " p = stats[team]['points']\n", - " print(f\"{team:<5} {p['mean']:8.2f} {p['std']:8.2f} {p['min']:5d} {p['max']:5d}\")" + "\tstats[team][\"points\"][\"mean\"] = np.mean(points_by_team_week[points_by_team_week[\"team\"] == team][\"points\"])\n", + "\tstats[team][\"points\"][\"std\"] = np.std(points_by_team_week[points_by_team_week[\"team\"] == team][\"points\"])\n", + "\tstats[team][\"points\"][\"max\"] = np.max(points_by_team_week[points_by_team_week[\"team\"] == team][\"points\"])\n", + "\tstats[team][\"points\"][\"min\"] = np.min(points_by_team_week[points_by_team_week[\"team\"] == team][\"points\"])\n", + "\tp = stats[team][\"points\"]\n", + "\tprint(f\"{team:<5} {p['mean']:8.2f} {p['std']:8.2f} {p['min']:5d} {p['max']:5d}\")" ] }, { @@ -435,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "1eb6e7da", "metadata": {}, "outputs": [ @@ -666,7 +663,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "586caee7", "metadata": {}, "outputs": [ @@ -710,7 +707,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "efe5c7cd", "metadata": {}, "outputs": [ @@ -725,20 +722,20 @@ } ], "source": [ - "man = len(plays_df[plays_df['pff_manZone'] == 'Man'])\n", - "zone = len(plays_df[plays_df['pff_manZone'] == 'Zone'])\n", + "man = len(plays_df[plays_df[\"pff_manZone\"] == \"Man\"])\n", + "zone = len(plays_df[plays_df[\"pff_manZone\"] == \"Zone\"])\n", "\n", - "man_percent = (man / (man + zone))*100\n", - "zone_percent = (zone / (man + zone))*100\n", + "man_percent = (man / (man + zone)) * 100\n", + "zone_percent = (zone / (man + zone)) * 100\n", "\n", - "print(plays_df['pff_manZone'].unique())\n", + "print(plays_df[\"pff_manZone\"].unique())\n", "print(f\"Man: {man_percent}\")\n", - "print(f\"Zone: {zone_percent}\")\n" + "print(f\"Zone: {zone_percent}\")" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "id": "75adfaba", "metadata": {}, "outputs": [ @@ -755,67 +752,59 @@ ], "source": [ "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", + "\n", "coverage_percent_map = {}\n", "for team in unique_teams:\n", + "\t# All plays\n", + "\ttotal_all_plays = len(plays_df[plays_df[\"defensiveTeam\"] == team])\n", + "\tman_all_plays = len(plays_df[(plays_df[\"pff_manZone\"] == \"Man\") & (plays_df[\"defensiveTeam\"] == team)])\n", + "\tzone_all_plays = len(plays_df[(plays_df[\"pff_manZone\"] == \"Zone\") & (plays_df[\"defensiveTeam\"] == team)])\n", + "\tman_all_pct = round((man_all_plays / total_all_plays) * 100)\n", + "\tzone_all_pct = round((zone_all_plays / total_all_plays) * 100)\n", "\n", - " # All plays\n", - " total_all_plays = len(plays_df[plays_df[\"defensiveTeam\"] == team]) \n", - " man_all_plays = len(plays_df[(plays_df[\"pff_manZone\"] == \"Man\") & (plays_df[\"defensiveTeam\"] == team)]) \n", - " zone_all_plays = len(plays_df[(plays_df[\"pff_manZone\"] == \"Zone\") & (plays_df[\"defensiveTeam\"] == team)]) \n", - " man_all_pct = round((man_all_plays / total_all_plays) * 100)\n", - " zone_all_pct = round((zone_all_plays / total_all_plays) * 100)\n", - "\n", - " # Just pure passing plays\n", - " total_pass_plays = len(plays_df[\n", - " (plays_df[\"defensiveTeam\"] == team) & \n", - " (plays_df[\"passResult\"].notna())\n", - " ]) \n", - " man_pass_plays = len(plays_df[\n", - " (plays_df[\"pff_manZone\"] == \"Man\") &\n", - " (plays_df[\"passResult\"].notna()) &\n", - " (plays_df[\"defensiveTeam\"] == team)\n", - " ]) \n", - " zone_pass_plays = len(plays_df[\n", - " (plays_df[\"pff_manZone\"] == \"Zone\") &\n", - " (plays_df[\"passResult\"].notna()) &\n", - " (plays_df[\"defensiveTeam\"] == team)\n", - " ]) \n", - " man_pass_pct = round((man_pass_plays / total_pass_plays) * 100)\n", - " zone_pass_pct = round((zone_pass_plays / total_pass_plays) * 100)\n", + "\t# Just pure passing plays\n", + "\ttotal_pass_plays = len(plays_df[(plays_df[\"defensiveTeam\"] == team) & (plays_df[\"passResult\"].notna())])\n", + "\tman_pass_plays = len(plays_df[(plays_df[\"pff_manZone\"] == \"Man\") & (plays_df[\"passResult\"].notna()) & (plays_df[\"defensiveTeam\"] == team)])\n", + "\tzone_pass_plays = len(plays_df[(plays_df[\"pff_manZone\"] == \"Zone\") & (plays_df[\"passResult\"].notna()) & (plays_df[\"defensiveTeam\"] == team)])\n", + "\tman_pass_pct = round((man_pass_plays / total_pass_plays) * 100)\n", + "\tzone_pass_pct = round((zone_pass_plays / total_pass_plays) * 100)\n", "\n", - " coverage_percent_map[team] = {\n", - " \"zone_all\": zone_all_pct,\n", - " \"zone_pass\": zone_pass_pct,\n", - " \"man_all\": man_all_pct,\n", - " \"man_pass\": man_pass_pct,\n", - " }\n", + "\tcoverage_percent_map[team] = {\n", + "\t\t\"zone_all\": zone_all_pct,\n", + "\t\t\"zone_pass\": zone_pass_pct,\n", + "\t\t\"man_all\": man_all_pct,\n", + "\t\t\"man_pass\": man_pass_pct,\n", + "\t}\n", "\n", "\n", "# Convert dict to DataFrame\n", "df = pd.DataFrame.from_dict(coverage_percent_map, orient=\"index\")\n", "df = df[[\"man_pass\"]]\n", "df = df.sort_values(by=\"man_pass\", ascending=False)\n", - "df = df.rename(columns={\n", - " \"man_pass\": \"Man\",\n", - "})\n", + "df = df.rename(\n", + "\tcolumns={\n", + "\t\t\"man_pass\": \"Man\",\n", + "\t}\n", + ")\n", "\n", "# --- normalize per column for heatmap shading ---\n", "# Each column scaled to [0,1] for its own color contrast\n", "df_norm = df.copy()\n", "for col in df.columns:\n", - " cmin, cmax = df[col].min(), df[col].max()\n", - " df_norm[col] = (df[col] - cmin) / (cmax - cmin) if cmax != cmin else 0\n", + "\tcmin, cmax = df[col].min(), df[col].max()\n", + "\tdf_norm[col] = (df[col] - cmin) / (cmax - cmin) if cmax != cmin else 0\n", "\n", "plt.figure(figsize=(3, 10), dpi=200) # adjust height dynamically\n", "sns.set_theme(style=\"whitegrid\", font_scale=0.8)\n", "\n", "ax = sns.heatmap(\n", - " df_norm,\n", - " annot=df, fmt=\"d\",\n", - " cmap=\"flare\",\n", - " linewidths=0.5, linecolor=\"white\",\n", - " cbar_kws={\"label\": \"Relative intensity per column\"},\n", + "\tdf_norm,\n", + "\tannot=df,\n", + "\tfmt=\"d\",\n", + "\tcmap=\"flare\",\n", + "\tlinewidths=0.5,\n", + "\tlinecolor=\"white\",\n", + "\tcbar_kws={\"label\": \"Relative intensity per column\"},\n", ")\n", "\n", "# Title & labels\n", @@ -826,7 +815,7 @@ "# X axis\n", "ax.tick_params(axis=\"x\", top=True, labeltop=True, bottom=False, labelbottom=False)\n", "ax.set_xticklabels(ax.get_xticklabels(), rotation=0, ha=\"center\", fontsize=11, fontweight=\"bold\")\n", - "ax.xaxis.set_visible(False) # For only showing man\n", + "ax.xaxis.set_visible(False) # For only showing man\n", "\n", "# Y labels (teams)\n", "ax.set_yticklabels(ax.get_yticklabels(), rotation=0, fontsize=8)\n", @@ -846,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "c11be70f", "metadata": {}, "outputs": [ @@ -881,12 +870,12 @@ "coverages = plays_df[\"pff_passCoverage\"].unique()\n", "print(f\"Total plays: {len(plays_df)}\")\n", "for coverage in coverages:\n", - " print(f\"{coverage}: {len(plays_df[plays_df[\"pff_passCoverage\"] == coverage])}\")" + "\tprint(f\"{coverage}: {len(plays_df[plays_df['pff_passCoverage'] == coverage])}\")" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "id": "593f505b", "metadata": {}, "outputs": [ @@ -906,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "id": "5ed33246", "metadata": {}, "outputs": [ @@ -931,43 +920,26 @@ "source": [ "coverage_df = plays_df[plays_df[\"pff_passCoverage\"].isin(valid_coverages)].copy()\n", "print(len(coverage_df))\n", - "order = (\n", - " coverage_df.groupby(\"pff_passCoverage\")[\"yardsGained\"]\n", - " .median()\n", - " .sort_values(ascending=False)\n", - " .index\n", - ")\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", + "order = coverage_df.groupby(\"pff_passCoverage\")[\"yardsGained\"].median().sort_values(ascending=False).index\n", "\n", "sns.set_theme(style=\"ticks\")\n", - "fig, ax = plt.subplots(figsize=(8,6), dpi=150)\n", + "fig, ax = plt.subplots(figsize=(8, 6), dpi=150)\n", "# Horizontal boxplot: yards gained (x) vs coverage (y)\n", "sns.boxplot(\n", - " data=coverage_df,\n", - " x=\"yardsGained\",\n", - " y=\"pff_passCoverage\",\n", - " order=order,\n", - " whis=[0, 100],\n", - " width=0.6,\n", - " palette=\"vlag\",\n", - " hue=\"pff_passCoverage\",\n", - " ax=ax,\n", - " legend=False,\n", + "\tdata=coverage_df,\n", + "\tx=\"yardsGained\",\n", + "\ty=\"pff_passCoverage\",\n", + "\torder=order,\n", + "\twhis=[0, 100],\n", + "\twidth=0.6,\n", + "\tpalette=\"vlag\",\n", + "\thue=\"pff_passCoverage\",\n", + "\tax=ax,\n", + "\tlegend=False,\n", ")\n", "\n", "# Add each play as a dot for density context\n", - "sns.stripplot(\n", - " data=coverage_df,\n", - " x=\"yardsGained\",\n", - " y=\"pff_passCoverage\",\n", - " order=order,\n", - " size=3,\n", - " color=\".3\",\n", - " alpha=0.5,\n", - " ax=ax\n", - ")\n", + "sns.stripplot(data=coverage_df, x=\"yardsGained\", y=\"pff_passCoverage\", order=order, size=3, color=\".3\", alpha=0.5, ax=ax)\n", "\n", "# Clean up visuals\n", "ax.xaxis.grid(True)\n", @@ -979,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "id": "b97f02a9", "metadata": {}, "outputs": [ @@ -1002,46 +974,32 @@ } ], "source": [ - "pass_coverage_df = coverage_df[coverage_df['passResult'].notna()]\n", + "pass_coverage_df = coverage_df[coverage_df[\"passResult\"].notna()]\n", "print(len(pass_coverage_df))\n", "\n", - "order = (\n", - " pass_coverage_df.groupby(\"pff_passCoverage\")[\"yardsGained\"]\n", - " .median()\n", - " .sort_values(ascending=False)\n", - " .index\n", - ")\n", + "order = pass_coverage_df.groupby(\"pff_passCoverage\")[\"yardsGained\"].median().sort_values(ascending=False).index\n", "\n", - "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "\n", "sns.set_theme(style=\"ticks\")\n", - "fig, ax = plt.subplots(figsize=(8,6), dpi=150)\n", + "fig, ax = plt.subplots(figsize=(8, 6), dpi=150)\n", "# Horizontal boxplot: yards gained (x) vs coverage (y)\n", "sns.boxplot(\n", - " data=pass_coverage_df,\n", - " x=\"yardsGained\",\n", - " y=\"pff_passCoverage\",\n", - " order=order,\n", - " whis=[0, 100],\n", - " width=0.6,\n", - " palette=\"vlag\",\n", - " hue=\"pff_passCoverage\",\n", - " ax=ax,\n", - " legend=False,\n", + "\tdata=pass_coverage_df,\n", + "\tx=\"yardsGained\",\n", + "\ty=\"pff_passCoverage\",\n", + "\torder=order,\n", + "\twhis=[0, 100],\n", + "\twidth=0.6,\n", + "\tpalette=\"vlag\",\n", + "\thue=\"pff_passCoverage\",\n", + "\tax=ax,\n", + "\tlegend=False,\n", ")\n", "\n", "# Add each play as a dot for density context\n", - "sns.stripplot(\n", - " data=pass_coverage_df,\n", - " x=\"yardsGained\",\n", - " y=\"pff_passCoverage\",\n", - " order=order,\n", - " size=3,\n", - " color=\".3\",\n", - " alpha=0.5,\n", - " ax=ax\n", - ")\n", + "sns.stripplot(data=pass_coverage_df, x=\"yardsGained\", y=\"pff_passCoverage\", order=order, size=3, color=\".3\", alpha=0.5, ax=ax)\n", "\n", "# Clean up visuals\n", "ax.xaxis.grid(True)\n", @@ -1061,7 +1019,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "id": "94b904bf", "metadata": {}, "outputs": [ @@ -1098,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "id": "a51f2f5d", "metadata": {}, "outputs": [ @@ -1130,13 +1088,9 @@ } ], "source": [ - "location_data_df = location_data_df.merge(\n", - " games_df[[\"gameId\", \"week\"]],\n", - " on=\"gameId\",\n", - " how=\"left\"\n", - " )\n", + "location_data_df = location_data_df.merge(games_df[[\"gameId\", \"week\"]], on=\"gameId\", how=\"left\")\n", "\n", - "print(location_data_df.head())\n" + "print(location_data_df.head())" ] } ], diff --git a/notebooks/predictions.ipynb b/notebooks/predictions.ipynb index 55f17f1..1811e06 100644 --- a/notebooks/predictions.ipynb +++ b/notebooks/predictions.ipynb @@ -7,11 +7,10 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "pd.options.mode.chained_assignment = None\n", - "import seaborn as sns" + "import pandas as pd\n", + "\n", + "pd.options.mode.chained_assignment = None" ] }, { @@ -24,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "e1b3a2c1", "metadata": {}, "outputs": [ @@ -98,14 +97,13 @@ "predictions = []\n", "\n", "for week_eval in [9]:\n", - "\n", - " preds_week = pd.read_csv(f\"/home/sam/repos/hobby-repos/nfl/data/processed/tracking_week_{week_eval}_preds.csv\")\n", - " print(preds_week.head())\n", - " predictions.append(preds_week)\n", - " print(f\"Finished processing week {week_eval}...\")\n", + "\tpreds_week = pd.read_csv(f\"/home/sam/repos/hobby-repos/nfl/data/inference/tracking_week_{week_eval}_preds.csv\")\n", + "\tprint(preds_week.head())\n", + "\tpredictions.append(preds_week)\n", + "\tprint(f\"Finished processing week {week_eval}...\")\n", "\n", "predictions_df = pd.concat(predictions, ignore_index=True)\n", - "predictions_df['base_correct'] = predictions_df['pred'] == predictions_df['actual']\n", + "predictions_df[\"base_correct\"] = predictions_df[\"pred\"] == predictions_df[\"actual\"]\n", "print(predictions_df.head())" ] }, @@ -164,43 +162,29 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", - "from matplotlib.ticker import MultipleLocator, AutoMinorLocator\n", + "from matplotlib.ticker import AutoMinorLocator, MultipleLocator\n", "\n", "# Take 15s before snap and 1.5s post snap then create a new human readable column\n", - "week_9_df = predictions_df[\n", - " (predictions_df['week'] == 9)\n", - " & (predictions_df['frames_from_snap'] < 15)\n", - " & (predictions_df['frames_from_snap'] > -150)\n", - "].copy()\n", - "week_9_df['seconds_from_snap'] = week_9_df['frames_from_snap'] / 10\n", + "week_9_df = predictions_df[(predictions_df[\"week\"] == 9) & (predictions_df[\"frames_from_snap\"] < 15) & (predictions_df[\"frames_from_snap\"] > -150)].copy()\n", + "week_9_df[\"seconds_from_snap\"] = week_9_df[\"frames_from_snap\"] / 10\n", "\n", "# Group all the rows of the same time step together then take the mean of those rows (1 or 0) to yield accuracy\n", - "accuracy_by_frame = week_9_df.groupby('seconds_from_snap')['base_correct'].mean()\n", + "accuracy_by_frame = week_9_df.groupby(\"seconds_from_snap\")[\"base_correct\"].mean()\n", "\n", "# Colors (NGS-ish palette)\n", - "BG = \"#0b1736\" # dark navy\n", - "AX_BG = \"#121e45\" # panel navy\n", - "GRID = \"#3b4268\" # grid gray-blue\n", - "LINE = \"#00e28e\" # neon green\n", - "ACCENT = \"#39c0ff\" # cyan for highlights\n", - "TEXT = \"#dfe6ff\" # off-white text\n", - "SUBT = \"#9aa5d1\" # subdued text\n", + "BG = \"#0b1736\" # dark navy\n", + "AX_BG = \"#121e45\" # panel navy\n", + "GRID = \"#3b4268\" # grid gray-blue\n", + "LINE = \"#00e28e\" # neon green\n", + "ACCENT = \"#39c0ff\" # cyan for highlights\n", + "TEXT = \"#dfe6ff\" # off-white text\n", + "SUBT = \"#9aa5d1\" # subdued text\n", "\n", "fig, ax = plt.subplots(figsize=(10, 6), dpi=120, facecolor=BG)\n", "ax.set_facecolor(AX_BG)\n", "\n", "# Plot\n", - "ax.plot(\n", - " accuracy_by_frame.index,\n", - " accuracy_by_frame.values,\n", - " marker='o',\n", - " linestyle='-',\n", - " linewidth=2.8,\n", - " markersize=5,\n", - " color=LINE,\n", - " label='Accuracy'\n", - ")\n", + "ax.plot(accuracy_by_frame.index, accuracy_by_frame.values, marker=\"o\", linestyle=\"-\", linewidth=2.8, markersize=5, color=LINE, label=\"Accuracy\")\n", "\n", "\n", "# Grid + ticks (match animation)\n", @@ -221,13 +205,13 @@ "\n", "# Tick colors\n", "ax.yaxis.set_major_locator(MultipleLocator(0.02)) # tick every 0.02\n", - "ax.tick_params(axis='x', colors=TEXT)\n", - "ax.tick_params(axis='y', colors=TEXT)\n", + "ax.tick_params(axis=\"x\", colors=TEXT)\n", + "ax.tick_params(axis=\"y\", colors=TEXT)\n", "\n", "# Legend styling\n", "legend = ax.legend(facecolor=AX_BG, edgecolor=GRID)\n", "for text in legend.get_texts():\n", - " text.set_color(SUBT)\n", + "\ttext.set_color(SUBT)\n", "\n", "fig.tight_layout()\n", "plt.show()" @@ -307,19 +291,23 @@ ], "source": [ "# Get a df that is only pre snap and pass attempts, also that is sorted by frames from snap\n", - "pre_snap_df = predictions_df[(predictions_df['frames_from_snap'] < 0) & (predictions_df['passAttempt'] == 1)].copy()\n", - "pre_snap_df = pre_snap_df.sort_values(['gameId', 'playId', 'frames_from_snap'])\n", + "pre_snap_df = predictions_df[(predictions_df[\"frames_from_snap\"] < 0) & (predictions_df[\"passAttempt\"] == 1)].copy()\n", + "pre_snap_df = pre_snap_df.sort_values([\"gameId\", \"playId\", \"frames_from_snap\"])\n", "\n", "# For each play, get the min (earliest frame) and max (latest pre-snap frame) man_prob\n", - "change_df = pre_snap_df.groupby(['gameId', 'playId']).agg(\n", - " man_prob_start=('man_prob', 'first'), # Assign first value of man probability to man_prob_start\n", - " man_prob_end=('man_prob', 'last') # Assign last value of man probability to man_prob end\n", - ").reset_index()\n", + "change_df = (\n", + "\tpre_snap_df.groupby([\"gameId\", \"playId\"])\n", + "\t.agg(\n", + "\t\tman_prob_start=(\"man_prob\", \"first\"), # Assign first value of man probability to man_prob_start\n", + "\t\tman_prob_end=(\"man_prob\", \"last\"), # Assign last value of man probability to man_prob end\n", + "\t)\n", + "\t.reset_index()\n", + ")\n", "\n", "# Compute the largest change and create a view that is this sorted\n", - "change_df['man_prob_change'] = change_df['man_prob_end'] - change_df['man_prob_start']\n", - "largest_man_prob_increase = change_df.sort_values('man_prob_change', ascending=False)\n", - "largest_man_prob_increase = largest_man_prob_increase[largest_man_prob_increase['man_prob_end'] > 0.8]\n", + "change_df[\"man_prob_change\"] = change_df[\"man_prob_end\"] - change_df[\"man_prob_start\"]\n", + "largest_man_prob_increase = change_df.sort_values(\"man_prob_change\", ascending=False)\n", + "largest_man_prob_increase = largest_man_prob_increase[largest_man_prob_increase[\"man_prob_end\"] > 0.8]\n", "top_50_plays = largest_man_prob_increase.head(50)\n", "print(top_50_plays.head(50))" ] @@ -370,7 +358,7 @@ } ], "source": [ - "plays_df = pd.read_parquet('/home/sam/repos/hobby-repos/nfl/data/parquet/plays.parquet')\n", + "plays_df = pd.read_parquet(\"/home/sam/repos/hobby-repos/nfl/data/parquet/plays.parquet\")\n", "print(plays_df.columns)" ] }, @@ -420,11 +408,7 @@ } ], "source": [ - "merged_df = predictions_df.merge(\n", - " plays_df,\n", - " on=['gameId', 'playId'],\n", - " how='left'\n", - ")\n", + "merged_df = predictions_df.merge(plays_df, on=[\"gameId\", \"playId\"], how=\"left\")\n", "print(merged_df.columns)" ] }, @@ -444,100 +428,88 @@ "outputs": [], "source": [ "def animate_play(game_id, play_id, quarter, play_description, actual_coverage, specific_coverage):\n", - " import numpy as np\n", - " import matplotlib.pyplot as plt\n", - " import matplotlib.animation as animation\n", - " from matplotlib.ticker import MultipleLocator, AutoMinorLocator\n", + "\timport matplotlib.animation as animation\n", + "\timport matplotlib.pyplot as plt\n", + "\tfrom matplotlib.ticker import AutoMinorLocator, MultipleLocator\n", "\n", - " # Create the unique id for lookup in our df\n", - " uuid = f\"{game_id}_{play_id}\"\n", + "\t# Create the unique id for lookup in our df\n", + "\tuuid = f\"{game_id}_{play_id}\"\n", "\n", - " # Create the plot df\n", - " plot_df = week_9_df[\n", - " (week_9_df['uniqueId'] == uuid) &\n", - " (week_9_df['frames_from_snap'] < 300) &\n", - " (week_9_df['frames_from_snap'] > -200)\n", - " ].copy()\n", - " plot_df['seconds_from_snap'] = plot_df['frames_from_snap'] / 10 # Create human readable seconds (frames are at 10 Hz)\n", - " plot_df = plot_df.sort_values('seconds_from_snap') # Sort for seconds from snap\n", - " plot_df = plot_df.dropna(subset=['man_prob']) # Drop rows with missing y to avoid NaN issues\n", - " plot_df = plot_df.iloc[::10, :].reset_index(drop=True) # Downsample (since frame logging at 10 Hz, logging at 1 Hz)\n", - " if plot_df.empty:\n", - " print(f\"[skip] No frames for {uuid}\")\n", - " return\n", + "\t# Create the plot df\n", + "\tplot_df = week_9_df[(week_9_df[\"uniqueId\"] == uuid) & (week_9_df[\"frames_from_snap\"] < 300) & (week_9_df[\"frames_from_snap\"] > -200)].copy()\n", + "\tplot_df[\"seconds_from_snap\"] = plot_df[\"frames_from_snap\"] / 10 # Create human readable seconds (frames are at 10 Hz)\n", + "\tplot_df = plot_df.sort_values(\"seconds_from_snap\") # Sort for seconds from snap\n", + "\tplot_df = plot_df.dropna(subset=[\"man_prob\"]) # Drop rows with missing y to avoid NaN issues\n", + "\tplot_df = plot_df.iloc[::10, :].reset_index(drop=True) # Downsample (since frame logging at 10 Hz, logging at 1 Hz)\n", + "\tif plot_df.empty:\n", + "\t\tprint(f\"[skip] No frames for {uuid}\")\n", + "\t\treturn\n", "\n", - " # Push to numpy\n", - " x_all = plot_df['seconds_from_snap'].to_numpy()\n", - " y_all = plot_df['man_prob'].to_numpy()\n", + "\t# Push to numpy\n", + "\tx_all = plot_df[\"seconds_from_snap\"].to_numpy()\n", + "\ty_all = plot_df[\"man_prob\"].to_numpy()\n", "\n", - " # Colors (NGS-ish palette)\n", - " BG = \"#0b1736\" # dark navy\n", - " AX_BG = \"#121e45\" # panel navy\n", - " GRID = \"#3b4268\" # grid gray-blue\n", - " LINE = \"#00e28e\" # neon green\n", - " ACCENT = \"#39c0ff\" # cyan for highlights\n", - " TEXT = \"#dfe6ff\" # off-white text\n", - " SUBT = \"#9aa5d1\" # subdued text\n", + "\t# Colors (NGS-ish palette)\n", + "\tBG = \"#0b1736\" # dark navy\n", + "\tAX_BG = \"#121e45\" # panel navy\n", + "\tGRID = \"#3b4268\" # grid gray-blue\n", + "\tLINE = \"#00e28e\" # neon green\n", + "\tACCENT = \"#39c0ff\" # cyan for highlights\n", + "\tTEXT = \"#dfe6ff\" # off-white text\n", + "\tSUBT = \"#9aa5d1\" # subdued text\n", "\n", - " # Create plot\n", - " fig, ax = plt.subplots(figsize=(10, 6), dpi=120, facecolor=BG)\n", - " ax.set_facecolor(AX_BG)\n", - " (line,) = ax.plot([], [], lw=2.8, color=LINE, label=\"Man Probability\")\n", - " dot = ax.plot([], [], marker=\"o\", markersize=6, color=ACCENT, lw=0)[0]\n", - " txt = ax.text(0.99, 0.98, \"\", transform=ax.transAxes, ha=\"right\", va=\"top\", color=SUBT, fontsize=10)\n", - " ax.set_xlim(x_all.min(), x_all.max())\n", - " ax.set_ylim(0, 1)\n", - " ax.yaxis.set_major_locator(MultipleLocator(0.1))\n", - " ax.grid(True, which=\"major\", color=GRID, linewidth=1.0, alpha=0.6)\n", - " ax.grid(True, which=\"minor\", color=GRID, linewidth=0.6, alpha=0.35)\n", - " ax.xaxis.set_minor_locator(AutoMinorLocator(4))\n", - " ax.yaxis.set_minor_locator(AutoMinorLocator(2))\n", + "\t# Create plot\n", + "\tfig, ax = plt.subplots(figsize=(10, 6), dpi=120, facecolor=BG)\n", + "\tax.set_facecolor(AX_BG)\n", + "\t(line,) = ax.plot([], [], lw=2.8, color=LINE, label=\"Man Probability\")\n", + "\tdot = ax.plot([], [], marker=\"o\", markersize=6, color=ACCENT, lw=0)[0]\n", + "\ttxt = ax.text(0.99, 0.98, \"\", transform=ax.transAxes, ha=\"right\", va=\"top\", color=SUBT, fontsize=10)\n", + "\tax.set_xlim(x_all.min(), x_all.max())\n", + "\tax.set_ylim(0, 1)\n", + "\tax.yaxis.set_major_locator(MultipleLocator(0.1))\n", + "\tax.grid(True, which=\"major\", color=GRID, linewidth=1.0, alpha=0.6)\n", + "\tax.grid(True, which=\"minor\", color=GRID, linewidth=0.6, alpha=0.35)\n", + "\tax.xaxis.set_minor_locator(AutoMinorLocator(4))\n", + "\tax.yaxis.set_minor_locator(AutoMinorLocator(2))\n", "\n", - " # Create title text\n", - " coverage = \"Man\" if actual_coverage == 1 else \"Zone\"\n", - " title_text = f\"Q{quarter}: {play_description}\\n Actual Coverage: {coverage} - {specific_coverage}\\n\"\n", + "\t# Create title text\n", + "\tcoverage = \"Man\" if actual_coverage == 1 else \"Zone\"\n", + "\ttitle_text = f\"Q{quarter}: {play_description}\\n Actual Coverage: {coverage} - {specific_coverage}\\n\"\n", "\n", - " # Continued formatting\n", - " ax.set_title(title_text, loc=\"left\", pad=8, color=TEXT, fontsize=10)\n", - " ax.set_xlabel(\"Seconds from Snap\", color=TEXT, fontweight=\"bold\")\n", - " ax.set_ylabel(\"Man Probability\", color=TEXT, fontweight=\"bold\")\n", - " ax.axvline(0.0, color=ACCENT, linestyle=\"--\", linewidth=1.2, alpha=0.9)\n", + "\t# Continued formatting\n", + "\tax.set_title(title_text, loc=\"left\", pad=8, color=TEXT, fontsize=10)\n", + "\tax.set_xlabel(\"Seconds from Snap\", color=TEXT, fontweight=\"bold\")\n", + "\tax.set_ylabel(\"Man Probability\", color=TEXT, fontweight=\"bold\")\n", + "\tax.axvline(0.0, color=ACCENT, linestyle=\"--\", linewidth=1.2, alpha=0.9)\n", "\n", - " # Make tick labels match dark theme\n", - " ax.tick_params(axis='x', colors=TEXT)\n", - " ax.tick_params(axis='y', colors=TEXT)\n", + "\t# Make tick labels match dark theme\n", + "\tax.tick_params(axis=\"x\", colors=TEXT)\n", + "\tax.tick_params(axis=\"y\", colors=TEXT)\n", "\n", - " ##########################################################\n", - " # Animation\n", - " ##########################################################\n", - " def init():\n", - " line.set_data([], [])\n", - " dot.set_data([], [])\n", - " txt.set_text(\"\")\n", - " return (line, dot, txt)\n", + "\t##########################################################\n", + "\t# Animation\n", + "\t##########################################################\n", + "\tdef init():\n", + "\t\tline.set_data([], [])\n", + "\t\tdot.set_data([], [])\n", + "\t\ttxt.set_text(\"\")\n", + "\t\treturn (line, dot, txt)\n", "\n", - " def update(i):\n", - " # i goes from 0..len(x_all)-1\n", - " x = x_all[:i+1] # include current frame\n", - " y = y_all[:i+1]\n", - " line.set_data(x, y)\n", - " # IMPORTANT: wrap scalars as sequences\n", - " dot.set_data([x[-1]], [y[-1]])\n", - " txt.set_text(f\"t = {x[-1]:.2f}s P(man) = {y[-1]:.2f}\")\n", - " return (line, dot, txt)\n", + "\tdef update(i):\n", + "\t\t# i goes from 0..len(x_all)-1\n", + "\t\tx = x_all[: i + 1] # include current frame\n", + "\t\ty = y_all[: i + 1]\n", + "\t\tline.set_data(x, y)\n", + "\t\t# IMPORTANT: wrap scalars as sequences\n", + "\t\tdot.set_data([x[-1]], [y[-1]])\n", + "\t\ttxt.set_text(f\"t = {x[-1]:.2f}s P(man) = {y[-1]:.2f}\")\n", + "\t\treturn (line, dot, txt)\n", "\n", - " ani = animation.FuncAnimation(\n", - " fig, update, frames=len(x_all),\n", - " init_func=init, blit=True, interval=1, repeat=False\n", - " )\n", + "\tani = animation.FuncAnimation(fig, update, frames=len(x_all), init_func=init, blit=True, interval=1, repeat=False)\n", "\n", - " fig.tight_layout()\n", - " ani.save(\n", - " f\"play_{uuid}.gif\",\n", - " writer=\"pillow\",\n", - " fps=60\n", - " )\n", - " plt.close(fig)" + "\tfig.tight_layout()\n", + "\tani.save(f\"play_{uuid}.gif\", writer=\"pillow\", fps=60)\n", + "\tplt.close(fig)" ] }, { @@ -556,17 +528,17 @@ ], "source": [ "for idx, row in top_50_plays.iterrows():\n", - " play = merged_df[(merged_df['playId'] == row['playId']) & (merged_df['gameId'] == row['gameId'])]\n", - " if play['passResult'].values[0] == 'C':\n", - " print(f\"gameId: {play['gameId'].iloc[0]}, playId: {play['playId'].iloc[0]}, Q: {play['quarter'].iloc[0]}, {play['playDescription'].iloc[0]}\")\n", - " animate_play(\n", - " game_id=play['gameId'].iloc[0],\n", - " play_id=play['playId'].iloc[0],\n", - " quarter=play['quarter'].iloc[0],\n", - " play_description=play['playDescription'].iloc[0],\n", - " actual_coverage=play['actual'].iloc[0],\n", - " specific_coverage=play['pff_passCoverage'].iloc[0],\n", - " )" + "\tplay = merged_df[(merged_df[\"playId\"] == row[\"playId\"]) & (merged_df[\"gameId\"] == row[\"gameId\"])]\n", + "\tif play[\"passResult\"].values[0] == \"C\":\n", + "\t\tprint(f\"gameId: {play['gameId'].iloc[0]}, playId: {play['playId'].iloc[0]}, Q: {play['quarter'].iloc[0]}, {play['playDescription'].iloc[0]}\")\n", + "\t\tanimate_play(\n", + "\t\t\tgame_id=play[\"gameId\"].iloc[0],\n", + "\t\t\tplay_id=play[\"playId\"].iloc[0],\n", + "\t\t\tquarter=play[\"quarter\"].iloc[0],\n", + "\t\t\tplay_description=play[\"playDescription\"].iloc[0],\n", + "\t\t\tactual_coverage=play[\"actual\"].iloc[0],\n", + "\t\t\tspecific_coverage=play[\"pff_passCoverage\"].iloc[0],\n", + "\t\t)" ] } ], diff --git a/notebooks/train_lstm.ipynb b/notebooks/train_lstm.ipynb index 98e9235..3cd7479 100644 --- a/notebooks/train_lstm.ipynb +++ b/notebooks/train_lstm.ipynb @@ -53,12 +53,11 @@ "# Load data\n", "#################################################################\n", "\n", - "import os\n", "from common.data_loader import RawDataLoader\n", "\n", "# Get raw data\n", "loader = RawDataLoader()\n", - "games_df, plays_df, players_df, location_data_df = loader.get_data(weeks=[week for week in range (1,10)])\n", + "games_df, plays_df, players_df, location_data_df = loader.get_data(weeks=[week for week in range(1, 10)])\n", "\n", "print(location_data_df.head(10))" ] @@ -108,33 +107,33 @@ "# Find the starting plays\n", "print(\"Filtering data...\")\n", "original_play_length = len(filtered_plays_df)\n", - "print(f'Total plays: {original_play_length}')\n", + "print(f\"Total plays: {original_play_length}\")\n", "\n", "# Filter out penalties\n", - "filtered_plays_df = filtered_plays_df[filtered_plays_df['playNullifiedByPenalty'] == 'N']\n", + "filtered_plays_df = filtered_plays_df[filtered_plays_df[\"playNullifiedByPenalty\"] == \"N\"]\n", "# Filter out rows with 'PENALTY' in the 'playDescription' column\n", - "filtered_plays_df = filtered_plays_df[~filtered_plays_df['playDescription'].str.contains(\"PENALTY\", na=False)]\n", - "print(f'Total plays after filtering out penalties: {len(filtered_plays_df)}')\n", + "filtered_plays_df = filtered_plays_df[~filtered_plays_df[\"playDescription\"].str.contains(\"PENALTY\", na=False)]\n", + "print(f\"Total plays after filtering out penalties: {len(filtered_plays_df)}\")\n", "\n", "# Filter down to valid Man or Zone defensive play calls\n", - "filtered_plays_df = filtered_plays_df[filtered_plays_df['pff_manZone'].isin(['Man', 'Zone'])]\n", - "print(f'Total plays after filtering to valid Man or Zone classifications: {len(filtered_plays_df)}')\n", + "filtered_plays_df = filtered_plays_df[filtered_plays_df[\"pff_manZone\"].isin([\"Man\", \"Zone\"])]\n", + "print(f\"Total plays after filtering to valid Man or Zone classifications: {len(filtered_plays_df)}\")\n", "\n", "# Filter for only rows that indicate a pass play\n", - "filtered_plays_df = filtered_plays_df[filtered_plays_df['passResult'].notna()]\n", - "print(f'Total plays after filtering to only pass plays: {len(filtered_plays_df)}')\n", + "filtered_plays_df = filtered_plays_df[filtered_plays_df[\"passResult\"].notna()]\n", + "print(f\"Total plays after filtering to only pass plays: {len(filtered_plays_df)}\")\n", "\n", "# Filter for only plays where the win probablity isn't lopsided (between 0.2 and 0.8)\n", - "filtered_plays_df = filtered_plays_df[(filtered_plays_df['preSnapHomeTeamWinProbability'] > 0.1) & (filtered_plays_df['preSnapHomeTeamWinProbability'] < 0.9)]\n", - "print(f'Total plays after filtering out garbage time: {len(filtered_plays_df)}')\n", + "filtered_plays_df = filtered_plays_df[(filtered_plays_df[\"preSnapHomeTeamWinProbability\"] > 0.1) & (filtered_plays_df[\"preSnapHomeTeamWinProbability\"] < 0.9)]\n", + "print(f\"Total plays after filtering out garbage time: {len(filtered_plays_df)}\")\n", "\n", "# Filter for only third down or fourth down plays\n", "# filtered_plays_df = filtered_plays_df[filtered_plays_df['down'].isin([3, 4])]\n", "# print(f'Total plays after filtering for 3rd or 4th down: {len(filtered_plays_df)}')\n", "\n", "# Filter for plays that are in our gameIds (in location data df)\n", - "filtered_plays_df = filtered_plays_df[filtered_plays_df['gameId'].isin(location_data_df['gameId'].unique())]\n", - "print(f'Total plays after making sure they are in our location data: {len(filtered_plays_df)}')\n", + "filtered_plays_df = filtered_plays_df[filtered_plays_df[\"gameId\"].isin(location_data_df[\"gameId\"].unique())]\n", + "print(f\"Total plays after making sure they are in our location data: {len(filtered_plays_df)}\")\n", "\n", "print(filtered_plays_df.columns)" ] @@ -164,12 +163,12 @@ "# Cut down df to only cols we care about\n", "#################################################################\n", "# Cut down to columns we care about\n", - "keep_cols_from_plays = ['gameId', 'playId', 'possessionTeam', 'defensiveTeam', 'pff_manZone']\n", + "keep_cols_from_plays = [\"gameId\", \"playId\", \"possessionTeam\", \"defensiveTeam\", \"pff_manZone\"]\n", "filtered_plays_df = filtered_plays_df.loc[:, keep_cols_from_plays].drop_duplicates()\n", "\n", "# Make sure we don't have any NAs in this cut down col df\n", "filtered_plays_df.dropna()\n", - "print(f'Total plays after cutting down to our cols and dropping NAs: {len(filtered_plays_df)}')\n", + "print(f\"Total plays after cutting down to our cols and dropping NAs: {len(filtered_plays_df)}\")\n", "\n", "# Print total plays\n", "print(filtered_plays_df.head())" @@ -205,23 +204,12 @@ "#################################################################\n", "# Create merged df that has gameId, playId, frameID all before SNAP, with x, y, and offense/defense\n", "#################################################################\n", - "import pandas as pd\n", "import numpy as np\n", + "import pandas as pd\n", "\n", "# Create a copy of the location tracking data, cut it down to columns we care about\n", "loc_trimmed_df = location_data_df.copy()\n", - "keep_cols = [\n", - " 'gameId',\n", - " 'playId',\n", - " 'nflId',\n", - " 'frameId',\n", - " 'frameType',\n", - " 'club',\n", - " 'x',\n", - " 'y',\n", - " 's',\n", - " 'a'\n", - "]\n", + "keep_cols = [\"gameId\", \"playId\", \"nflId\", \"frameId\", \"frameType\", \"club\", \"x\", \"y\", \"s\", \"a\"]\n", "loc_trimmed_df = location_data_df.loc[:, keep_cols]\n", "\n", "# Cut down location tracking data copy to only before the snap and where the team isn't valid\n", @@ -261,16 +249,16 @@ "# Label offense/defense, cut down df further, and sort\n", "#################################################################\n", "# Merge the two datasets such that we can have the possession and defensive team for each row\n", - "merged_df = pd.merge(filtered_plays_df, loc_trimmed_df, on=['gameId', 'playId'], how='inner')\n", + "merged_df = pd.merge(filtered_plays_df, loc_trimmed_df, on=[\"gameId\", \"playId\"], how=\"inner\")\n", "\n", "# Tag the \"side\" of the player for each row (that being \"off\" or \"def\")\n", - "merged_df['side'] = np.where(merged_df['club'] == merged_df['possessionTeam'], 'off', 'def')\n", + "merged_df[\"side\"] = np.where(merged_df[\"club\"] == merged_df[\"possessionTeam\"], \"off\", \"def\")\n", "\n", "# Drop some columns we don't need anymore\n", - "merged_df = merged_df.drop(['possessionTeam', 'defensiveTeam', 'club', 'frameType'], axis=1)\n", + "merged_df = merged_df.drop([\"possessionTeam\", \"defensiveTeam\", \"club\", \"frameType\"], axis=1)\n", "\n", "# Sort for deterministic frame ordering\n", - "merged_df = merged_df.sort_values(['gameId','playId','frameId'])\n", + "merged_df = merged_df.sort_values([\"gameId\", \"playId\", \"frameId\"])\n", "\n", "# Let's see what we have\n", "print(merged_df.head())" @@ -294,9 +282,7 @@ "#################################################################\n", "# Decide the target sequence length using the median number of frames per play\n", "#################################################################\n", - "frame_counts = (merged_df\n", - " .groupby(['gameId','playId'])['frameId']\n", - " .nunique())\n", + "frame_counts = merged_df.groupby([\"gameId\", \"playId\"])[\"frameId\"].nunique()\n", "min_frames = int(np.percentile(frame_counts.values, 10))\n", "print(f\"Using plays that have above {min_frames} frames\")" ] @@ -322,88 +308,80 @@ "# Built dataset where frame is only min frames and each side has exactly 11 players\n", "#################################################################\n", "def exactly_eleven_per_side(play_df: pd.DataFrame) -> bool:\n", - " return (\n", - " play_df.loc[play_df.side == 'off', 'nflId'].nunique() == 11 and\n", - " play_df.loc[play_df.side == 'def', 'nflId'].nunique() == 11\n", - " )\n", + "\treturn play_df.loc[play_df.side == \"off\", \"nflId\"].nunique() == 11 and play_df.loc[play_df.side == \"def\", \"nflId\"].nunique() == 11\n", + "\n", "\n", "def slot_order_by_left_to_right(play_df: pd.DataFrame, side: str) -> list:\n", - " side_df = play_df.loc[play_df[\"side\"] == side]\n", - " stats = (side_df\n", - " .groupby('nflId', as_index=True)[['x','y']]\n", - " .median()\n", - " .rename(columns={'x':'x_med','y':'y_med'})\n", - " .sort_values(['x_med','y_med']))\n", - " return stats.index.tolist() # list of sorted NFL player ids for this play to determine median x --> y player locs\n", + "\tside_df = play_df.loc[play_df[\"side\"] == side]\n", + "\tstats = side_df.groupby(\"nflId\", as_index=True)[[\"x\", \"y\"]].median().rename(columns={\"x\": \"x_med\", \"y\": \"y_med\"}).sort_values([\"x_med\", \"y_med\"])\n", + "\treturn stats.index.tolist() # list of sorted NFL player ids for this play to determine median x --> y player locs\n", + "\n", "\n", "def build_side_feature_cube(play_df: pd.DataFrame, side: str, frames: np.ndarray, feature_cols: tuple) -> np.ndarray:\n", - " \"\"\"\n", - " For one side ('off' or 'def'), build a 3D tensor:\n", - " (T, 11, F) where F=len(feature_cols), with rows aligned to `frames`\n", - " and slots 0..10 as columns. Missing -> NaN.\n", - " \"\"\"\n", - "\n", - " # pivot to (frames x slots) for x and y, fill missing with NaN, then stack → (min_frames, 11, F)\n", - " side_df = play_df.loc[play_df[\"side\"] == side]\n", - "\n", - " mats = []\n", - " for col in feature_cols:\n", - " # Takes the long df and goes from frameId, slot, x, y as cols to:\n", - " # slot, 0, 1, 2 as cols ... with frameId 1, frameId 2... etc as the rows....shape is (min_frames, 11)\n", - " mat = side_df.pivot_table(index=\"frameId\", columns=\"slot\", values=col)\n", - " # It's possible certain players don't have exact tracking data throughout (ie one player has frame 10 and 12 but not frame 11), this will end up breaking our shape and cause issues downstream for model training\n", - " # So this forces the matrix to have for each frame \n", - " mat = mat.reindex(index=frames, columns=range(11), fill_value=np.nan)\n", - " mats.append(mat.to_numpy()) # shape: (T, 11)\n", - "\n", - " # stack features on the last axis to shape: (T, 11, F)\n", - " return np.stack(mats, axis=-1)\n", + "\t\"\"\"\n", + "\tFor one side ('off' or 'def'), build a 3D tensor:\n", + "\t (T, 11, F) where F=len(feature_cols), with rows aligned to `frames`\n", + "\t and slots 0..10 as columns. Missing -> NaN.\n", + "\t\"\"\"\n", + "\n", + "\t# pivot to (frames x slots) for x and y, fill missing with NaN, then stack → (min_frames, 11, F)\n", + "\tside_df = play_df.loc[play_df[\"side\"] == side]\n", + "\n", + "\tmats = []\n", + "\tfor col in feature_cols:\n", + "\t\t# Takes the long df and goes from frameId, slot, x, y as cols to:\n", + "\t\t# slot, 0, 1, 2 as cols ... with frameId 1, frameId 2... etc as the rows....shape is (min_frames, 11)\n", + "\t\tmat = side_df.pivot_table(index=\"frameId\", columns=\"slot\", values=col)\n", + "\t\t# It's possible certain players don't have exact tracking data throughout (ie one player has frame 10 and 12 but not frame 11), this will end up breaking our shape and cause issues downstream for model training\n", + "\t\t# So this forces the matrix to have for each frame\n", + "\t\tmat = mat.reindex(index=frames, columns=range(11), fill_value=np.nan)\n", + "\t\tmats.append(mat.to_numpy()) # shape: (T, 11)\n", + "\n", + "\t# stack features on the last axis to shape: (T, 11, F)\n", + "\treturn np.stack(mats, axis=-1)\n", + "\n", "\n", "# Init series maps\n", "off_series = {}\n", "def_series = {}\n", "\n", "# Lists to peek at later if we skip plays\n", - "skipped_wrong_player_count_list = [] # plays where offense or defense had >11 unique players\n", - "skipped_under_min_frames_list = [] # plays with fewer than min_frames\n", + "skipped_wrong_player_count_list = [] # plays where offense or defense had >11 unique players\n", + "skipped_under_min_frames_list = [] # plays with fewer than min_frames\n", "\n", "# Iterate on each play\n", - "for (game_id, play_id), play in merged_df.groupby(['gameId','playId'], sort=False):\n", - " # Skip if not 11 players\n", - " if not exactly_eleven_per_side(play):\n", - " skipped_wrong_player_count_list.append((game_id, play_id))\n", - " continue\n", - "\n", - " # Define slot maps (left→right by median x, tie-break median y)\n", - " off_slots = slot_order_by_left_to_right(play, 'off')\n", - " def_slots = slot_order_by_left_to_right(play, 'def')\n", - "\n", - " # Create a map that goes player id --> index so we can assign each player to an index as we go frame by frame\n", - " off_id2slot = {pid: i for i, pid in enumerate(off_slots)}\n", - " def_id2slot = {pid: i for i, pid in enumerate(def_slots)}\n", - "\n", - " # Assign slots (if offense use offensive map, if defense, use defensive map)\n", - " tmp = play.copy()\n", - " tmp['slot'] = np.where(\n", - " tmp['side'] == 'off',\n", - " tmp['nflId'].map(off_id2slot),\n", - " tmp['nflId'].map(def_id2slot)\n", - " )\n", - "\n", - " # Choose frame window (last min_frames frames)\n", - " frames_all = np.sort(tmp['frameId'].unique())\n", - " if frames_all.size < min_frames:\n", - " skipped_under_min_frames_list.append((game_id, play_id))\n", - " continue\n", - " frames = frames_all[-min_frames:] # Get the last min frames, so each play is consistent\n", - "\n", - " # Build offense/defense cubes: (min_frames, 11, 2) the 2 is x and y coords\n", - " feature_cols = (\"x\", \"y\", \"s\", \"a\")\n", - " off_arr = build_side_feature_cube(tmp, \"off\", frames, feature_cols)\n", - " def_arr = build_side_feature_cube(tmp, \"def\", frames, feature_cols)\n", - "\n", - " off_series[(game_id, play_id)] = off_arr\n", - " def_series[(game_id, play_id)] = def_arr\n", + "for (game_id, play_id), play in merged_df.groupby([\"gameId\", \"playId\"], sort=False):\n", + "\t# Skip if not 11 players\n", + "\tif not exactly_eleven_per_side(play):\n", + "\t\tskipped_wrong_player_count_list.append((game_id, play_id))\n", + "\t\tcontinue\n", + "\n", + "\t# Define slot maps (left→right by median x, tie-break median y)\n", + "\toff_slots = slot_order_by_left_to_right(play, \"off\")\n", + "\tdef_slots = slot_order_by_left_to_right(play, \"def\")\n", + "\n", + "\t# Create a map that goes player id --> index so we can assign each player to an index as we go frame by frame\n", + "\toff_id2slot = {pid: i for i, pid in enumerate(off_slots)}\n", + "\tdef_id2slot = {pid: i for i, pid in enumerate(def_slots)}\n", + "\n", + "\t# Assign slots (if offense use offensive map, if defense, use defensive map)\n", + "\ttmp = play.copy()\n", + "\ttmp[\"slot\"] = np.where(tmp[\"side\"] == \"off\", tmp[\"nflId\"].map(off_id2slot), tmp[\"nflId\"].map(def_id2slot))\n", + "\n", + "\t# Choose frame window (last min_frames frames)\n", + "\tframes_all = np.sort(tmp[\"frameId\"].unique())\n", + "\tif frames_all.size < min_frames:\n", + "\t\tskipped_under_min_frames_list.append((game_id, play_id))\n", + "\t\tcontinue\n", + "\tframes = frames_all[-min_frames:] # Get the last min frames, so each play is consistent\n", + "\n", + "\t# Build offense/defense cubes: (min_frames, 11, 2) the 2 is x and y coords\n", + "\tfeature_cols = (\"x\", \"y\", \"s\", \"a\")\n", + "\toff_arr = build_side_feature_cube(tmp, \"off\", frames, feature_cols)\n", + "\tdef_arr = build_side_feature_cube(tmp, \"def\", frames, feature_cols)\n", + "\n", + "\toff_series[(game_id, play_id)] = off_arr\n", + "\tdef_series[(game_id, play_id)] = def_arr\n", "\n", "print(f\"Kept plays: {len(off_series)}\")\n", "print(f\"Skipped (>11 players): {len(skipped_wrong_player_count_list)}\")\n", @@ -412,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "6bd0c4d5", "metadata": {}, "outputs": [], @@ -424,35 +402,36 @@ "import numpy as np\n", "import pandas as pd\n", "import torch\n", - "from torch.utils.data import Dataset\n", + "\n", "\n", "def impute_timewise(X_np: np.ndarray) -> np.ndarray:\n", - " \"\"\"\n", - " X_np: (T, F) with NaNs.\n", - " Impute per feature (column) along time:\n", - " 1) forward-fill if we miss a frame, assume the player stayed where he was last seen.\n", - " 2) back-fill\n", - " 3) fill remaining NaNs with column mean (0 if all NaN)\n", - " \"\"\"\n", - " df = pd.DataFrame(X_np) # (T, 11 players * 2 features = 44)\n", - "\n", - " # Copy the last known values fwd in time if there's missing NaNs\n", - " # Fill any leading NaNs that had no earlier data\n", - " # Example: [NaN, 3, 4, NaN, NaN, 7] ---> foward fill [NaN, 3, 4, 4, 4, 7] ---> backward fill [3, 3, 4, 4, 4, 7]\n", - " # If a whole column has NaNs we then fill it with 0s (only time this realistically kicks in)\n", - " df = df.ffill().bfill().fillna(0.0)\n", - " \n", - " return df.values.astype(np.float32)\n", - "\n", - "def build_plays_data_numpy(off_series, def_series, labels_dict):\n", - " X_np, y_np = [], []\n", - " for key, off_arr in off_series.items():\n", - " def_arr = def_series[key]\n", - " X_play = np.concatenate([off_arr, def_arr], axis=1).reshape(off_arr.shape[0], -1) # (T, 22 * F)\n", - " X_play = impute_timewise(X_play)\n", - " X_np.append(X_play.astype(np.float32))\n", - " y_np.append(labels_dict[key])\n", - " return X_np, np.array(y_np, dtype=int)" + "\t\"\"\"\n", + "\tX_np: (T, F) with NaNs.\n", + "\tImpute per feature (column) along time:\n", + "\t\t1) forward-fill if we miss a frame, assume the player stayed where he was last seen.\n", + "\t\t2) back-fill\n", + "\t\t3) fill remaining NaNs with column mean (0 if all NaN)\n", + "\t\"\"\"\n", + "\tdf = pd.DataFrame(X_np) # (T, 11 players * 2 features = 44)\n", + "\n", + "\t# Copy the last known values fwd in time if there's missing NaNs\n", + "\t# Fill any leading NaNs that had no earlier data\n", + "\t# Example: [NaN, 3, 4, NaN, NaN, 7] ---> foward fill [NaN, 3, 4, 4, 4, 7] ---> backward fill [3, 3, 4, 4, 4, 7]\n", + "\t# If a whole column has NaNs we then fill it with 0s (only time this realistically kicks in)\n", + "\tdf = df.ffill().bfill().fillna(0.0)\n", + "\n", + "\treturn df.values.astype(np.float32)\n", + "\n", + "\n", + "def build_plays_data_numpy(off_series: dict, def_series: dict, labels_dict: dict) -> tuple[list[np.ndarray], np.ndarray]:\n", + "\tX_np, y_np = [], []\n", + "\tfor key, off_arr in off_series.items():\n", + "\t\tdef_arr = def_series[key]\n", + "\t\tX_play = np.concatenate([off_arr, def_arr], axis=1).reshape(off_arr.shape[0], -1) # (T, 22 * F)\n", + "\t\tX_play = impute_timewise(X_play)\n", + "\t\tX_np.append(X_play.astype(np.float32))\n", + "\t\ty_np.append(labels_dict[key])\n", + "\treturn X_np, np.array(y_np, dtype=int)" ] }, { @@ -465,13 +444,13 @@ "#################################################################\n", "# Make dataloaders\n", "#################################################################\n", + "import joblib\n", "from sklearn.model_selection import train_test_split\n", - "from torch.utils.data import TensorDataset, DataLoader\n", "from sklearn.preprocessing import StandardScaler\n", - "import joblib\n", + "from torch.utils.data import DataLoader, TensorDataset\n", "\n", "# Build labels dict mapping of (gameId, playId) --> 0/1\n", - "label_map = {'Man': 1, 'Zone': 0}\n", + "label_map = {\"Man\": 1, \"Zone\": 0}\n", "labels_dict = {(r.gameId, r.playId): label_map[r.pff_manZone] for r in filtered_plays_df.itertuples()}\n", "\n", "# Get dataset (imputed, converted to tensor)\n", @@ -479,11 +458,11 @@ "\n", "# Splittys\n", "idx_train, idx_val = train_test_split(\n", - " np.arange(len(X_np)), # Create an array from 0 to x number of plays\n", - " test_size=0.2, # Choosing standard 20% for test size\n", - " random_state=42, # Life universe and everything\n", - " stratify=y_np # Says split the data while keeping same ratio of 0s and 1s in both train and validation sets\n", - " )\n", + "\tnp.arange(len(X_np)), # Create an array from 0 to x number of plays\n", + "\ttest_size=0.2, # Choosing standard 20% for test size\n", + "\trandom_state=42, # Life universe and everything\n", + "\tstratify=y_np, # Says split the data while keeping same ratio of 0s and 1s in both train and validation sets\n", + ")\n", "\n", "# Combine all frames from training plays\n", "train_stacked = np.vstack([X_np[i] for i in idx_train]) # shape: (total_train_frames, 22*F)\n", @@ -493,9 +472,11 @@ "scaler.fit(train_stacked) # computes mean_ and scale_ only on training data\n", "joblib.dump(scaler, \"plays_standard_scaler.pkl\")\n", "\n", + "\n", "def apply_scaler_to_list(X_list, idxs, scaler):\n", - " for i in idxs:\n", - " X_list[i] = scaler.transform(X_list[i])\n", + "\tfor i in idxs:\n", + "\t\tX_list[i] = scaler.transform(X_list[i])\n", + "\n", "\n", "apply_scaler_to_list(X_np, idx_train, scaler)\n", "apply_scaler_to_list(X_np, idx_val, scaler)\n", @@ -504,13 +485,10 @@ "# NOTE: X will be of shape (play_count, min_frames, 44)\n", "# NOTE: Y will be of shape (play_count, )\n", "train_ds = TensorDataset(\n", - " torch.stack([torch.from_numpy(X_np[i]).float() for i in idx_train]), # Each x is (min_frame, 22*F)\n", - " torch.from_numpy(y_np[idx_train]).long()\n", - ")\n", - "val_ds = TensorDataset(\n", - " torch.stack([torch.from_numpy(X_np[i]).float() for i in idx_val]),\n", - " torch.from_numpy(y_np[idx_val]).long()\n", + "\ttorch.stack([torch.from_numpy(X_np[i]).float() for i in idx_train]), # Each x is (min_frame, 22*F)\n", + "\ttorch.from_numpy(y_np[idx_train]).long(),\n", ")\n", + "val_ds = TensorDataset(torch.stack([torch.from_numpy(X_np[i]).float() for i in idx_val]), torch.from_numpy(y_np[idx_val]).long())\n", "\n", "# Reproducibility seeds\n", "SEED = 42\n", @@ -539,28 +517,26 @@ "#################################################################\n", "import torch.nn as nn\n", "\n", + "\n", "class LSTMClassifier(nn.Module):\n", - " def __init__(self, input_size=44, hidden_size=64, num_layers=1, dropout=0.0, bidir=False, num_classes=2):\n", - " super().__init__()\n", - " self.lstm = nn.LSTM(\n", - " input_size=input_size,\n", - " hidden_size=hidden_size,\n", - " num_layers=num_layers,\n", - " batch_first=True,\n", - " dropout=dropout if num_layers > 1 else 0.0,\n", - " bidirectional=bidir,\n", - " )\n", - " out_dim = hidden_size * (2 if bidir else 1)\n", - " self.head = nn.Sequential(\n", - " nn.LayerNorm(out_dim),\n", - " nn.Linear(out_dim, num_classes)\n", - " )\n", - "\n", - " def forward(self, x): # x: (B, T, F)\n", - " out, (h_n, c_n) = self.lstm(x) # out: (B, T, H)\n", - " last = out[:, -1, :] # use last timestep representation\n", - " logits = self.head(last) # (B, C)\n", - " return logits" + "\tdef __init__(self, input_size=44, hidden_size=64, num_layers=1, dropout=0.0, bidir=False, num_classes=2):\n", + "\t\tsuper().__init__()\n", + "\t\tself.lstm = nn.LSTM(\n", + "\t\t\tinput_size=input_size,\n", + "\t\t\thidden_size=hidden_size,\n", + "\t\t\tnum_layers=num_layers,\n", + "\t\t\tbatch_first=True,\n", + "\t\t\tdropout=dropout if num_layers > 1 else 0.0,\n", + "\t\t\tbidirectional=bidir,\n", + "\t\t)\n", + "\t\tout_dim = hidden_size * (2 if bidir else 1)\n", + "\t\tself.head = nn.Sequential(nn.LayerNorm(out_dim), nn.Linear(out_dim, num_classes))\n", + "\n", + "\tdef forward(self, x): # x: (B, T, F)\n", + "\t\tout, (h_n, c_n) = self.lstm(x) # out: (B, T, H)\n", + "\t\tlast = out[:, -1, :] # use last timestep representation\n", + "\t\tlogits = self.head(last) # (B, C)\n", + "\t\treturn logits" ] }, { @@ -783,14 +759,14 @@ "#################################################################\n", "from sklearn.utils.class_weight import compute_class_weight\n", "\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "model = LSTMClassifier(input_size=88, hidden_size=64, num_layers=3, dropout=0.0, bidir=False).to(device)\n", "\n", "# Create criterion with CE losss weighted with class weights to account for higher proportion of man coverage\n", "# Zone dominates class weighting, calc distribution then assign man a higher waiting on the CE loss\n", - "y_train = y_np[idx_train] # Slice to the training fold\n", - "classes = np.array([0, 1], dtype=int) # 0=Zone, 1=Man\n", - "w = compute_class_weight(class_weight='balanced', classes=classes, y=y_train)\n", + "y_train = y_np[idx_train] # Slice to the training fold\n", + "classes = np.array([0, 1], dtype=int) # 0=Zone, 1=Man\n", + "w = compute_class_weight(class_weight=\"balanced\", classes=classes, y=y_train)\n", "print(\"Class weights (Zone, Man):\", w)\n", "class_weights = torch.tensor(w, dtype=torch.float32, device=device)\n", "criterion = nn.CrossEntropyLoss(weight=class_weights)\n", @@ -800,26 +776,26 @@ "\n", "# Train\n", "for epoch in range(200):\n", - " # Train\n", - " model.train()\n", - " for X, y in train_loader:\n", - " X, y = X.to(device), y.to(device)\n", - " logits = model(X)\n", - " loss = criterion(logits, y)\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " # Val\n", - " model.eval()\n", - " correct = total = 0\n", - " with torch.no_grad():\n", - " for X, y in val_loader:\n", - " X, y = X.to(device), y.to(device)\n", - " pred = model(X).argmax(dim=1)\n", - " correct += (pred == y).sum().item()\n", - " total += y.numel()\n", - " print(f\"Epoch {epoch+1}: val acc = {correct/total:.3f}\")\n" + "\t# Train\n", + "\tmodel.train()\n", + "\tfor X, y in train_loader:\n", + "\t\tX, y = X.to(device), y.to(device)\n", + "\t\tlogits = model(X)\n", + "\t\tloss = criterion(logits, y)\n", + "\t\toptimizer.zero_grad()\n", + "\t\tloss.backward()\n", + "\t\toptimizer.step()\n", + "\n", + "\t# Val\n", + "\tmodel.eval()\n", + "\tcorrect = total = 0\n", + "\twith torch.no_grad():\n", + "\t\tfor X, y in val_loader:\n", + "\t\t\tX, y = X.to(device), y.to(device)\n", + "\t\t\tpred = model(X).argmax(dim=1)\n", + "\t\t\tcorrect += (pred == y).sum().item()\n", + "\t\t\ttotal += y.numel()\n", + "\tprint(f\"Epoch {epoch + 1}: val acc = {correct / total:.3f}\")" ] }, { @@ -854,11 +830,11 @@ "all_preds, all_true = [], []\n", "\n", "with torch.no_grad():\n", - " for X, y in val_loader:\n", - " X, y = X.to(device), y.to(device)\n", - " pred = model(X).argmax(dim=1)\n", - " all_preds.extend(pred.cpu().numpy())\n", - " all_true.extend(y.cpu().numpy())\n", + "\tfor X, y in val_loader:\n", + "\t\tX, y = X.to(device), y.to(device)\n", + "\t\tpred = model(X).argmax(dim=1)\n", + "\t\tall_preds.extend(pred.cpu().numpy())\n", + "\t\tall_true.extend(y.cpu().numpy())\n", "\n", "print(classification_report(all_true, all_preds, target_names=[\"Zone\", \"Man\"]))" ] @@ -884,11 +860,12 @@ "#################################################################\n", "# Viz confusion matrix\n", "#################################################################\n", - "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n", + "\n", "cm = confusion_matrix(all_true, all_preds, labels=[0, 1])\n", "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"Zone\", \"Man\"])\n", - "disp.plot(cmap='Blues', values_format='d')\n", + "disp.plot(cmap=\"Blues\", values_format=\"d\")\n", "plt.title(\"Confusion Matrix\")\n", "plt.show()" ] diff --git a/pyproject.toml b/pyproject.toml index ba270e1..568fbe2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,14 +33,18 @@ dependencies = [ "lightgbm>=4.6.0", "matplotlib>=3.10.6", "mlflow>=3.4.0", + "mypy>=1.19.0", "numpy>=2.3.3", "pandas>=2.3.2", "pip-tools>=7.5.0", "polars>=1.35.1", + "pre-commit>=4.5.0", "ray[train,tune]>=2.51.1", + "ruff>=0.14.7", "scikit-learn>=1.7.2", "seaborn>=0.13.2", "torch>=2.8.0", + "torchvision>=0.24.1", "tqdm>=4.67.1", "xgboost>=3.0.5", ] @@ -51,3 +55,29 @@ myproject-cli = "myproject.cli:main" [tool.setuptools.packages.find] where = ["src"] + +[tool.ruff] +target-version = "py312" +line-length = 200 +select = [ + "E", # general pep8 error rules (formatting) + "F", # pyflakes (logica/correctness) + "B", # bugbear (logic traps) + "I", # isort-like import rules (import org) + "UP", # pyupgrade (modern syntax) + "ANN", # type annotation rules +] +exclude = ["*.ipynb"] + +[tool.ruff.lint] +ignore = [ + "E203", # makes it compat with Black-style slicing + "E501", # ignore line length +] + +[tool.ruff.lint.isort] +known-first-party = ["nfl"] + +[tool.ruff.format] +quote-style = "double" +indent-style = "tab" diff --git a/src/__init__.py b/src/__init__.py index e69de29..ab174c1 100644 --- a/src/__init__.py +++ b/src/__init__.py @@ -0,0 +1,4 @@ +#!/usr/bin/env python +""" +Initializes the nfl package for training and inference utilities. +""" diff --git a/src/clean_data.py b/src/clean_data.py index 6916bb4..090e709 100644 --- a/src/clean_data.py +++ b/src/clean_data.py @@ -1,295 +1,320 @@ #!/usr/bin/env python - """ -Contains functions to clean and standarize data +Cleans and prepares tracking data for downstream modeling and feature creation. """ +from collections.abc import Iterable, Sequence + import numpy as np import pandas as pd -import math import torch -def rotate_direction_and_orientation(df): - """ - Rotate the direction and orientation angles so that 0° points from left to right on the field, and increasing angle goes counterclockwise - This should be done BEFORE the call to make_plays_left_to_right, because that function with compensate for the flipped angles. +def rotate_direction_and_orientation(df: pd.DataFrame) -> pd.DataFrame: + """ + Rotates direction and orientation so field left-to-right is 0 degrees. + + Inputs: + - df: Tracking rows containing original direction and orientation. + + Outputs: + - rotated_df: Dataframe with normalized direction/orientation columns. + """ + + df["o_clean"] = (-(df["o"] - 90)) % 360 + df["dir_clean"] = (-(df["dir"] - 90)) % 360 + return df + + +def make_plays_left_to_right(df: pd.DataFrame) -> pd.DataFrame: + """ + Flips plays so every snap proceeds left to right and creates cleaned columns. + + Inputs: + - df: Tracking rows with original play direction. + + Outputs: + - standardized_df: Dataframe with *_clean columns aligned left to right. + """ + + df["x_clean"] = np.where( + df["playDirection"] == "left", + 120 - df["x"], + df["x"], + ) - :param df: the aggregate dataframe created using the aggregate_data() method + df["y_clean"] = df["y"] + df["s_clean"] = df["s"] + df["a_clean"] = df["a"] + df["dis_clean"] = df["dis"] - :return df: the aggregate dataframe with orientation and direction angles rotated 90° clockwise - """ + df["o_clean"] = np.where(df["playDirection"] == "left", 180 - df["o_clean"], df["o_clean"]) - df["o_clean"] = (-(df["o"] - 90)) % 360 - df["dir_clean"] = (-(df["dir"] - 90)) % 360 + df["o_clean"] = (df["o_clean"] + 360) % 360 - return df + df["dir_clean"] = np.where(df["playDirection"] == "left", 180 - df["dir_clean"], df["dir_clean"]) + df["dir_clean"] = (df["dir_clean"] + 360) % 360 -def make_plays_left_to_right(df): + return df - """ - Flip tracking data so that all plays run from left to right. The new x, y, s, a, dis, o, and dir data - will be stored in new columns with the suffix "_clean" even if the variables do not change from their original value. - :param df: the aggregate dataframe created using the aggregate_data() method +def calculate_velocity_components(df: pd.DataFrame) -> pd.DataFrame: + """ + Derives velocity components from cleaned direction and speed. - :return df: the aggregate dataframe with the new columns such that all plays run left to right - """ + Inputs: + - df: Tracking rows with normalized direction and speed columns. - df["x_clean"] = np.where( - df["playDirection"] == "left", - 120 - df["x"], - df[ - "x" - ], # 120 because the endzones (10 yds each) are included in the ["x"] values - ) + Outputs: + - velocity_df: Dataframe with v_x and v_y components added. + """ - df["y_clean"] = df["y"] - df["s_clean"] = df["s"] - df["a_clean"] = df["a"] - df["dis_clean"] = df["dis"] + df["dir_radians"] = np.radians(df["dir_clean"]) + df["v_x"] = df["s_clean"] * np.cos(df["dir_radians"]) + df["v_y"] = df["s_clean"] * np.sin(df["dir_radians"]) + return df - df["o_clean"] = np.where( - df["playDirection"] == "left", 180 - df["o_clean"], df["o_clean"] - ) - df["o_clean"] = (df["o_clean"] + 360) % 360 # remove negative angles +def label_offense_defense_coverage(presnap_df: pd.DataFrame, plays_df: pd.DataFrame) -> pd.DataFrame: + """ + Maps PFF coverage strings to numeric labels and marks defenders. - df["dir_clean"] = np.where( - df["playDirection"] == "left", 180 - df["dir_clean"], df["dir_clean"] - ) + Inputs: + - presnap_df: Tracking rows before the snap. + - plays_df: Play metadata including pass coverage labels. - df["dir_clean"] = (df["dir_clean"] + 360) % 360 # remove negative angles + Outputs: + - labeled_df: Presnap rows with defense flag and coverage label added. + """ - return df + coverage_replacements = { + "Cover-3 Cloud Right": "Cover-3", + "Cover-3 Cloud Left": "Cover-3", + "Cover-3 Seam": "Cover-3", + "Cover-3 Double Cloud": "Cover-3", + "Cover-6 Right": "Cover-6", + "Cover 6-Left": "Cover-6", + "Cover-1 Double": "Cover-1", + } + values_to_drop = ["Miscellaneous", "Bracket", "Prevent", "Red Zone", "Goal Line"] -def calculate_velocity_components(df): - """ - Calculate the velocity components (v_x and v_y) for each row in the dataframe. + plays_df["pff_passCoverage"] = plays_df["pff_passCoverage"].replace(coverage_replacements) - :param df: the aggregate dataframe with "_clean" columns created using make_plays_left_to_right() + plays_df = plays_df.dropna(subset=["pff_passCoverage"]) + plays_df = plays_df[~plays_df["pff_passCoverage"].isin(values_to_drop)] - :return df: the dataframe with additional columns 'v_x' and 'v_y' representing the velocity components - """ + coverage_mapping = {"Cover-0": 0, "Cover-1": 1, "Cover-2": 2, "Cover-3": 3, "Quarters": 4, "2-Man": 5, "Cover-6": 6} - df["dir_radians"] = np.radians(df["dir_clean"]) + merged_df = presnap_df.merge(plays_df[["gameId", "playId", "possessionTeam", "defensiveTeam", "pff_passCoverage"]], on=["gameId", "playId"], how="left") - df["v_x"] = df["s_clean"] * np.cos(df["dir_radians"]) - df["v_y"] = df["s_clean"] * np.sin(df["dir_radians"]) + merged_df["defense"] = ((merged_df["club"] == merged_df["defensiveTeam"]) & (merged_df["club"] != "football")).astype(int) + merged_df["pff_passCoverage"] = merged_df["pff_passCoverage"].map(coverage_mapping) + merged_df.dropna(subset=["pff_passCoverage"], inplace=True) - return df + return merged_df -def label_offense_defense_coverage(presnap_df, plays_df): +def label_offense_defense_manzone(presnap_df: pd.DataFrame, plays_df: pd.DataFrame) -> pd.DataFrame: + """ + Adds man/zone numeric labels and defense flags to presnap rows. - coverage_replacements = { - 'Cover-3 Cloud Right': 'Cover-3', - 'Cover-3 Cloud Left': 'Cover-3', - 'Cover-3 Seam': 'Cover-3', - 'Cover-3 Double Cloud': 'Cover-3', - 'Cover-6 Right': 'Cover-6', - 'Cover 6-Left': 'Cover-6', - 'Cover-1 Double': 'Cover-1'} + Inputs: + - presnap_df: Tracking rows before the snap. + - plays_df: Play metadata including man/zone tags. - values_to_drop = ["Miscellaneous", "Bracket", "Prevent", "Red Zone", "Goal Line"] + Outputs: + - labeled_df: Presnap rows with defense flag and man/zone label added. + """ - plays_df['pff_passCoverage'] = plays_df['pff_passCoverage'].replace(coverage_replacements) + plays_df = plays_df.dropna(subset=["pff_manZone"]) - plays_df = plays_df.dropna(subset=['pff_passCoverage']) - plays_df = plays_df[~plays_df['pff_passCoverage'].isin(values_to_drop)] + coverage_mapping = {"Zone": 0, "Man": 1} - coverage_mapping = { - 'Cover-0': 0, - 'Cover-1': 1, - 'Cover-2': 2, - 'Cover-3': 3, - 'Quarters': 4, - '2-Man': 5, - 'Cover-6': 6 - } + merged_df = presnap_df.merge(plays_df[["gameId", "playId", "possessionTeam", "defensiveTeam", "pff_manZone"]], on=["gameId", "playId"], how="left") - merged_df = presnap_df.merge( - plays_df[['gameId', 'playId', 'possessionTeam', 'defensiveTeam', 'pff_passCoverage']], - on=['gameId', 'playId'], - how='left' - ) + merged_df["defense"] = ((merged_df["club"] == merged_df["defensiveTeam"]) & (merged_df["club"] != "football")).astype(int) - merged_df['defense'] = ((merged_df['club'] == merged_df['defensiveTeam']) & (merged_df['club'] != 'football')).astype(int) + merged_df["pff_manZone"] = merged_df["pff_manZone"].map(coverage_mapping) + merged_df.dropna(subset=["pff_manZone"], inplace=True) - merged_df['pff_passCoverage'] = merged_df['pff_passCoverage'].map(coverage_mapping) - merged_df.dropna(subset=['pff_passCoverage'], inplace=True) + return merged_df - return merged_df +def label_offense_defense_formation(presnap_df: pd.DataFrame, plays_df: pd.DataFrame) -> pd.DataFrame: + """ + Attaches offense/defense flags and offensive formations to presnap rows. -def label_offense_defense_manzone(presnap_df, plays_df): + Inputs: + - presnap_df: Tracking rows before the snap. + - plays_df: Play metadata including formation. - plays_df = plays_df.dropna(subset=['pff_manZone']) + Outputs: + - labeled_df: Presnap rows with formation code and offense/defense indicators. + """ - coverage_mapping = { - 'Zone': 0, - 'Man': 1} + formation_mapping = {"EMPTY": 0, "I_FORM": 1, "JUMBO": 2, "PISTOL": 3, "SHOTGUN": 4, "SINGLEBACK": 5, "WILDCAT": 6} - merged_df = presnap_df.merge( - plays_df[['gameId', 'playId', 'possessionTeam', 'defensiveTeam', 'pff_manZone']], - on=['gameId', 'playId'], - how='left' - ) + merged_df = presnap_df.merge(plays_df[["gameId", "playId", "possessionTeam", "defensiveTeam", "offenseFormation"]], on=["gameId", "playId"], how="left") - merged_df['defense'] = ((merged_df['club'] == merged_df['defensiveTeam']) & (merged_df['club'] != 'football')).astype(int) + merged_df["defense"] = ((merged_df["club"] == merged_df["defensiveTeam"]) & (merged_df["club"] != "football")).astype(int) - merged_df['pff_manZone'] = merged_df['pff_manZone'].map(coverage_mapping) - merged_df.dropna(subset=['pff_manZone'], inplace=True) + merged_df["offenseFormation"] = merged_df["offenseFormation"].map(formation_mapping) + merged_df.dropna(subset=["offenseFormation"], inplace=True) - return merged_df + return merged_df -def label_offense_defense_formation(presnap_df, plays_df): +def split_data_by_uniqueId( + df: pd.DataFrame, + train_ratio: float = 0.7, + test_ratio: float = 0.15, + val_ratio: float = 0.15, + unique_id_column: str = "uniqueId", +) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """ - """ - Adds 'offense' and 'defense' columns to presnap_df, marking players as offense (1) or defense (0) - based on possession team and defensive team from plays_df. Enumerates offensive formations - and removes rows with missing formations. + Splits tracking rows into train/test/val while keeping plays intact. - Parameters: - presnap_df (pd.DataFrame): DataFrame containing tracking data with 'gameId', 'playId', and 'club'. - plays_df (pd.DataFrame): DataFrame containing 'gameId', 'playId', 'possessionTeam', 'defensiveTeam', 'offenseFormation'. + Inputs: + - df: Full set of tracking rows. + - train_ratio/test_ratio/val_ratio: Fractions used for each split. + - unique_id_column: Column defining play identity. - Returns: - pd.DataFrame: Updated presnap_df with added 'offense', 'defense', and enumerated 'offenseFormation' columns, with NaN formations dropped. - """ + Outputs: + - splits: Three dataframes for train, test, and validation. + """ + unique_ids = df[unique_id_column].unique() + np.random.shuffle(unique_ids) - formation_mapping = { - 'EMPTY': 0, - 'I_FORM': 1, - 'JUMBO': 2, - 'PISTOL': 3, - 'SHOTGUN': 4, - 'SINGLEBACK': 5, - 'WILDCAT': 6 - } + num_ids = len(unique_ids) + train_end = int(train_ratio * num_ids) + test_end = train_end + int(test_ratio * num_ids) - merged_df = presnap_df.merge( - plays_df[['gameId', 'playId', 'possessionTeam', 'defensiveTeam', 'offenseFormation']], - on=['gameId', 'playId'], - how='left' - ) + train_ids = unique_ids[:train_end] + test_ids = unique_ids[train_end:test_end] + val_ids = unique_ids[test_end:] - merged_df['defense'] = ((merged_df['club'] == merged_df['defensiveTeam']) & (merged_df['club'] != 'football')).astype(int) + train_df = df[df[unique_id_column].isin(train_ids)] + test_df = df[df[unique_id_column].isin(test_ids)] + val_df = df[df[unique_id_column].isin(val_ids)] - merged_df['offenseFormation'] = merged_df['offenseFormation'].map(formation_mapping) - merged_df.dropna(subset=['offenseFormation'], inplace=True) + print(f"Train Dataframe Frames: {train_df.shape[0]}") + print(f"Test Dataframe Frames: {test_df.shape[0]}") + print(f"Val Dataframe Frames: {val_df.shape[0]}") - return merged_df + return train_df, test_df, val_df -def split_data_by_uniqueId(df, train_ratio=0.7, test_ratio=0.15, val_ratio=0.15, unique_id_column="uniqueId"): +def pass_attempt_merging(tracking: pd.DataFrame, plays: pd.DataFrame) -> pd.DataFrame: + """ + Flags pass attempts and merges that indicator into tracking rows. - """ - Split the dataframe into training, testing, and validation sets based on a given ratio while - ensuring all rows with the same uniqueId are in the same set. + Inputs: + - tracking: Tracking rows to annotate. + - plays: Play metadata with pass results. - :param df: the aggregate dataframe containing all frames for each play - :param train_ratio: proportion of the data to allocate to training (default 0.7) - :param test_ratio: proportion of the data to allocate to testing (default 0.15) - :param val_ratio: proportion of the data to allocate to validation (default 0.15) - :param unique_id_column: the name of the column containing the unique identifiers for each play + Outputs: + - merged_df: Tracking rows with passAttempt column. + """ - :return: three dataframes (train_df, test_df, val_df) for training, testing, and validation - """ + plays["passAttempt"] = np.where(plays["passResult"].isin(["C", "I", "IN", "S"]), 1, 0) - unique_ids = df[unique_id_column].unique() - np.random.shuffle(unique_ids) + plays_for_merge = plays[["gameId", "playId", "passAttempt"]] + merged_df = tracking.merge(plays_for_merge, on=["gameId", "playId"], how="left") - num_ids = len(unique_ids) - train_end = int(train_ratio * num_ids) - test_end = train_end + int(test_ratio * num_ids) + return merged_df - train_ids = unique_ids[:train_end] - test_ids = unique_ids[train_end:test_end] - val_ids = unique_ids[test_end:] - train_df = df[df[unique_id_column].isin(train_ids)] - test_df = df[df[unique_id_column].isin(test_ids)] - val_df = df[df[unique_id_column].isin(val_ids)] +def prepare_frame_data(df: pd.DataFrame, features: Sequence[str], target_column: str) -> tuple[torch.Tensor | None, torch.Tensor | None]: + """ + Converts per-frame tracking rows into stacked feature and target tensors. - print(f"Train Dataframe Frames: {train_df.shape[0]}") - print(f"Test Dataframe Frames: {test_df.shape[0]}") - print(f"Val Dataframe Frames: {val_df.shape[0]}") + Inputs: + - df: Frame-level tracking rows with required features. + - features: Column names to include as model inputs. + - target_column: Column containing frame target labels. - return train_df, test_df, val_df + Outputs: + - feature_tensor: Batched tensor of frame features. + - target_tensor: Tensor of frame targets aligned to feature order. + """ + features_array = df.groupby("frameUniqueId")[features].apply(lambda x: x.to_numpy(dtype=np.float32)).to_numpy() -def pass_attempt_merging(tracking, plays): + try: + features_tensor = torch.tensor(np.stack(features_array)) + except ValueError as e: + print("Skipping batch due to inconsistent shapes in features_array:", e) + return None, None - # Possible values are of passResult are: - # C: completed pass - # I or IN: incompleted pass - # R: run play - # S: Sack - # None: Something else - # We want completed passes, incompleted passes, or sack (good coverage can lead to this) - plays['passAttempt'] = np.where(plays['passResult'].isin(['C', 'I', 'IN', 'S']), 1, 0) + targets_array = df.groupby("frameUniqueId")[target_column].first().to_numpy() + targets_tensor = torch.tensor(targets_array, dtype=torch.long) - plays_for_merge = plays[['gameId', 'playId', 'passAttempt']] - merged_df = tracking.merge( - plays_for_merge, - on=['gameId', 'playId'], - how='left') + return features_tensor, targets_tensor - return merged_df +def select_augmented_frames(df: pd.DataFrame, num_samples: int, sigma: int = 5) -> np.ndarray: + """ + Samples frames for augmentation, biased around the snap. -def prepare_frame_data(df, features, target_column): + Inputs: + - df: Tracking rows with frame identifiers and snap offsets. + - num_samples: Number of frame identifiers to sample. + - sigma: Spread of the snap-centered weighting. - features_array = df.groupby("frameUniqueId")[features].apply( - lambda x: x.to_numpy(dtype=np.float32)).to_numpy() + Outputs: + - selected_frames: Array of frameUniqueId values to augment. + """ - try: - features_tensor = torch.tensor(np.stack(features_array)) - except ValueError as e: - print("Skipping batch due to inconsistent shapes in features_array:", e) - return None, None # or return some placeholder values if needed + df_frames = df[["frameUniqueId", "frames_from_snap"]].drop_duplicates() + weights = np.exp(-((df_frames["frames_from_snap"] + 10) ** 2) / (2 * sigma**2)) - targets_array = df.groupby("frameUniqueId")[target_column].first().to_numpy() - targets_tensor = torch.tensor(targets_array, dtype=torch.long) + weights /= weights.sum() - return features_tensor, targets_tensor + selected_frames = np.random.choice(df_frames["frameUniqueId"], size=num_samples, replace=False, p=weights) + return selected_frames -def select_augmented_frames(df, num_samples, sigma=5): - df_frames = df[['frameUniqueId', 'frames_from_snap']].drop_duplicates() - weights = np.exp(-((df_frames['frames_from_snap'] + 10) ** 2) / (2 * sigma ** 2)) +def data_augmentation(df: pd.DataFrame, augmented_frames: Iterable[str]) -> pd.DataFrame: + """ + Mirrors selected frames to add variation around the snap. - weights /= weights.sum() + Inputs: + - df: Tracking rows ready for augmentation. + - augmented_frames: Frame identifiers chosen for mirroring. - selected_frames = np.random.choice( - df_frames['frameUniqueId'], size=num_samples, replace=False, p=weights - ) + Outputs: + - augmented_df: Augmented rows with flipped coordinates and tags. + """ - return selected_frames + df_sample = df.loc[df["frameUniqueId"].isin(augmented_frames)].copy() + df_sample["y_clean"] = (160 / 3) - df_sample["y_clean"] + df_sample["dir_radians"] = (2 * np.pi) - df_sample["dir_radians"] + df_sample["dir_clean"] = np.degrees(df_sample["dir_radians"]) -def data_augmentation(df, augmented_frames): + df_sample["frameUniqueId"] = df_sample["frameUniqueId"].astype(str) + "_aug" - df_sample = df.loc[df['frameUniqueId'].isin(augmented_frames)].copy() + return df_sample - df_sample['y_clean'] = (160 / 3) - df_sample['y_clean'] - df_sample['dir_radians'] = (2 * np.pi) - df_sample['dir_radians'] - df_sample['dir_clean'] = np.degrees(df_sample['dir_radians']) - df_sample['frameUniqueId'] = df_sample['frameUniqueId'].astype(str) + '_aug' +def add_frames_from_snap(df: pd.DataFrame) -> pd.DataFrame: + """ + Calculates snap-relative frame offsets for each play. - return df_sample + Inputs: + - df: Tracking rows containing frameType and frameId. + Outputs: + - df_with_offsets: Dataframe including frames_from_snap column. + """ -def add_frames_from_snap(df): - snap_frames = df[df['frameType'] == 'SNAP'].groupby('uniqueId')['frameId'].first() - df = df.merge(snap_frames.rename('snap_frame'), on='uniqueId', how='left') - df['frames_from_snap'] = df['frameId'] - df['snap_frame'] + snap_frames = df[df["frameType"] == "SNAP"].groupby("uniqueId")["frameId"].first() + df = df.merge(snap_frames.rename("snap_frame"), on="uniqueId", how="left") + df["frames_from_snap"] = df["frameId"] - df["snap_frame"] - return df + return df diff --git a/src/common/__init__.py b/src/common/__init__.py new file mode 100644 index 0000000..32c613c --- /dev/null +++ b/src/common/__init__.py @@ -0,0 +1,4 @@ +#!/usr/bin/env python +""" +Initializes the common package for common utilities used across codebase. +""" diff --git a/src/common/args.py b/src/common/args.py index 9ce7940..1df7668 100644 --- a/src/common/args.py +++ b/src/common/args.py @@ -1,27 +1,37 @@ #!/usr/bin/env python - """ -Parses input arguments to then act on in code +Parses shared CLI arguments used by training scripts. """ import argparse -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--tune", - action="store_true", - help="Use Ray Tune to search hyperparameters instead of a single training run", - ) - parser.add_argument( - "--profile", - action="store_true", - help="Enable @time_fcn timing decorators for profiling", - ) - parser.add_argument( - "--ci", - action="store_true", - help="Enable CI mode with reduced epochs for faster training during pipeline", - ) - return parser.parse_args() \ No newline at end of file +def parse_args() -> argparse.Namespace: + """ + Defines and parses common command-line flags. + + Inputs: + - None (relies on sys.argv). + + Outputs: + - args: Namespace containing tune/profile/ci flags. + """ + + parser = argparse.ArgumentParser() + parser.add_argument( + "--tune", + action="store_true", + help="Use Ray Tune to search hyperparameters instead of a single training run", + ) + parser.add_argument( + "--profile", + action="store_true", + help="Enable @time_fcn timing decorators for profiling", + ) + + parser.add_argument( + "--ci", + action="store_true", + help="Enable CI mode with reduced epochs for faster training during pipeline", + ) + return parser.parse_args() diff --git a/src/common/csv_to_parquet.py b/src/common/csv_to_parquet.py index bbfdc7d..92bf784 100644 --- a/src/common/csv_to_parquet.py +++ b/src/common/csv_to_parquet.py @@ -1,78 +1,85 @@ #!/usr/bin/env python - """ -Transforms raw csv data to raw parquet data - -Requires: -- Raw location tracking data in nfl/data/parquet - -Outputs parquet data in nfl/data/parquet that is stored in git LFS +Converts source AWS NGS csv files (optionally compressed) to Parquet format. """ import sys -import os from pathlib import Path + import pandas as pd import pyarrow as pa import pyarrow.parquet as pq CSV_EXTS = {".csv", ".csv.gz", ".csv.bz2", ".csv.xz"} -def csv_to_parquet(in_path: Path, out_path: Path, chunksize: int = 250_000, compression: str = "zstd"): - out_path.parent.mkdir(parents=True, exist_ok=True) - - # Prepare a writer lazily after first chunk defines schema - writer = None - try: - # Tweak read_csv args as needed (parse dates, dtype, etc.) - # Example: parse_dates=['timestamp'] if you have a time column - for i, chunk in enumerate(pd.read_csv(in_path, chunksize=chunksize, low_memory=False)): - # Convert to Arrow table - table = pa.Table.from_pandas(chunk, preserve_index=False) - - if writer is None: - writer = pq.ParquetWriter( - where=str(out_path), - schema=table.schema, - compression=compression, - # Parquet v2 encodings generally smaller/better - use_dictionary=True - ) - writer.write_table(table) - - if writer is None: - # Empty CSV -> write empty parquet with no rows - empty_tbl = pa.Table.from_pandas(pd.DataFrame(), preserve_index=False) - pq.write_table(empty_tbl, out_path, compression=compression) - finally: - if writer is not None: - writer.close() - -def main(): - if len(sys.argv) != 3: - print("Usage: python csv_to_parquet.py /path/to/csv_root /path/to/parquet_out") - sys.exit(1) - - in_root = Path(sys.argv[1]).resolve() - out_root = Path(sys.argv[2]).resolve() - - if not in_root.exists(): - print(f"Input path does not exist: {in_root}") - sys.exit(1) - - converted = 0 - for p in in_root.rglob("*"): - if p.is_file() and any(str(p).lower().endswith(ext) for ext in CSV_EXTS): - rel = p.relative_to(in_root) - # Replace only the final extension with .parquet - out_rel = rel.with_suffix(".parquet") - out_path = out_root / out_rel - - print(f"[convert] {p} -> {out_path}") - csv_to_parquet(p, out_path) - converted += 1 - - print(f"Done. Converted {converted} file(s) to {out_root}") + +def csv_to_parquet(in_path: Path, out_path: Path, chunksize: int = 250_000, compression: str = "zstd") -> None: + """ + Streams a CSV file to Parquet, writing in chunks to limit memory. + + Inputs: + - in_path: Path to the CSV file. + - out_path: Destination path for the Parquet file. + - chunksize: Rows per chunk to process at a time. + - compression: Parquet compression codec. + + Outputs: + - Writes a Parquet file to out_path. + """ + + out_path.parent.mkdir(parents=True, exist_ok=True) + writer = None + try: + for chunk in pd.read_csv(in_path, chunksize=chunksize, low_memory=False): + table = pa.Table.from_pandas(chunk, preserve_index=False) + + if writer is None: + writer = pq.ParquetWriter(where=str(out_path), schema=table.schema, compression=compression, use_dictionary=True) + writer.write_table(table) + + if writer is None: + empty_tbl = pa.Table.from_pandas(pd.DataFrame(), preserve_index=False) + pq.write_table(empty_tbl, out_path, compression=compression) + finally: + if writer is not None: + writer.close() + + +def main() -> None: + """ + CLI entry point to convert all CSV files under a directory tree. + + Inputs: + - sys.argv[1]: Root directory containing CSVs. + - sys.argv[2]: Destination root for Parquet outputs. + + Outputs: + - Writes converted Parquet files mirroring the input tree. + """ + if len(sys.argv) != 3: + print("Usage: python csv_to_parquet.py /path/to/csv_root /path/to/parquet_out") + sys.exit(1) + + in_root = Path(sys.argv[1]).resolve() + out_root = Path(sys.argv[2]).resolve() + + if not in_root.exists(): + print(f"Input path does not exist: {in_root}") + sys.exit(1) + + converted = 0 + for p in in_root.rglob("*"): + if p.is_file() and any(str(p).lower().endswith(ext) for ext in CSV_EXTS): + rel = p.relative_to(in_root) + out_rel = rel.with_suffix(".parquet") + out_path = out_root / out_rel + + print(f"[convert] {p} -> {out_path}") + csv_to_parquet(p, out_path) + converted += 1 + + print(f"Done. Converted {converted} file(s) to {out_root}") + if __name__ == "__main__": - main() + main() diff --git a/src/common/decorators.py b/src/common/decorators.py index f53b0b5..3f19f34 100644 --- a/src/common/decorators.py +++ b/src/common/decorators.py @@ -1,37 +1,57 @@ #!/usr/bin/env python """ -Contains common use decorators +Provides timing-related decorators used across the project. """ -import time import functools import logging +import time +from collections.abc import Callable +from typing import ParamSpec _TIME_DECORATORS_ENABLED = False + def set_time_decorators_enabled(enabled: bool) -> None: - """ - Enable/disable time_fcn decorators globally, called at startup - """ - global _TIME_DECORATORS_ENABLED - _TIME_DECORATORS_ENABLED = enabled - -def time_fcn(fn): - """ - Decorator that measures wall-clock time of a function and logs it. - When _TIME_DECORATORS_ENABLED is False, it becomes a simple passthrough. - """ - @functools.wraps(fn) - def wrapper(*args, **kwargs): - # Fast exit when disabled (minimal overhead) - if not _TIME_DECORATORS_ENABLED: - return fn(*args, **kwargs) - - start = time.perf_counter() - try: - return fn(*args, **kwargs) - finally: - elapsed = time.perf_counter() - start - logging.info("Function %s took %.4f s", fn.__name__, elapsed) - return wrapper + """ + Toggles whether timing decorators emit logs. + + Inputs: + - enabled: True to enable timing, False to silence it. + + Outputs: + - Updates global flag controlling decorator behavior. + """ + global _TIME_DECORATORS_ENABLED + _TIME_DECORATORS_ENABLED = enabled + + +P = ParamSpec("P") + + +def time_fcn[T](fn: Callable[P, T]) -> Callable[P, T]: + """ + Wraps a function to log its wall-clock runtime when enabled. + + Inputs: + - fn: Function to wrap. + + Outputs: + - wrapper: Callable that optionally measures and logs runtime. + """ + + @functools.wraps(fn) + def wrapper(*args: P.args, **kwargs: P.kwargs) -> T: + # Fast exit when disabled (minimal overhead) + if not _TIME_DECORATORS_ENABLED: + return fn(*args, **kwargs) + + start = time.perf_counter() + try: + return fn(*args, **kwargs) + finally: + elapsed = time.perf_counter() - start + logging.info("Function %s took %.4f s", fn.__name__, elapsed) + + return wrapper diff --git a/src/common/mlflow.py b/src/common/mlflow.py index 6d16dcf..1fd20b8 100644 --- a/src/common/mlflow.py +++ b/src/common/mlflow.py @@ -1,12 +1,22 @@ #!/usr/bin/env python - """ -Contains funcions to setup MLflow for experiments +Lightweight helper to configure MLflow tracking for experiments. """ -# MLflow import mlflow -def setup_mlflow(experiment_name: str = "random-experiement", tracking_uri: str = "file:./mlruns"): - mlflow.set_tracking_uri(tracking_uri) - mlflow.set_experiment(experiment_name) + +def setup_mlflow(experiment_name: str = "random-experiement", tracking_uri: str = "file:./mlruns") -> None: + """ + Initializes MLflow tracking URI and experiment. + + Inputs: + - experiment_name: Name of the experiment to log under. + - tracking_uri: Backend store location. + + Outputs: + - Sets global MLflow state for subsequent logging calls. + """ + + mlflow.set_tracking_uri(tracking_uri) + mlflow.set_experiment(experiment_name) diff --git a/src/common/paths.py b/src/common/paths.py index 18aed02..99852b0 100644 --- a/src/common/paths.py +++ b/src/common/paths.py @@ -1,12 +1,29 @@ +#!/usr/bin/env python +""" +Defines common filesystem paths used across the project. +""" + from pathlib import Path + def project_root() -> Path: - # 2 since this is at src/commmon/path.py - return Path(__file__).resolve().parents[2] + """ + Finds the repository root directory. + + Inputs: + - None. + + Outputs: + - root_path: Path pointing to the project root. + """ + + return Path(__file__).resolve().parents[2] + -# Set project root PROJECT_ROOT = project_root() -# Create processing dirs -SAVE_DIR = PROJECT_ROOT / "data" / "processed" -SAVE_DIR.mkdir(parents=True, exist_ok=True) \ No newline at end of file +PROCESSED_DIR = PROJECT_ROOT / "data" / "processed" +PROCESSED_DIR.mkdir(parents=True, exist_ok=True) + +INFERENCE_DIR = PROJECT_ROOT / "data" / "inference" +INFERENCE_DIR.mkdir(parents=True, exist_ok=True) diff --git a/src/create_features.py b/src/create_features.py index 4bcccea..1e411a7 100644 --- a/src/create_features.py +++ b/src/create_features.py @@ -1,180 +1,179 @@ #!/usr/bin/env python - """ -Creates features for model training - -Requires: -- Raw location tracking data in nfl/data/parquet - -Cleans data and creates the following in nfl/data/processed: -- features_training.pt -- features_val.pt -- targets_training.pt -- targets_val.pt +Builds weekly training and validation feature tensors from raw tracking data. """ import gc -import os -import math import logging +from collections.abc import Sequence +from pathlib import Path import pandas as pd -import numpy as np -import matplotlib.pyplot as plt import torch -from torch.utils.data import TensorDataset -from torch.utils.data import DataLoader -from load_data import RawDataLoader -from clean_data import * -from common.decorators import * -from common.paths import SAVE_DIR +from clean_data import ( + add_frames_from_snap, + calculate_velocity_components, + data_augmentation, + label_offense_defense_manzone, + make_plays_left_to_right, + pass_attempt_merging, + prepare_frame_data, + rotate_direction_and_orientation, + select_augmented_frames, +) from common.args import parse_args +from common.decorators import set_time_decorators_enabled, time_fcn +from common.paths import PROCESSED_DIR +from load_data import RawDataLoader + + +def clean_df(location_df: pd.DataFrame, plays_df: pd.DataFrame, game_df: pd.DataFrame) -> pd.DataFrame: + """ + Cleans a week of tracking data and enriches it with play context. + + Inputs: + - location_df: Raw tracking rows for a given set of plays. + - plays_df: Play metadata including man/zone labels. + - game_df: Game metadata containing week numbers. -def clean_df(location_df: pd.DataFrame, plays_df: pd.DataFrame, game_df: pd.DataFrame): - - # Clean data - location_df = rotate_direction_and_orientation(location_df) - location_df = make_plays_left_to_right(location_df) - location_df = calculate_velocity_components(location_df) - location_df = pass_attempt_merging(location_df, plays_df) - # location_df = label_offense_defense_coverage(location_df, plays_df) # for specific coverage... currently set to man/zone only - location_df = label_offense_defense_manzone(location_df, plays_df) + Outputs: + - cleaned_df: Tracking rows with directional fixes, labels, and snap-relative fields. + """ - # Filter out the football and if it's not a pass attempt - location_df = location_df[(location_df['club'] != 'football') & (location_df['passAttempt'] == 1)] + location_df = rotate_direction_and_orientation(location_df) + location_df = make_plays_left_to_right(location_df) + location_df = calculate_velocity_components(location_df) + location_df = pass_attempt_merging(location_df, plays_df) + location_df = label_offense_defense_manzone(location_df, plays_df) - # Add in the week number - location_df = location_df.merge( - game_df[["gameId", "week"]], - on="gameId", - how="left" - ) + location_df = location_df[(location_df["club"] != "football") & (location_df["passAttempt"] == 1)] - # Create uids - location_df['uniqueId'] = location_df['gameId'].astype(str) + "_" + location_df['playId'].astype(str) - location_df['frameUniqueId'] = ( - location_df['gameId'].astype(str) + "_" + - location_df['playId'].astype(str) + "_" + - location_df['frameId'].astype(str)) + location_df = location_df.merge(game_df[["gameId", "week"]], on="gameId", how="left") - # Adding frames_from_snap (to do: make this a function but fine for now) - location_df = add_frames_from_snap(location_df) + location_df["uniqueId"] = location_df["gameId"].astype(str) + "_" + location_df["playId"].astype(str) + location_df["frameUniqueId"] = location_df["gameId"].astype(str) + "_" + location_df["playId"].astype(str) + "_" + location_df["frameId"].astype(str) - # Get rid of noisier outliers out of scope (15 seconds after the snap) - location_df = location_df[(location_df['frames_from_snap'] >= -150) & (location_df['frames_from_snap'] <= 50)] + location_df = add_frames_from_snap(location_df) + location_df = location_df[(location_df["frames_from_snap"] >= -150) & (location_df["frames_from_snap"] <= 50)] - return location_df + return location_df def _process_df(location_df: pd.DataFrame, plays_df: pd.DataFrame, games_df: pd.DataFrame) -> pd.DataFrame: - - location_df = clean_df(location_df, plays_df, games_df) + """ + Applies cleaning, subsampling, and augmentation to a week's tracking data. - # Filtering only for even frames (reduce amount of data that looks the same) - location_df = location_df[location_df['frameId'] % 2 == 0] + Inputs: + - location_df: Raw tracking rows for the week. + - plays_df: Play-level labels and metadata. + - games_df: Game-level metadata including weeks. - # Apply data augmentation to increase training size (centered around 0-4 seconds presnap!) - # -- 1/3rd of the current num of frames... specifically selecting for frames around the snap - num_unique_frames = len(set(location_df['frameUniqueId'])) - selected_frames = select_augmented_frames(location_df, int(num_unique_frames / 3), sigma=5) - augmented_location_df = data_augmentation(location_df, selected_frames) + Outputs: + - processed_df: Combined original and augmented rows ready for tensorization. + """ - # Combine og and augmented data - combined_location_df = pd.concat([location_df, augmented_location_df]) + location_df = clean_df(location_df, plays_df, games_df) + location_df = location_df[location_df["frameId"] % 2 == 0] - return combined_location_df + num_unique_frames = len(set(location_df["frameUniqueId"])) + selected_frames = select_augmented_frames(location_df, int(num_unique_frames / 3), sigma=5) + augmented_location_df = data_augmentation(location_df, selected_frames) + combined_location_df = pd.concat([location_df, augmented_location_df]) -def _load_weeks(weeks, prefix, SAVE_DIR): - tensors = [torch.load(SAVE_DIR / f"{prefix}_w{w}.pt") for w in weeks] - return torch.cat(tensors, dim=0) if len(tensors) > 1 else tensors[0] + return combined_location_df + + +def _load_weeks(weeks: Sequence[int], prefix: str, PROCESSED_DIR: Path) -> torch.Tensor: + """ + Loads and concatenates saved tensors for a set of weeks. + + Inputs: + - weeks: Week numbers to pull from disk. + - prefix: Base filename prefix (e.g., 'features'). + - PROCESSED_DIR: Directory where tensors are stored. + + Outputs: + - tensor: Concatenated tensor spanning the requested weeks. + """ + + tensors = [torch.load(PROCESSED_DIR / f"{prefix}_w{w}.pt") for w in weeks] + return torch.cat(tensors, dim=0) if len(tensors) > 1 else tensors[0] @time_fcn def main() -> None: - # Logging - logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') - - # Get input args - args = parse_args() - - # Enable/disable timing decorators - if args.profile: - set_time_decorators_enabled(True) - logging.info("Timing decorators enabled") - else: - set_time_decorators_enabled(False) - logging.info("Timing decorators disabled") - - # Specify constants - all_weeks = list(range(1, 10)) - train_weeks = list(range(1, 9)) - val_weeks = [9] - features = ["x_clean", "y_clean", "v_x", "v_y", "defense"] - target_column = "pff_manZone" - - # Load data once - raw = RawDataLoader() - games_df, plays_df, players_df, _ = raw.get_data(weeks=all_weeks) - - # Process each week - for w in all_weeks: - logging.info(f"Processing week: {w}") - - # Load just this week’s location data - _, _, _, location_df_w = raw.get_data(weeks=[w]) - - # Process (cleaning, data augmentation, etc) - combined_loc_df_w = _process_df(location_df_w, plays_df, games_df) - - # Keep the cols that we want to reduce memory - keep_cols = [ - "frameUniqueId", "displayName", "frameId", "frameType", - "x_clean", "y_clean", "v_x", "v_y", "defensiveTeam", - "pff_manZone", "defense" - ] - combined_loc_df_w = combined_loc_df_w[keep_cols].copy() - - # Downcast numeric columns before tensorization - for c in ["x_clean", "y_clean", "v_x", "v_y", "defense"]: - combined_loc_df_w[c] = pd.to_numeric(combined_loc_df_w[c], downcast="float") - - # Convert each frame to a tensor - features_tensor_w, targets_tensor_w = prepare_frame_data(combined_loc_df_w, features=features, target_column=target_column) - - # Save per-week artifacts - torch.save(features_tensor_w, SAVE_DIR / f"features_w{w}.pt") - torch.save(targets_tensor_w, SAVE_DIR / f"targets_w{w}.pt") - - # Free memory for next loop - del location_df_w, combined_loc_df_w, features_tensor_w, targets_tensor_w - gc.collect() - - # Aggregrate train/val from saved weekly tensors - logging.info("Aggregrating training set (Week 1 through 8)") - train_features = _load_weeks(train_weeks, "features", SAVE_DIR) - train_targets = _load_weeks(train_weeks, "targets", SAVE_DIR) - - logging.info("Aggregrating validation set (Week 9)") - val_features = _load_weeks(val_weeks, "features", SAVE_DIR) - val_targets = _load_weeks(val_weeks, "targets", SAVE_DIR) - - # Optional: ensure float32 - train_features = train_features.to(torch.float32) - val_features = val_features.to(torch.float32) - - # Save pooled artifacts - torch.save(train_features, SAVE_DIR / "features_training.pt") - torch.save(train_targets, SAVE_DIR / "targets_training.pt") - torch.save(val_features, SAVE_DIR / "features_val.pt") - torch.save(val_targets, SAVE_DIR / "targets_val.pt") - - logging.info("Train features: %s", getattr(train_features, "shape", None)) - logging.info("Val features: %s", getattr(val_features, "shape", None)) - logging.info("Train targets: %s", getattr(train_targets, "shape", None)) - logging.info("Val targets: %s", getattr(val_targets, "shape", None)) + """ + Generates and saves weekly and pooled tensors for transformer training. + + Inputs: + - Command-line flags control profiling. + + Outputs: + - Serialized feature and target tensors for training and validation weeks. + """ + + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + + args = parse_args() + if args.profile: + set_time_decorators_enabled(True) + logging.info("Timing decorators enabled") + else: + set_time_decorators_enabled(False) + logging.info("Timing decorators disabled") + + all_weeks = list(range(1, 10)) + train_weeks = list(range(1, 9)) + val_weeks = [9] + features = ["x_clean", "y_clean", "v_x", "v_y", "defense"] + target_column = "pff_manZone" + + raw = RawDataLoader() + games_df, plays_df, players_df, _ = raw.get_data(weeks=all_weeks) + + for w in all_weeks: + logging.info(f"Processing week: {w}") + _, _, _, location_df_w = raw.get_data(weeks=[w]) + + combined_loc_df_w = _process_df(location_df_w, plays_df, games_df) + + keep_cols = ["frameUniqueId", "displayName", "frameId", "frameType", "x_clean", "y_clean", "v_x", "v_y", "defensiveTeam", "pff_manZone", "defense"] + combined_loc_df_w = combined_loc_df_w[keep_cols].copy() + + for c in ["x_clean", "y_clean", "v_x", "v_y", "defense"]: + combined_loc_df_w[c] = pd.to_numeric(combined_loc_df_w[c], downcast="float") + + features_tensor_w, targets_tensor_w = prepare_frame_data(combined_loc_df_w, features=features, target_column=target_column) + + torch.save(features_tensor_w, PROCESSED_DIR / f"features_w{w}.pt") + torch.save(targets_tensor_w, PROCESSED_DIR / f"targets_w{w}.pt") + + del location_df_w, combined_loc_df_w, features_tensor_w, targets_tensor_w + gc.collect() + + logging.info("Aggregrating training set (Week 1 through 8)") + train_features = _load_weeks(train_weeks, "features", PROCESSED_DIR) + train_targets = _load_weeks(train_weeks, "targets", PROCESSED_DIR) + + logging.info("Aggregrating validation set (Week 9)") + val_features = _load_weeks(val_weeks, "features", PROCESSED_DIR) + val_targets = _load_weeks(val_weeks, "targets", PROCESSED_DIR) + + train_features = train_features.to(torch.float32) + val_features = val_features.to(torch.float32) + + torch.save(train_features, PROCESSED_DIR / "features_training.pt") + torch.save(train_targets, PROCESSED_DIR / "targets_training.pt") + torch.save(val_features, PROCESSED_DIR / "features_val.pt") + torch.save(val_targets, PROCESSED_DIR / "targets_val.pt") + + logging.info("Train features: %s", getattr(train_features, "shape", None)) + logging.info("Val features: %s", getattr(val_features, "shape", None)) + logging.info("Train targets: %s", getattr(train_targets, "shape", None)) + logging.info("Val targets: %s", getattr(val_targets, "shape", None)) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/src/generate_predictions.py b/src/generate_predictions.py index 72c650f..c3197ab 100644 --- a/src/generate_predictions.py +++ b/src/generate_predictions.py @@ -1,236 +1,240 @@ #!/usr/bin/env python - """ -Trains LSTM model on location tracking data - -Requires: -- Raw location tracking data in nfl/data/parquet -- Trained model in nfl/data/processed/model.pth - -Performs inference on each week and saves to nfl/data/processed/week{n}_predictions.csv +Runs the trained transformer to generate man/zone predictions for tracking data. """ -import math -import warnings -import random -import os -import logging import json +import logging +import os from pathlib import Path -import pandas as pd -pd.options.mode.chained_assignment = None import numpy as np -import matplotlib.pyplot as plt +import pandas as pd import polars as pl import torch -import torch.nn as nn -from torch.utils.data import TensorDataset, DataLoader -from torch.optim import AdamW +from torch.profiler import ProfilerActivity -from models.transformer import ManZoneTransformer -from load_data import RawDataLoader -from clean_data import * -from common.paths import PROJECT_ROOT, SAVE_DIR -from common.decorators import * +from clean_data import calculate_velocity_components, label_offense_defense_manzone, make_plays_left_to_right, pass_attempt_merging, rotate_direction_and_orientation from common.args import parse_args +from common.decorators import set_time_decorators_enabled, time_fcn +from common.paths import PROJECT_ROOT +from load_data import RawDataLoader +from models.transformer import create_transformer_model + + +def process_week_data_preds(week_number: int, plays: pd.DataFrame) -> pd.DataFrame: + """ + Loads, cleans, and labels a single week's tracking data. + + Inputs: + - week_number: Week to process. + - plays: Play metadata with labels. + Outputs: + - week_df: Cleaned and labeled tracking rows for the requested week. + """ -def process_week_data_preds(week_number, plays): - if os.getenv("CI_DATA_ROOT"): - WEEK_PARQUET_PATH = Path(os.getenv("CI_DATA_ROOT")) / f"tracking_week_{week_number}.parquet" - else: - WEEK_PARQUET_PATH = PROJECT_ROOT / "data" / "parquet" / f"tracking_week_{week_number}.parquet" - week = pd.read_parquet(WEEK_PARQUET_PATH) - logging.info(f"Finished reading Week {week_number} data") + # Swap the data root if it's CI (since CI runs with a local runner we are using the actual data location to avoid having the VM do git LFS pulls) + if os.getenv("CI_DATA_ROOT"): + WEEK_PARQUET_PATH = Path(os.getenv("CI_DATA_ROOT")) / f"tracking_week_{week_number}.parquet" + else: + WEEK_PARQUET_PATH = PROJECT_ROOT / "data" / "parquet" / f"tracking_week_{week_number}.parquet" - # applying cleaning functions - week = rotate_direction_and_orientation(week) - week = make_plays_left_to_right(week) - week = calculate_velocity_components(week) - week = pass_attempt_merging(week, plays) - # week = label_offense_defense_coverage(week, plays) # for specific coverage... currently set to man/zone only - week = label_offense_defense_manzone(week, plays) + # Load data + week = pd.read_parquet(WEEK_PARQUET_PATH) + logging.info(f"Finished reading Week {week_number} data") - week['week'] = week_number - week['uniqueId'] = week['gameId'].astype(str) + "_" + week['playId'].astype(str) - week['frameUniqueId'] = ( - week['gameId'].astype(str) + "_" + - week['playId'].astype(str) + "_" + - week['frameId'].astype(str)) + # Apply cleaning functions + week = rotate_direction_and_orientation(week) + week = make_plays_left_to_right(week) + week = calculate_velocity_components(week) + week = pass_attempt_merging(week, plays) + # week = label_offense_defense_coverage(week, plays) # for specific coverage... currently set to man/zone only + week = label_offense_defense_manzone(week, plays) - # adding frames_from_snap (to do: make this a function but fine for now) - snap_frames = week[week['frameType'] == 'SNAP'].groupby('uniqueId')['frameId'].first() - week = week.merge(snap_frames.rename('snap_frame'), on='uniqueId', how='left') - week['frames_from_snap'] = week['frameId'] - week['snap_frame'] + week["week"] = week_number + week["uniqueId"] = week["gameId"].astype(str) + "_" + week["playId"].astype(str) + week["frameUniqueId"] = week["gameId"].astype(str) + "_" + week["playId"].astype(str) + "_" + week["frameId"].astype(str) - # filtering only for even frames - # week = week[week['frameId'] % 2 == 0] + # Add frames_from_snap (to do: make this a function but fine for now) + snap_frames = week[week["frameType"] == "SNAP"].groupby("uniqueId")["frameId"].first() + week = week.merge(snap_frames.rename("snap_frame"), on="uniqueId", how="left") + week["frames_from_snap"] = week["frameId"] - week["snap_frame"] - # ridding of any potential outliers (25 seconds after the snap) - week = week[(week['frames_from_snap'] >= -150) & (week['frames_from_snap'] <= 30)] + # filtering only for even frames + # week = week[week['frameId'] % 2 == 0] - # applying data augmentation to increase training size (centered around 0-4 seconds presnap!) - # -- 1/3rd of the current num of frames... specifically selecting for frames around the snap + # Ridding of any potential outliers (25 seconds after the snap) + week = week[(week["frames_from_snap"] >= -150) & (week["frames_from_snap"] <= 30)] - # num_unique_frames = len(set(week['frameUniqueId'])) - # selected_frames = select_augmented_frames(week, int(num_unique_frames / 3), sigma=5) - # week_aug = data_augmentation(week, selected_frames) + # applying data augmentation to increase training size (centered around 0-4 seconds presnap!) + # -- 1/3rd of the current num of frames... specifically selecting for frames around the snap - # week = pd.concat([week, week_aug]) + # num_unique_frames = len(set(week['frameUniqueId'])) + # selected_frames = select_augmented_frames(week, int(num_unique_frames / 3), sigma=5) + # week_aug = data_augmentation(week, selected_frames) - logging.info(f"Finished processing Week {week_number} data") + # week = pd.concat([week, week_aug]) - return week + logging.info(f"Finished processing Week {week_number} data") + return week -def prepare_tensor(play, num_players=22, num_features=5): - features = ['x_clean', 'y_clean', 'v_x', 'v_y', 'defense'] - play_data = play[features + ['frameId']] - play_data = play_data.sort_values(by='frameId') - frames = ( - play_data - .groupby('frameId')[features] - .apply(lambda g: g.to_numpy()) - ) - all_frames_tensor = np.stack(frames.to_list()) # Shape: [num_frames, num_players, num_features] - all_frames_tensor = torch.tensor(all_frames_tensor, dtype=torch.float32) +def prepare_tensor(play: pd.DataFrame) -> torch.Tensor: + """ + Converts a single play slice into a model-ready tensor. - return all_frames_tensor # Shape: [num_frames, num_players, num_features] + Inputs: + - play: Tracking rows for one frameUniqueId. + + Outputs: + - frame_tensor: Tensor shaped [frames, players, features] for inference. + """ + features = ["x_clean", "y_clean", "v_x", "v_y", "defense"] + play_data = play[features + ["frameId"]] + play_data = play_data.sort_values(by="frameId") + + frames = play_data.groupby("frameId")[features].apply(lambda g: g.to_numpy()) + all_frames_tensor = np.stack(frames.to_list()) # Shape: [num_frames, num_players, num_features] + all_frames_tensor = torch.tensor(all_frames_tensor, dtype=torch.float32) + + return all_frames_tensor # Shape: [num_frames, num_players, num_features] @time_fcn -def main(): - # Set logging - logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') - - # Get input args - args = parse_args() - - # Enable/disable timing decorators - if args.profile: - set_time_decorators_enabled(True) - logging.info("Timing decorators enabled") - else: - set_time_decorators_enabled(False) - logging.info("Timing decorators disabled") - - # Set TensorFloat-32 (TF32) mode for matmul and cudnn (speeds up training on Ampere+ GPUs with minimal impact on accuracy) - torch.backends.cuda.matmul.allow_tf32 = True - torch.backends.cudnn.allow_tf32 = True - - # Get model params and set device - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - with open(PROJECT_ROOT / "data" / "training" / "model_params.json", 'r') as file: - config = json.load(file) - model = ManZoneTransformer( - feature_len=5, # num of input features (x, y, v_x, v_y, defense) - model_dim=int(config["model_dim"]), # from ray tune or loaded - num_heads=int(config["num_heads"]), # from ray tune or loaded - num_layers=int(config["num_layers"]), # from ray tune or loaded - dim_feedforward=int(config["model_dim"]) * int(config["multiplier"]), # from ray tune or loaded - dropout=float(config["dropout"]), # from ray tune or loaded - output_dim=2 # man or zone classification - ) - # Move model to device (GPU) - model = model.to(device) - - # Compile with fullgraph - model = torch.compile( - model, - mode="default", # default, reduces overhead, generally stable - fullgraph=True # enable full graph fusion, helps reduce kernel launch overhead - ) - - # Load model - model.load_state_dict(torch.load(PROJECT_ROOT / "data" / "training" / "best_model.pth", map_location=device)) - - # Set to eval mode - model.eval() - - # Load data - rawLoader = RawDataLoader() - _, plays_df, _, _ = rawLoader.get_data(weeks=list(range(1, 10))) - - # Process + predict one week at a time (keeps RAM low) - for week_eval in [9]: - week_df = process_week_data_preds(week_eval, plays_df) - - # Filtering early to shrink memory - week_df = week_df[(week_df['club'] != 'football') & (week_df['passAttempt'] == 1)].copy() - - # Polars convert optional for speed - tracking_df_polars = pl.DataFrame(week_df) - - # Stream predictions to CSV in batches - weekly_predictions_path_csv = SAVE_DIR / f"week{week_eval}_preds.csv" - wrote_header = False - batch = [] - BATCH_SIZE = 5000 # tune (smaller => lower peak RAM) - - # Iterate unique frames without building a giant Python set - list_ids = pd.unique(week_df['frameUniqueId'].values) - - logging.info(f"Starting loop for week {week_eval}...") - for idx, frame_id in enumerate(list_ids, start=1): - if idx % 20000 == 0: - logging.info(f"Processed {idx}/{len(list_ids)} frames ({100*idx/len(list_ids):.1f}%)") - - # Grab frame rows (polars or pandas) - if tracking_df_polars is not None: - frame = tracking_df_polars.filter(pl.col("frameUniqueId") == frame_id).to_pandas() - else: - frame = week_df.loc[week_df["frameUniqueId"] == frame_id] - - # Lightweight tensor build - frame_tensor = prepare_tensor(frame) - if frame_tensor is None: - continue - - frame_tensor = frame_tensor.to(device, non_blocking=True) - - with torch.no_grad(): - outputs = model(frame_tensor) # [1, 2] - probabilities = torch.softmax(outputs, dim=1).cpu().numpy()[0] - zone_prob, man_prob = float(probabilities[0]), float(probabilities[1]) - pred = 0 if zone_prob > man_prob else 1 - actual = int(frame['pff_manZone'].iloc[0]) if 'pff_manZone' in frame.columns and not pd.isna(frame['pff_manZone'].iloc[0]) else -1 - - play_id = "_".join(frame_id.split("_")[:2]) - frame_num = int(frame_id.split("_")[-1]) - - batch.append({ - 'frameUniqueId': frame_id, - 'uniqueId': play_id, - 'frameId': frame_num, - 'zone_prob': zone_prob, - 'man_prob': man_prob, - 'pred': pred, - 'actual': actual - }) - - # Flush batch to CSV to keep RAM low - if len(batch) >= BATCH_SIZE: - pd.DataFrame(batch).to_csv(weekly_predictions_path_csv, mode='a', header=not wrote_header, index=False) - wrote_header = True - batch.clear() - - # Flush tail - if batch: - pd.DataFrame(batch).to_csv(weekly_predictions_path_csv, mode='a', header=not wrote_header, index=False) - batch.clear() - - logging.info(f"Finished week {week_eval}... saved to week{week_eval}_preds.csv\n") - - # Merge week_df with preds (per-week, small) - preds_week = pd.read_csv(weekly_predictions_path_csv, usecols=['frameUniqueId','zone_prob','man_prob','pred', 'actual']) - tracking_preds = week_df.merge(preds_week, on='frameUniqueId', how='left') - tracking_preds.to_csv(SAVE_DIR / f"tracking_week_{week_eval}_preds.csv", index=False) - - # Free RAM before next week - del week_df, tracking_df_polars, preds_week, tracking_preds - import gc; gc.collect() +def main() -> None: + """ + Streams weekly tracking frames through the transformer and writes predictions. + + Inputs: + - CLI flags from parse_args control profiling. + + Outputs: + - CSV files with frame-level predictions and merged tracking data. + """ + # Set logging + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + + # Get input args + args = parse_args() + + # Enable/disable timing decorators + if args.profile: + set_time_decorators_enabled(True) + logging.info("Timing decorators enabled") + else: + set_time_decorators_enabled(False) + logging.info("Timing decorators disabled") + + # Set TensorFloat-32 (TF32) mode for matmul and cudnn (speeds up training on Ampere+ GPUs with minimal impact on accuracy) + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + + # Set device and profiler + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + activities = [ProfilerActivity.CPU] + activities += [ProfilerActivity.CUDA] + + # Reload model which includes: + # 1. Load model weights that were generated as best trial from Ray HPO + # 2. Create the model with those hyperparameters + # 3. Load it on the GPU + # 4. Compile the model with torch.compile for faster inference + # 5. Load the last checkpoint of the model weights + # 6. Set to eval for inference + with open(PROJECT_ROOT / "data" / "training" / "model_params.json") as file: + config = json.load(file) + model = create_transformer_model(config) + model = model.to(device) + model = torch.compile( + model, + mode="default", # default, reduces overhead, generally stable + fullgraph=True, # enable full graph fusion, helps reduce kernel launch overhead + ) + model.load_state_dict(torch.load(PROJECT_ROOT / "data" / "training" / "transformer.pt", map_location=device)) + model.eval() + + # Load data + rawLoader = RawDataLoader() + _, plays_df, _, _ = rawLoader.get_data(weeks=list(range(1, 10))) + + # Process + predict one week at a time (keeps RAM low) + for week_eval in [9]: + week_df = process_week_data_preds(week_eval, plays_df) + + # Filtering early to shrink memory + week_df = week_df[(week_df["club"] != "football") & (week_df["passAttempt"] == 1)].copy() + + # Polars convert optional for speed + tracking_df_polars = pl.DataFrame(week_df) + + # Stream predictions to CSV in batches + weekly_predictions_path_csv = PROJECT_ROOT / "data" / "inference" / f"week{week_eval}_preds.csv" + wrote_header = False + batch = [] + + # Iterate unique frames without building a giant Python set + list_ids = pd.unique(week_df["frameUniqueId"].values) + + logging.info(f"Starting loop for week {week_eval}...") + for idx, frame_id in enumerate(list_ids, start=1): + if idx % 20000 == 0: + logging.info(f"Processed {idx}/{len(list_ids)} frames ({100 * idx / len(list_ids):.1f}%)") + + # Grab frame rows (polars or pandas) + if tracking_df_polars is not None: + frame = tracking_df_polars.filter(pl.col("frameUniqueId") == frame_id).to_pandas() + else: + frame = week_df.loc[week_df["frameUniqueId"] == frame_id] + + # Lightweight tensor build + frame_tensor = prepare_tensor(frame) + if frame_tensor is None: + continue + + # Save the first tensor for profiling later + if idx == 1: + torch.save(frame_tensor, PROJECT_ROOT / "data" / "inference" / "example_frame_tensor.pt") + + # Move to device and run inference + frame_tensor = frame_tensor.to(device, non_blocking=True) + with torch.no_grad(): + outputs = model(frame_tensor) # [1, 2] + probabilities = torch.softmax(outputs, dim=1).cpu().numpy()[0] + zone_prob, man_prob = float(probabilities[0]), float(probabilities[1]) + pred = 0 if zone_prob > man_prob else 1 + actual = int(frame["pff_manZone"].iloc[0]) if "pff_manZone" in frame.columns and not pd.isna(frame["pff_manZone"].iloc[0]) else -1 + + play_id = "_".join(frame_id.split("_")[:2]) + frame_num = int(frame_id.split("_")[-1]) + + batch.append({"frameUniqueId": frame_id, "uniqueId": play_id, "frameId": frame_num, "zone_prob": zone_prob, "man_prob": man_prob, "pred": pred, "actual": actual}) + + # Flush batch to CSV to keep RAM low + if len(batch) >= config["batch_size"]: + pd.DataFrame(batch).to_csv(weekly_predictions_path_csv, mode="a", header=not wrote_header, index=False) + wrote_header = True + batch.clear() + + # Flush tail + if batch: + pd.DataFrame(batch).to_csv(weekly_predictions_path_csv, mode="a", header=not wrote_header, index=False) + batch.clear() + + logging.info(f"Finished week {week_eval}... saved to week{week_eval}_preds.csv\n") + + # Merge week_df with preds (per-week, small) + preds_week = pd.read_csv(weekly_predictions_path_csv, usecols=["frameUniqueId", "zone_prob", "man_prob", "pred", "actual"]) + tracking_preds = week_df.merge(preds_week, on="frameUniqueId", how="left") + tracking_preds.to_csv(PROJECT_ROOT / "data" / "inference" / f"tracking_week_{week_eval}_preds.csv", index=False) + + # Free RAM before next week + del week_df, tracking_df_polars, preds_week, tracking_preds + import gc + + gc.collect() + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/src/load_data.py b/src/load_data.py index 54dceaf..0839d87 100644 --- a/src/load_data.py +++ b/src/load_data.py @@ -1,70 +1,128 @@ #!/usr/bin/env python - """ -Loads raw parquet data for usage in training models +Loads raw parquet data into pandas DataFrames for modeling. """ import os -import pandas as pd -from typing import Tuple +from collections.abc import Iterable from pathlib import Path +import pandas as pd + from common.paths import PROJECT_ROOT class RawDataLoader: - def __init__(self): - if os.getenv("CI_DATA_ROOT"): - self.DATA_PATH = Path(os.getenv("CI_DATA_ROOT")) - else: - self.DATA_PATH = PROJECT_ROOT / "data" / "parquet" - self.games_df = None - self.plays_df = None - self.players_df = None - self.location_data_df = None - - def _load_parquet(self, filepath): - """Load parquet data with basic error handling""" - try: - return pd.read_parquet(filepath) - except Exception as e: - print(f"Error loading {filepath}: {e}") - return None - - def _load_base_data(self): - """Load games, plays, and players data""" - self.games_df = self._load_parquet(self.DATA_PATH / 'games.parquet') - self.plays_df = self._load_parquet(self.DATA_PATH / 'plays.parquet') - self.players_df = self._load_parquet(self.DATA_PATH / 'players.parquet') - - def _load_tracking_data(self, weeks=list[int]): - """Load tracking data for specified weeks""" - weekly_dfs = [] - - for week in weeks: - filepath = self.DATA_PATH / f'tracking_week_{week}.parquet' - df = self._load_parquet(filepath) - if df is not None: - weekly_dfs.append(df) - else: - print(f"Skipping week {week} due to load error.") - - self.location_data_df = pd.concat(weekly_dfs, ignore_index=True) - - def get_data(self, weeks: list[int] = range(1, 10)) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]: - """Return the processed data""" - - # Load data - self._load_base_data() - self._load_tracking_data(weeks=weeks) - - return (self.games_df, self.plays_df, self.players_df, self.location_data_df) - -def main(): - # Example usage so that I can selectively load in a few weeks to keep mem down - loader = RawDataLoader() - games_df, plays_df, players_df, location_data_df = loader.get_data(weeks=[1, 2]) + """ + Convenience loader for raw games, plays, players, and tracking parquet files. + """ -if __name__ == "__main__": - main() + def __init__(self) -> None: + """ + Builds a data loader for pulling raw parquet files. + + Inputs: + - None + + Outputs: + - Initialized data loader. + """ + + if os.getenv("CI_DATA_ROOT"): + self.DATA_PATH = Path(os.getenv("CI_DATA_ROOT")) + else: + self.DATA_PATH = PROJECT_ROOT / "data" / "parquet" + self.games_df = None + self.plays_df = None + self.players_df = None + self.location_data_df = None + + def _load_parquet(self, filepath: Path) -> pd.DataFrame | None: + """ + Loads a parquet file with basic error handling. + + Inputs: + - filepath: Location of the parquet file. + + Outputs: + - df_or_none: DataFrame on success, None on failure. + """ + + try: + return pd.read_parquet(filepath) + except Exception as e: + print(f"Error loading {filepath}: {e}") + return None + + def _load_base_data(self) -> None: + """ + Pulls games, plays, and players tables into memory. + + Inputs: + - None. + + Outputs: + - Populates internal DataFrame attributes. + """ + + self.games_df = self._load_parquet(self.DATA_PATH / "games.parquet") + self.plays_df = self._load_parquet(self.DATA_PATH / "plays.parquet") + self.players_df = self._load_parquet(self.DATA_PATH / "players.parquet") + + def _load_tracking_data(self, weeks: Iterable[int]) -> None: + """ + Loads weekly tracking parquet files and concatenates them. + Inputs: + - weeks: Iterable of week numbers to include. + + Outputs: + - Populates location_data_df with concatenated rows. + """ + + weekly_dfs = [] + + for week in weeks: + filepath = self.DATA_PATH / f"tracking_week_{week}.parquet" + df = self._load_parquet(filepath) + if df is not None: + weekly_dfs.append(df) + else: + print(f"Skipping week {week} due to load error.") + + self.location_data_df = pd.concat(weekly_dfs, ignore_index=True) + + def get_data(self, weeks: Iterable[int] = range(1, 10)) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """ + Loads requested datasets and returns them as a tuple. + + Inputs: + - weeks: Weeks of tracking data to pull. + + Outputs: + - games_df, plays_df, players_df, location_data_df in that order. + """ + + self._load_base_data() + self._load_tracking_data(weeks=weeks) + + return (self.games_df, self.plays_df, self.players_df, self.location_data_df) + + +def main() -> None: + """ + Demonstrates loading a subset of weeks for quick inspection. + + Inputs: + - None (uses defaults). + + Outputs: + - Loads data into DataFrames for manual exploration. + """ + + loader = RawDataLoader() + games_df, plays_df, players_df, location_data_df = loader.get_data(weeks=[1, 2]) + + +if __name__ == "__main__": + main() diff --git a/src/models/__init__.py b/src/models/__init__.py index e69de29..7293dc1 100644 --- a/src/models/__init__.py +++ b/src/models/__init__.py @@ -0,0 +1,4 @@ +#!/usr/bin/env python +""" +Model wrappers and architectures for coverage classification. +""" diff --git a/src/models/knn.py b/src/models/knn.py index 20ffbc3..d8ad06d 100644 --- a/src/models/knn.py +++ b/src/models/knn.py @@ -1,11 +1,31 @@ #!/usr/bin/env python - """ -Contains class for default sklearn K-Nearest-Neighbor model for general usage +Wraps a default k-nearest-neighbor classifier for reuse. """ from sklearn.neighbors import KNeighborsClassifier -class KNNModel(): - def __init__(self, n_neighbors=5): - self.model = KNeighborsClassifier(n_neighbors=n_neighbors) \ No newline at end of file + +class KNNModel: + """ + Thin wrapper that instantiates a sklearn KNN classifier. + + Inputs: + - n_neighbors: Number of neighbors to consider. + + Outputs: + - model attribute holds the configured classifier. + """ + + def __init__(self, n_neighbors: int = 5) -> None: + """ + Builds the underlying KNeighborsClassifier. + + Inputs: + - n_neighbors: Number of neighbors to use. + + Outputs: + - Initializes the self.model attribute. + """ + + self.model = KNeighborsClassifier(n_neighbors=n_neighbors) diff --git a/src/models/lgb.py b/src/models/lgb.py index 5e52bc1..90ab02b 100644 --- a/src/models/lgb.py +++ b/src/models/lgb.py @@ -1,11 +1,33 @@ #!/usr/bin/env python - """ -Contains class for light gradient boosting model for general usage +Wraps a LightGBM classifier for reuse. """ import lightgbm as lgb -class LGBModel(): - def __init__(self, n_estimators=100, class_weight='balanced', random_state=42): - self.model = lgb.LGBMClassifier(n_estimators=n_estimators, class_weight=class_weight, random_state=random_state) \ No newline at end of file + +class LGBModel: + """ + Thin wrapper around lightgbm.LGBMClassifier. + + Inputs: + - n_estimators/class_weight/random_state: Core model settings. + + Outputs: + - model attribute holds the configured classifier. + """ + + def __init__(self, n_estimators: int = 100, class_weight: str = "balanced", random_state: int = 42) -> None: + """ + Builds the underlying LightGBM classifier. + + Inputs: + - n_estimators: Number of boosting rounds. + - class_weight: Strategy for class weighting. + - random_state: Seed for reproducibility. + + Outputs: + - Initializes the self.model attribute. + """ + + self.model = lgb.LGBMClassifier(n_estimators=n_estimators, class_weight=class_weight, random_state=random_state) diff --git a/src/models/log.py b/src/models/log.py index 1b9fbad..8ab8453 100644 --- a/src/models/log.py +++ b/src/models/log.py @@ -1,11 +1,32 @@ #!/usr/bin/env python - """ -Contains class for default sklearn logisitc regression model for general usage +Wraps a default logistic regression classifier for reuse. """ from sklearn.linear_model import LogisticRegression -class LogisticModel(): - def __init__(self, max_iter=1000, class_weight='balanced'): - self.model = LogisticRegression(max_iter=max_iter, class_weight=class_weight) \ No newline at end of file + +class LogisticModel: + """ + Thin wrapper around sklearn's LogisticRegression. + + Inputs: + - max_iter/class_weight: Core model settings. + + Outputs: + - model attribute holds the configured classifier. + """ + + def __init__(self, max_iter: int = 1000, class_weight: str = "balanced") -> None: + """ + Builds the underlying LogisticRegression model. + + Inputs: + - max_iter: Maximum solver iterations. + - class_weight: Strategy for class weighting. + + Outputs: + - Initializes the self.model attribute. + """ + + self.model = LogisticRegression(max_iter=max_iter, class_weight=class_weight) diff --git a/src/models/lstm.py b/src/models/lstm.py index 0f06645..615a997 100644 --- a/src/models/lstm.py +++ b/src/models/lstm.py @@ -1,29 +1,62 @@ #!/usr/bin/env python - """ -Contains class for default long short term memory model for general usage +Defines an LSTM classifier for sequence-based coverage prediction. """ -from torch import nn + +from torch import Tensor, nn + class LSTMClassifier(nn.Module): - def __init__(self, input_size, hidden_size, num_layers, dropout, bidir, num_classes): - super().__init__() - self.lstm = nn.LSTM( - input_size=input_size, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout if num_layers > 1 else 0.0, - bidirectional=bidir, - ) - out_dim = hidden_size * (2 if bidir else 1) - self.head = nn.Sequential( - nn.LayerNorm(out_dim), - nn.Linear(out_dim, num_classes) - ) - - def forward(self, x): # x: (B, T, F) - out, (h_n, c_n) = self.lstm(x) # out: (B, T, H) - last = out[:, -1, :] # use last timestep representation - logits = self.head(last) # (B, C) - return logits \ No newline at end of file + """ + LSTM-based model that pools the final timestep for classification. + + Inputs: + - input_size/hidden_size/num_layers/dropout/bidir/num_classes: Network shape and output size. + + Outputs: + - forward returns logits for the requested number of classes. + """ + + def __init__(self, input_size: int, hidden_size: int, num_layers: int, dropout: float, bidir: bool, num_classes: int) -> None: + """ + Builds the LSTM backbone and classification head. + + Inputs: + - input_size: Number of features per timestep. + - hidden_size: Hidden dimension of the LSTM. + - num_layers: Number of stacked LSTM layers. + - dropout: Dropout rate between LSTM layers. + - bidir: Whether to use a bidirectional LSTM. + - num_classes: Number of output classes. + + Outputs: + - Initializes the network modules. + """ + + super().__init__() + self.lstm = nn.LSTM( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout if num_layers > 1 else 0.0, + bidirectional=bidir, + ) + out_dim = hidden_size * (2 if bidir else 1) + self.head = nn.Sequential(nn.LayerNorm(out_dim), nn.Linear(out_dim, num_classes)) + + def forward(self, x: Tensor) -> Tensor: + """ + Runs a forward pass on a batch of sequences. + + Inputs: + - x: Tensor shaped [batch, timesteps, features]. + + Outputs: + - logits: Tensor shaped [batch, num_classes]. + """ + + out, _ = self.lstm(x) + last = out[:, -1, :] + logits = self.head(last) + return logits diff --git a/src/models/mlp.py b/src/models/mlp.py index 93f8759..10311b8 100644 --- a/src/models/mlp.py +++ b/src/models/mlp.py @@ -1,21 +1,48 @@ #!/usr/bin/env python - """ -Contains class for default multi-layer-perceptron model for general usage +Provides a simple feedforward neural network wrapper. """ import torch + class MLPModel(torch.nn.Module): - def __init__(self, input_size, hidden_size, output_size): - super().__init__() + """ + Basic multilayer perceptron with one hidden layer. + + Inputs: + - input_size/hidden_size/output_size: Layer dimensions. + + Outputs: + - forward returns logits for the configured output size. + """ + + def __init__(self, input_size: int, hidden_size: int, output_size: int) -> None: + """ + Constructs the MLP layers. + + Inputs: + - input_size: Number of input features. + - hidden_size: Size of hidden layer. + - output_size: Number of output units. + + Outputs: + - Initializes the torch module layers. + """ + + super().__init__() + + self.layers = torch.nn.Sequential(torch.nn.Linear(input_size, hidden_size), torch.nn.ReLU(), torch.nn.Linear(hidden_size, output_size)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Runs a forward pass through the MLP. + Inputs: + - x: Input tensor of shape [batch, input_size]. - self.layers = torch.nn.Sequential( - torch.nn.Linear(input_size, hidden_size), - torch.nn.ReLU(), - torch.nn.Linear(hidden_size, output_size) - ) + Outputs: + - logits: Tensor of shape [batch, output_size]. + """ - def forward(self, x): - return self.layers(x) \ No newline at end of file + return self.layers(x) diff --git a/src/models/rfr.py b/src/models/rfr.py index 5d5ca39..3a432dc 100644 --- a/src/models/rfr.py +++ b/src/models/rfr.py @@ -1,11 +1,33 @@ #!/usr/bin/env python - """ -Contains class for default sklearn random forest model for general usage +Wraps a default random forest classifier for reuse. """ from sklearn.ensemble import RandomForestClassifier -class RFRModel(): - def __init__(self, n_estimators=100, class_weight='balanced', random_state=42): - self.model = RandomForestClassifier(n_estimators=n_estimators, class_weight=class_weight, random_state=random_state) \ No newline at end of file + +class RFRModel: + """ + Thin wrapper around sklearn's RandomForestClassifier. + + Inputs: + - n_estimators/class_weight/random_state: Core model settings. + + Outputs: + - model attribute holds the configured classifier. + """ + + def __init__(self, n_estimators: int = 100, class_weight: str = "balanced", random_state: int = 42) -> None: + """ + Builds the underlying RandomForestClassifier. + + Inputs: + - n_estimators: Number of trees. + - class_weight: Strategy for class weighting. + - random_state: Seed for reproducibility. + + Outputs: + - Initializes the self.model attribute. + """ + + self.model = RandomForestClassifier(n_estimators=n_estimators, class_weight=class_weight, random_state=random_state) diff --git a/src/models/svc.py b/src/models/svc.py index d4810e8..472d7c4 100644 --- a/src/models/svc.py +++ b/src/models/svc.py @@ -1,9 +1,33 @@ #!/usr/bin/env python - """ -Contains class for default SVC model +Wraps a support vector classifier configured for balanced classes. """ from sklearn.svm import SVC -models['svm'] = SVC(kernel='rbf', class_weight='balanced', probability=True, random_state=42) \ No newline at end of file + +class SVCModel: + """ + Thin wrapper around sklearn's SVC with probability outputs. + + Inputs: + - kernel/class_weight/random_state: Core model settings. + + Outputs: + - model attribute holds the configured classifier. + """ + + def __init__(self, kernel: str = "rbf", class_weight: str = "balanced", random_state: int = 42) -> None: + """ + Builds the underlying SVC model with probability estimates enabled. + + Inputs: + - kernel: Kernel type for SVC. + - class_weight: Strategy for class weighting. + - random_state: Seed for reproducibility. + + Outputs: + - Initializes the self.model attribute. + """ + + self.model = SVC(kernel=kernel, class_weight=class_weight, probability=True, random_state=random_state) diff --git a/src/models/transformer.py b/src/models/transformer.py index a6c81f4..0198fa2 100644 --- a/src/models/transformer.py +++ b/src/models/transformer.py @@ -1,51 +1,119 @@ #!/usr/bin/env python - """ -Contains class for man zone transformer model +Defines the transformer architecture used for man/zone classification and holds a fcn to create a model. """ import torch import torch.nn as nn + class ManZoneTransformer(nn.Module): + """ + Transformer encoder that ingests per-player features and predicts coverage. + + Inputs: + - feature_len/model_dim/num_heads/num_layers/dim_feedforward/dropout/output_dim: Model shape and output size. + + Outputs: + - forward returns logits for each class. + """ + + def __init__( + self, + feature_len: int = 5, + model_dim: int = 64, + num_heads: int = 2, + num_layers: int = 4, + dim_feedforward: int = 256, + dropout: float = 0.1, + output_dim: int = 2, + ) -> None: + """ + Builds the transformer layers and pooling head. + + Inputs: + - feature_len: Number of input features per player. + - model_dim: Embedding dimension. + - num_heads: Attention heads per encoder layer. + - num_layers: Number of encoder layers. + - dim_feedforward: Hidden size of the feedforward sublayer. + - dropout: Dropout rate across the model. + - output_dim: Number of output classes. + + Outputs: + - Initialized model ready for training. + """ + + super().__init__() + self.feature_norm_layer = nn.BatchNorm1d(feature_len) + + self.feature_embedding_layer = nn.Sequential( + nn.Linear(feature_len, model_dim), + nn.ReLU(), + nn.LayerNorm(model_dim), + nn.Dropout(dropout), + ) + + transformer_encoder_layer = nn.TransformerEncoderLayer( + d_model=model_dim, + nhead=num_heads, + dim_feedforward=dim_feedforward, + dropout=dropout, + batch_first=True, + ) + self.transformer_encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers=num_layers) + + self.player_pooling_layer = nn.AdaptiveAvgPool1d(1) + + self.decoder = nn.Sequential( + nn.Linear(model_dim, model_dim), + nn.ReLU(), + nn.Dropout(dropout), + nn.Linear(model_dim, model_dim // 4), + nn.ReLU(), + nn.LayerNorm(model_dim // 4), + nn.Linear(model_dim // 4, output_dim), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Runs a forward pass over player features. + + Inputs: + - x: Tensor shaped [batch, num_players, feature_len]. + + Outputs: + - logits: Tensor shaped [batch, output_dim] representing class scores. + """ + + # x shape: (batch_size, num_players, feature_len) + x = self.feature_norm_layer(x.permute(0, 2, 1)).permute(0, 2, 1) + x = self.feature_embedding_layer(x) + x = self.transformer_encoder(x) + x = self.player_pooling_layer(x.permute(0, 2, 1)).squeeze(-1) + x = self.decoder(x) + return x + + +def create_transformer_model(config: dict[str, int | float]) -> ManZoneTransformer: + """ + Instantiates a ManZoneTransformer from a config mapping. + + Inputs: + - config: Dictionary containing model_dim, num_heads, num_layers, multiplier, and dropout. + + Outputs: + - model: Configured ManZoneTransformer instance. + """ + + model = ManZoneTransformer( + feature_len=5, # num of input features (x, y, v_x, v_y, defense) + model_dim=int(config["model_dim"]), # from ray tune or loaded + num_heads=int(config["num_heads"]), # from ray tune or loaded + num_layers=int(config["num_layers"]), # from ray tune or loaded + dim_feedforward=int(config["model_dim"]) * int(config["multiplier"]), # from ray tune or loaded + dropout=float(config["dropout"]), # from ray tune or loaded + output_dim=2, # man or zone classification + ) - def __init__(self, feature_len=5, model_dim=64, num_heads=2, num_layers=4, dim_feedforward=256, dropout=0.1, output_dim=2): - super(ManZoneTransformer, self).__init__() - self.feature_norm_layer = nn.BatchNorm1d(feature_len) - - self.feature_embedding_layer = nn.Sequential( - nn.Linear(feature_len, model_dim), - nn.ReLU(), - nn.LayerNorm(model_dim), - nn.Dropout(dropout), - ) - - transformer_encoder_layer = nn.TransformerEncoderLayer( - d_model=model_dim, - nhead=num_heads, - dim_feedforward=dim_feedforward, - dropout=dropout, - batch_first=True, - ) - self.transformer_encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers=num_layers) - - self.player_pooling_layer = nn.AdaptiveAvgPool1d(1) - - self.decoder = nn.Sequential( - nn.Linear(model_dim, model_dim), - nn.ReLU(), - nn.Dropout(dropout), - nn.Linear(model_dim, model_dim // 4), - nn.ReLU(), - nn.LayerNorm(model_dim // 4), - nn.Linear(model_dim // 4, output_dim), - ) - - def forward(self, x): - # x shape: (batch_size, num_players, feature_len) - x = self.feature_norm_layer(x.permute(0, 2, 1)).permute(0, 2, 1) - x = self.feature_embedding_layer(x) - x = self.transformer_encoder(x) - x = self.player_pooling_layer(x.permute(0, 2, 1)).squeeze(-1) - x = self.decoder(x) - return x \ No newline at end of file + return model diff --git a/src/models/xgb.py b/src/models/xgb.py index 94b4b6e..95251e2 100644 --- a/src/models/xgb.py +++ b/src/models/xgb.py @@ -1,11 +1,33 @@ #!/usr/bin/env python - """ -Contains default xgboost model +Wraps a default XGBoost classifier for reuse. """ import xgboost as xgb -class XGBModel(): - def __init__(self, objective='binary:logistic', n_estimators=100, random_state=42): - self.model = xgb.XGBClassifier(objective=objective, n_estimators=n_estimators, random_state=random_state) \ No newline at end of file + +class XGBModel: + """ + Thin wrapper around xgboost.XGBClassifier. + + Inputs: + - objective/n_estimators/random_state: Core model settings. + + Outputs: + - model attribute holds the configured classifier. + """ + + def __init__(self, objective: str = "binary:logistic", n_estimators: int = 100, random_state: int = 42) -> None: + """ + Builds the underlying XGBoost classifier. + + Inputs: + - objective: Loss function to optimize. + - n_estimators: Number of trees. + - random_state: Seed for reproducibility. + + Outputs: + - Initializes the self.model attribute. + """ + + self.model = xgb.XGBClassifier(objective=objective, n_estimators=n_estimators, random_state=random_state) diff --git a/src/old/train_xgb.py b/src/old/train_xgb.py index 4ccbaa6..aa0ddbb 100644 --- a/src/old/train_xgb.py +++ b/src/old/train_xgb.py @@ -1,268 +1,287 @@ #!/usr/bin/env python - """ -Trains xgboost model on location tracking data +Legacy script to train and evaluate an XGBoost model on coverage data. """ -# Standard imports + +import logging import os -import random -import time import pickle -import logging -import json -import joblib - -# General imports -import numpy as np -import pandas as pd +import time -# Plotting imports -import seaborn as sns +import joblib import matplotlib.pyplot as plt - -# ML utils -from sklearn.model_selection import StratifiedKFold, cross_val_score, cross_val_predict, train_test_split -from sklearn.metrics import roc_auc_score, log_loss, precision_score, recall_score, f1_score, classification_report, roc_curve, RocCurveDisplay +import mlflow +import numpy as np +from common.data_loader import DataLoader +from coverage.process_coverage import create_coverage_data +from sklearn.metrics import classification_report, f1_score, log_loss, precision_score, recall_score, roc_auc_score, roc_curve +from sklearn.model_selection import StratifiedKFold, cross_val_score, train_test_split from sklearn.preprocessing import StandardScaler +from xgboost import XGBClassifier -# MLflow -import mlflow from common.mlflow import setup_mlflow +from common.paths import PROJECT_ROOT -# Models -from xgboost import XGBClassifier -# Local imports -from common.data_loader import DataLoader -from coverage.process_coverage import create_coverage_data +def load_data() -> dict[str, np.ndarray]: + """ + Loads preprocessed coverage features and labels from disk. -# Local model imports -from common.models.log import LogisticModel -from common.models.lgb import LGBModel -#from common.models.xgb import XGBModel -#from common.models.svc import SVC -from common.models.rfr import RFRModel -from common.models.mlp import MLPModel -from common.paths import PROJECT_ROOT + Inputs: + - None (uses PROJECT_ROOT paths). + + Outputs: + - data_dict: Dictionary containing feature and label arrays. + """ + + base_path = PROJECT_ROOT / "data" / "coverage" + data_file_list = ["x", "y"] + data_dict = {} + for file in data_file_list: + file_name = file + ".pkl" + file_path = base_path / file_name + if not os.path.exists(file_path): + raise FileNotFoundError(f"Required data file {file_name} not found in {base_path}") + else: + with open(file_path, "rb") as f: + data_dict[file] = pickle.load(f) + + return data_dict + + +def plot_roc(y_test: np.ndarray, y_proba: np.ndarray) -> None: + """ + Plots and logs an ROC curve for the model predictions. + + Inputs: + - y_test: Ground-truth labels. + - y_proba: Predicted class probabilities. + + Outputs: + - Saves and logs ROC plot artifact via MLflow. + """ + + fpr, tpr, thresholds = roc_curve(y_test, y_proba[:, 1], pos_label=1) + plt.figure() + plt.plot(fpr, tpr, label=f"ROC Curve (AUC = {roc_auc_score(y_test, y_proba[:, 1]):.4f})") + plt.plot([0, 1], [0, 1], linestyle="--", color="gray") + plt.xlabel("False Positive Rate") + plt.ylabel("True Positive Rate") + plt.title("ROC Curve") + plt.legend(loc="lower right") + plt.grid(True) + image_name = "man_zone_roc_auc.png" + image_path = PROJECT_ROOT / "output" / "coverage" / image_name + plt.savefig(image_path, dpi=200) + mlflow.log_artifact(image_path, artifact_path="plots") -def load_data() -> dict[str]: - base_path = PROJECT_ROOT / "data" / "coverage" - data_file_list = ['x', 'y'] - data_dict = {} - for file in data_file_list: - file_name = file + '.pkl' - file_path = base_path / file_name - if not os.path.exists(file_path): - raise FileNotFoundError(f"Required data file {file_name} not found in {base_path}") - else: - with open(file_path, 'rb') as f: - data_dict[file] = pickle.load(f) - - return data_dict - -def plot_roc(y_test, y_proba) -> None: - fpr, tpr, thresholds = roc_curve(y_test, y_proba[:, 1], pos_label=1) - plt.figure() - plt.plot(fpr, tpr, label=f'ROC Curve (AUC = {roc_auc_score(y_test, y_proba[:, 1]):.4f})') - plt.plot([0, 1], [0, 1], linestyle='--', color='gray') - plt.xlabel('False Positive Rate') - plt.ylabel('True Positive Rate') - plt.title(f'ROC Curve') - plt.legend(loc='lower right') - plt.grid(True) - image_name = f'man_zone_roc_auc.png' - image_path = PROJECT_ROOT / "output" / "coverage" / image_name - plt.savefig(image_path, dpi=200) - mlflow.log_artifact(image_path, artifact_path="plots") def model_man_vs_zone() -> None: + """ + Trains and evaluates an XGBoost model for man vs. zone coverage. + + Inputs: + - None (loads data from disk and uses fixed params). + + Outputs: + - Logs metrics and artifacts to MLflow. + """ + + # Inputs + RUN_DATA_PROCESSING = False + + ########################################### + # Get data + ########################################### + # Run data processing if neccesary + if RUN_DATA_PROCESSING: + # Get raw data + loader = DataLoader() + games_df, plays_df, players_df, location_data_df = loader.get_data(weeks=[week for week in range(1, 10)]) + + # Process data + create_coverage_data(games_df, plays_df, players_df, location_data_df) # This will save down ML ready data to data/coverage/*.pkl + + # Get saved data locally + data_dict = load_data() + x_data = data_dict["x"] + y_data = data_dict["y"] + + ########################################### + # Split and standarize data + ########################################### + # Get x and y da + logging.info("Scaling and saving training and test data...") + + # Splittys + x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.2, stratify=y_data, random_state=42) + + # Standarize/scale data + scaler = StandardScaler() + x_train_scaled = scaler.fit_transform(x_train) + x_test_scaled = scaler.transform(x_test) + + ########################################### + # Run training for models + ########################################### + # NOTES + # - Consider oversampling/undersampling techniues like SMOTE, RandomOverSampler + # - Organize the data into positions and then analyze feature importance + # - Grid search on parameters + # - Try additional tree based models XGBoost and LightGBM + + # Setup MLflow + setup_mlflow(experiment_name="coverage-man-vs-zone") + + # Define 10-fold stratified cross-validation + skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42) + + # Xgb training + xgbModel = XGBClassifier( + objective="binary:logistic", + n_estimators=1000, # use more trees + early stopping when I add it later + learning_rate=0.05, + max_depth=6, + subsample=0.8, + colsample_bytree=0.8, + tree_method="hist", # fast histogram algo + device="cuda", # GPU + random_state=42, + ) + + # MLFlow wrapped training + with mlflow.start_run(run_name="xgb-10fold"): + # Log some dataset meta (sizes, class balance) + mlflow.log_params({"cv_folds": 10, "test_size": 0.2, "shuffle": True, "random_state": 42}) + + # Log class distribution + _, counts = np.unique(y_train, return_counts=True) + mlflow.log_metrics( + { + "train_class0": int(counts[0]), + "train_class1": int(counts[1]), + "train_size": int(x_train.shape[0]), + "test_size": int(x_test.shape[0]), + } + ) + + # Save scaler + artifact_path = PROJECT_ROOT / "output" / "coverage" + artifact_path.mkdir(parents=True, exist_ok=True) + joblib.dump(scaler, artifact_path / "standard_scaler.pkl") + mlflow.log_artifact(artifact_path / "standard_scaler.pkl", artifact_path="preprocessing") + + # Enable autologging so the fitted model is captured + mlflow.xgboost.autolog(log_model_signatures=True, log_input_examples=True) + + # Train + train_xgb(model=xgbModel, skf=skf, x_train=x_train_scaled, y_train=y_train, x_test=x_test_scaled, y_test=y_test) + + +def train_xgb( + model: XGBClassifier, + skf: StratifiedKFold, + x_train: np.ndarray, + y_train: np.ndarray, + x_test: np.ndarray, + y_test: np.ndarray, +) -> None: + """ + + Performs cross-validation, training, and evaluation for the XGB model. + + Inputs: + - model: XGBClassifier instance. + - skf: StratifiedKFold splitter. + - x_train/y_train: Training features and labels. + - x_test/y_test: Test features and labels. + + Outputs: + - Logs metrics, artifacts, and model to MLflow. + """ + start_time = time.time() + + logging.info("Starting K-Fold Cross-Validation...:") + cv_scores = cross_val_score(model, x_train, y_train, cv=skf, scoring="roc_auc") + + # Log per-fold and summary CV AUC + for i, s in enumerate(cv_scores, 1): + mlflow.log_metric("cv_auc", float(s), step=i) + mlflow.log_metric("cv_auc_mean", float(cv_scores.mean())) + mlflow.log_metric("cv_auc_std", float(cv_scores.std())) + + print(f"10-Fold Cross-Validation ROC AUC Scores: {cv_scores}") + print(f"Mean ROC AUC: {cv_scores.mean():.4f} (+/- {cv_scores.std():.4f})") + + # Fit on full training split (autolog will capture this model) + model.fit(x_train, y_train) + + # Predict on test data + y_pred = model.predict(x_test) + y_proba = model.predict_proba(x_test) + + # Metrics on Test Data + prec = precision_score(y_test, y_pred, pos_label=1) + rec = recall_score(y_test, y_pred, pos_label=1) + f1 = f1_score(y_test, y_pred, pos_label=1) + ll = log_loss(y_test, y_proba) + auc_ = roc_auc_score(y_test, y_proba[:, 1]) + + print(f"Precision (Man): {prec:.4f}") + print(f"Recall (Man): {rec:.4f}") + print(f"F1 Score (Man): {f1:.4f}") + print(f"Log Loss: {ll:.4f}") + print(f"Overall ROC AUC: {auc_:.4f}") + print(classification_report(y_test, y_pred, target_names=["Zone", "Man"])) + + # Log test metrics + mlflow.log_metrics( + { + "test_precision_pos1": float(prec), + "test_recall_pos1": float(rec), + "test_f1_pos1": float(f1), + "test_log_loss": float(ll), + "test_auc": float(auc_), + } + ) + + # Log the chosen hyperparameters as params (autolog also captures). + try: + mlflow.log_params(model.get_params()) + except Exception: + pass + + # Save classification report as an artifact + report_txt = classification_report(y_test, y_pred, target_names=["Zone", "Man"]) + artifact_path = PROJECT_ROOT / "output" / "coverage" + with open(artifact_path / "classification_report.txt", "w") as f: + f.write(report_txt) + mlflow.log_artifact(artifact_path / "classification_report.txt", artifact_path="reports") + + # Plot & log ROC curve + plot_roc(y_test, y_proba) + + # Log model signature + try: + # Just grab a few vals which is sufficient for artifacts + X_example = x_train[:5] + # y_example = y_train[:5] # TODO: Not used + sig = mlflow.models.infer_signature(X_example, model.predict_proba(X_example)) + mlflow.sklearn.log_model(sk_model=model, artifact_path="model_relogged", signature=sig, input_example=X_example) + except Exception as e: + logging.warning(f"Signature logging skipped: {e}") + + # Record training duration time + end_time = time.time() + training_duration = end_time - start_time + logging.info(f"Model generation time: {training_duration:.2f} seconds") + mlflow.log_metric("training_duration_sec", float(training_duration)) - # Inputs - RUN_DATA_PROCESSING = False - - ########################################### - # Get data - ########################################### - # Run data processing if neccesary - if RUN_DATA_PROCESSING: - - # Get raw data - loader = DataLoader() - games_df, plays_df, players_df, location_data_df = loader.get_data(weeks=[week for week in range (1,10)]) - - # Process data - create_coverage_data(games_df, plays_df, players_df, location_data_df) # This will save down ML ready data to data/coverage/*.pkl - - # Get saved data locally - data_dict = load_data() - x_data = data_dict['x'] - y_data = data_dict['y'] - - ########################################### - # Split and standarize data - ########################################### - # Get x and y da - logging.info("Scaling and saving training and test data...") - - # Splittys - x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.2, stratify=y_data, random_state=42) - - # Standarize/scale data - scaler = StandardScaler() - x_train_scaled = scaler.fit_transform(x_train) - x_test_scaled = scaler.transform(x_test) - - ########################################### - # Run training for models - ########################################### - # NOTES - # - Consider oversampling/undersampling techniues like SMOTE, RandomOverSampler - # - Organize the data into positions and then analyze feature importance - # - Grid search on parameters - # - Try additional tree based models XGBoost and LightGBM - - # Setup MLflow - setup_mlflow(experiment_name="coverage-man-vs-zone") - - # Define 10-fold stratified cross-validation - skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42) - - # Xgb training - xgbModel = XGBClassifier( - objective="binary:logistic", - n_estimators=1000, # use more trees + early stopping when I add it later - learning_rate=0.05, - max_depth=6, - subsample=0.8, - colsample_bytree=0.8, - tree_method="hist", # fast histogram algo - device="cuda", # GPU - random_state=42 - ) - - # MLFlow wrapped training - with mlflow.start_run(run_name="xgb-10fold"): - # Log some dataset meta (sizes, class balance) - mlflow.log_params({ - "cv_folds": 10, - "test_size": 0.2, - "shuffle": True, - "random_state": 42 - }) - - # Log class distribution - _, counts = np.unique(y_train, return_counts=True) - mlflow.log_metrics({ - "train_class0": int(counts[0]), - "train_class1": int(counts[1]), - "train_size": int(x_train.shape[0]), - "test_size": int(x_test.shape[0]), - }) - - # Save scaler - artifact_path = PROJECT_ROOT / "output" / "coverage" - artifact_path.mkdir(parents=True, exist_ok=True) - joblib.dump(scaler, artifact_path / "standard_scaler.pkl") - mlflow.log_artifact(artifact_path / "standard_scaler.pkl", artifact_path="preprocessing") - - # Enable autologging so the fitted model is captured - mlflow.xgboost.autolog(log_model_signatures=True, log_input_examples=True) - - # Train - train_xgb( - model=xgbModel, - skf=skf, - x_train=x_train_scaled, y_train=y_train, - x_test=x_test_scaled, y_test=y_test - ) - -def train_xgb(model, skf, x_train, y_train, x_test, y_test) -> None: - start_time = time.time() - - logging.info("Starting K-Fold Cross-Validation...:") - cv_scores = cross_val_score(model, x_train, y_train, cv=skf, scoring='roc_auc') - - # Log per-fold and summary CV AUC - for i, s in enumerate(cv_scores, 1): - mlflow.log_metric("cv_auc", float(s), step=i) - mlflow.log_metric("cv_auc_mean", float(cv_scores.mean())) - mlflow.log_metric("cv_auc_std", float(cv_scores.std())) - - print(f"10-Fold Cross-Validation ROC AUC Scores: {cv_scores}") - print(f"Mean ROC AUC: {cv_scores.mean():.4f} (+/- {cv_scores.std():.4f})") - - # Fit on full training split (autolog will capture this model) - model.fit(x_train, y_train) - - # Predict on test data - y_pred = model.predict(x_test) - y_proba = model.predict_proba(x_test) - - # Metrics on Test Data - prec = precision_score(y_test, y_pred, pos_label=1) - rec = recall_score(y_test, y_pred, pos_label=1) - f1 = f1_score(y_test, y_pred, pos_label=1) - ll = log_loss(y_test, y_proba) - auc_ = roc_auc_score(y_test, y_proba[:, 1]) - - print(f"Precision (Man): {prec:.4f}") - print(f"Recall (Man): {rec:.4f}") - print(f"F1 Score (Man): {f1:.4f}") - print(f"Log Loss: {ll:.4f}") - print(f"Overall ROC AUC: {auc_:.4f}") - print(classification_report(y_test, y_pred, target_names=['Zone', 'Man'])) - - # Log test metrics - mlflow.log_metrics({ - "test_precision_pos1": float(prec), - "test_recall_pos1": float(rec), - "test_f1_pos1": float(f1), - "test_log_loss": float(ll), - "test_auc": float(auc_), - }) - - # Log the chosen hyperparameters as params (autolog also captures). - try: - mlflow.log_params(model.get_params()) - except Exception: - pass - - # Save classification report as an artifact - report_txt = classification_report(y_test, y_pred, target_names=['Zone', 'Man']) - artifact_path = PROJECT_ROOT / "output" / "coverage" - with open(artifact_path / "classification_report.txt", "w") as f: - f.write(report_txt) - mlflow.log_artifact(artifact_path / "classification_report.txt", artifact_path="reports") - - # Plot & log ROC curve - plot_roc(y_test, y_proba) - - # Log model signature - try: - # Just grab a few vals which is sufficient for artifacts - X_example = x_train[:5] - y_example = y_train[:5] - sig = mlflow.models.infer_signature(X_example, model.predict_proba(X_example)) - mlflow.sklearn.log_model( - sk_model=model, - artifact_path="model_relogged", - signature=sig, - input_example=X_example - ) - except Exception as e: - logging.warning(f"Signature logging skipped: {e}") - - # Record training duration time - end_time = time.time() - training_duration = end_time - start_time - logging.info(f"Model generation time: {training_duration:.2f} seconds") - mlflow.log_metric("training_duration_sec", float(training_duration)) if __name__ == "__main__": - - # Configure basic logging - logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') - logger = logging.getLogger(__name__) + # Configure basic logging + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + logger = logging.getLogger(__name__) - # Run model - model_man_vs_zone() + # Run model + model_man_vs_zone() diff --git a/src/train_lstm.py b/src/train_lstm.py index 45a401f..dc3bef7 100644 --- a/src/train_lstm.py +++ b/src/train_lstm.py @@ -1,539 +1,667 @@ #!/usr/bin/env python - """ -Trains LSTM model on location tracking data - -Requires raw location tracking data in nfl/data/parquet - -Will produce classification report, confusion matrix, and ROC curve for best model +Trains an LSTM model to classify man versus zone coverage from tracking data. """ -# Base -import os -import random -import time -import pickle import logging -# Common -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt - -# Sklearn utils -from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import train_test_split -from sklearn.utils.class_weight import compute_class_weight -from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay, roc_curve, auc, precision_recall_fscore_support import joblib - -# PyTorch +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd import torch -from torch.utils.data import TensorDataset, DataLoader import torch.nn as nn +from common.models.lstm import LSTMClassifier +from sklearn.metrics import ConfusionMatrixDisplay, auc, classification_report, confusion_matrix, precision_recall_fscore_support, roc_curve +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.utils.class_weight import compute_class_weight +from torch.utils.data import DataLoader, TensorDataset -# Local from common.decorators import time_fcn -from common.models.lstm import LSTMClassifier + +PlayKey = tuple[int, int] +SeriesDict = dict[PlayKey, np.ndarray] + @time_fcn -def filter_plays(plays_df: pd.DataFrame) -> pd.DataFrame: - """Filter plays to include only relevant passing plays for coverage analysis.""" +def filter_plays(plays_df: pd.DataFrame, location_data_df: pd.DataFrame) -> pd.DataFrame: + """ + Filters plays down to valid pass attempts with man/zone labels. + + Inputs: + - plays_df: Raw plays table with penalty and label info. - # Create a copy - filtered_plays_df = plays_df.copy() + Outputs: + - filtered_plays_df: Plays limited to pass attempts with man/zone tags. + """ - # Find the starting plays - logging.info("Filtering data...") - original_play_length = len(filtered_plays_df) - logging.info(f'Total plays: {original_play_length}') + # Create a copy + filtered_plays_df = plays_df.copy() - # Filter out penalties - filtered_plays_df = filtered_plays_df[filtered_plays_df['playNullifiedByPenalty'] == 'N'] - # Filter out rows with 'PENALTY' in the 'playDescription' column - filtered_plays_df = filtered_plays_df[~filtered_plays_df['playDescription'].str.contains("PENALTY", na=False)] - logging.info(f'Total plays after filtering out penalties: {len(filtered_plays_df)}') + # Find the starting plays + logging.info("Filtering data...") + original_play_length = len(filtered_plays_df) + logging.info(f"Total plays: {original_play_length}") - # Filter down to valid Man or Zone defensive play calls - filtered_plays_df = filtered_plays_df[filtered_plays_df['pff_manZone'].isin(['Man', 'Zone'])] - logging.info(f'Total plays after filtering to valid Man or Zone classifications: {len(filtered_plays_df)}') + # Filter out penalties + filtered_plays_df = filtered_plays_df[filtered_plays_df["playNullifiedByPenalty"] == "N"] + # Filter out rows with 'PENALTY' in the 'playDescription' column + filtered_plays_df = filtered_plays_df[~filtered_plays_df["playDescription"].str.contains("PENALTY", na=False)] + logging.info(f"Total plays after filtering out penalties: {len(filtered_plays_df)}") - # Filter for only rows that indicate a pass play - filtered_plays_df = filtered_plays_df[filtered_plays_df['passResult'].notna()] - logging.info(f'Total plays after filtering to only pass plays: {len(filtered_plays_df)}') + # Filter down to valid Man or Zone defensive play calls + filtered_plays_df = filtered_plays_df[filtered_plays_df["pff_manZone"].isin(["Man", "Zone"])] + logging.info(f"Total plays after filtering to valid Man or Zone classifications: {len(filtered_plays_df)}") - # Filter for only plays where the win probablity isn't lopsided (between 0.1 and 0.9), likelyhood that there's more movement - # filtered_plays_df = filtered_plays_df[(filtered_plays_df['preSnapHomeTeamWinProbability'] > 0.1) & (filtered_plays_df['preSnapHomeTeamWinProbability'] < 0.9)] - # logging.info(f'Total plays after filtering out garbage time: {len(filtered_plays_df)}') + # Filter for only rows that indicate a pass play + filtered_plays_df = filtered_plays_df[filtered_plays_df["passResult"].notna()] + logging.info(f"Total plays after filtering to only pass plays: {len(filtered_plays_df)}") - # Filter for only third down or fourth down plays - # filtered_plays_df = filtered_plays_df[filtered_plays_df['down'].isin([3, 4])] - # logging.info(f'Total plays after filtering for 3rd or 4th down: {len(filtered_plays_df)}') + # Filter for only plays where the win probablity isn't lopsided (between 0.1 and 0.9), likelyhood that there's more movement + # filtered_plays_df = filtered_plays_df[(filtered_plays_df['preSnapHomeTeamWinProbability'] > 0.1) & (filtered_plays_df['preSnapHomeTeamWinProbability'] < 0.9)] + # logging.info(f'Total plays after filtering out garbage time: {len(filtered_plays_df)}') - # Filter for plays that are in our gameIds (in location data df) - filtered_plays_df = filtered_plays_df[filtered_plays_df['gameId'].isin(location_data_df['gameId'].unique())] - logging.info(f'Total plays after making sure they are in our location data: {len(filtered_plays_df)}') + # Filter for only third down or fourth down plays + # filtered_plays_df = filtered_plays_df[filtered_plays_df['down'].isin([3, 4])] + # logging.info(f'Total plays after filtering for 3rd or 4th down: {len(filtered_plays_df)}') - # Log final columns - logging.info(filtered_plays_df.columns) + # TODO: Fix + # Filter for plays that are in our gameIds (in location data df) + filtered_plays_df = filtered_plays_df[filtered_plays_df["gameId"].isin(location_data_df["gameId"].unique())] + logging.info(f"Total plays after making sure they are in our location data: {len(filtered_plays_df)}") - # Cut down to columns we care about - keep_cols_from_plays = ['gameId', 'playId', 'possessionTeam', 'defensiveTeam', 'pff_manZone'] - filtered_plays_df = filtered_plays_df.loc[:, keep_cols_from_plays].drop_duplicates() + # Log final columns + logging.info(filtered_plays_df.columns) - # Make sure we don't have any NAs in this cut down col df - filtered_plays_df.dropna() - logging.info(f'Total plays after cutting down to our cols and dropping NAs: {len(filtered_plays_df)}') + # Cut down to columns we care about + keep_cols_from_plays = ["gameId", "playId", "possessionTeam", "defensiveTeam", "pff_manZone"] + filtered_plays_df = filtered_plays_df.loc[:, keep_cols_from_plays].drop_duplicates() + + # Make sure we don't have any NAs in this cut down col df + filtered_plays_df.dropna() + logging.info(f"Total plays after cutting down to our cols and dropping NAs: {len(filtered_plays_df)}") + + # Return + return filtered_plays_df - # Return - return filtered_plays_df @time_fcn def create_merged_df(location_data_df: pd.DataFrame, filtered_plays_df: pd.DataFrame) -> pd.DataFrame: - """Merge location data with filtered plays data to create a comprehensive dataset for coverage analysis.""" + """ + Merges filtered plays with presnap location rows and tags offense/defense sides. + + Inputs: + - location_data_df: Tracking data containing positions and frame info. + - filtered_plays_df: Plays already narrowed to labeled pass attempts. + + Outputs: + - merged_df: Presnap tracking rows with side tags and minimal columns. + """ - logging.info("Merging location data with filtered plays data...") + logging.info("Merging location data with filtered plays data...") - # Create a copy of the location tracking data, cut it down to columns we care about - loc_trimmed_df = location_data_df.copy() - keep_cols = [ - 'gameId', - 'playId', - 'nflId', - 'frameId', - 'frameType', - 'club', - 'x', - 'y', - 's', - 'a' - ] - loc_trimmed_df = location_data_df.loc[:, keep_cols] + # Create a copy of the location tracking data, cut it down to columns we care about + loc_trimmed_df = location_data_df.copy() + keep_cols = ["gameId", "playId", "nflId", "frameId", "frameType", "club", "x", "y", "s", "a"] + loc_trimmed_df = location_data_df.loc[:, keep_cols] - # Cut down location tracking data copy to only before the snap and where the team isn't valid - loc_trimmed_df = loc_trimmed_df[(loc_trimmed_df["frameType"] == "BEFORE_SNAP") & (loc_trimmed_df["club"] != "football")] + # Cut down location tracking data copy to only before the snap and where the team isn't valid + loc_trimmed_df = loc_trimmed_df[(loc_trimmed_df["frameType"] == "BEFORE_SNAP") & (loc_trimmed_df["club"] != "football")] - # See the merged df that has gameId, playId, frameID all before SNAP, with x, y, and offense/defense - logging.info(loc_trimmed_df.head()) + # See the merged df that has gameId, playId, frameID all before SNAP, with x, y, and offense/defense + logging.info(loc_trimmed_df.head()) - # Merge the two datasets such that we can have the possession and defensive team for each row - merged_df = pd.merge(filtered_plays_df, loc_trimmed_df, on=['gameId', 'playId'], how='inner') + # Merge the two datasets such that we can have the possession and defensive team for each row + merged_df = pd.merge(filtered_plays_df, loc_trimmed_df, on=["gameId", "playId"], how="inner") - # Tag the "side" of the player for each row (that being "off" or "def") - merged_df['side'] = np.where(merged_df['club'] == merged_df['possessionTeam'], 'off', 'def') + # Tag the "side" of the player for each row (that being "off" or "def") + merged_df["side"] = np.where(merged_df["club"] == merged_df["possessionTeam"], "off", "def") - # Drop some columns we don't need anymore - merged_df = merged_df.drop(['possessionTeam', 'defensiveTeam', 'club', 'frameType'], axis=1) + # Drop some columns we don't need anymore + merged_df = merged_df.drop(["possessionTeam", "defensiveTeam", "club", "frameType"], axis=1) - # Sort for deterministic frame ordering - merged_df = merged_df.sort_values(['gameId','playId','frameId']) + # Sort for deterministic frame ordering + merged_df = merged_df.sort_values(["gameId", "playId", "frameId"]) - # Let's see what we have - logging.info(merged_df.head()) + # Let's see what we have + logging.info(merged_df.head()) + + return merged_df - return merged_df def _determine_sequence_length(merged_df: pd.DataFrame) -> int: - """Determine the maximum sequence length for the dataset based on frames per play.""" + """ + Computes a common sequence length using the lower decile of frame counts. + + Inputs: + - merged_df: Presnap tracking rows with frame identifiers. + + Outputs: + - min_frames: Sequence length threshold to keep plays. + """ - frame_counts = (merged_df - .groupby(['gameId','playId'])['frameId'] - .nunique()) - min_frames = int(np.percentile(frame_counts.values, 10)) - logging.info(f"Using plays that have above {min_frames} frames") + frame_counts = merged_df.groupby(["gameId", "playId"])["frameId"].nunique() + min_frames = int(np.percentile(frame_counts.values, 10)) + logging.info(f"Using plays that have above {min_frames} frames") + + return min_frames - return min_frames def _exactly_eleven_per_side(play_df: pd.DataFrame) -> bool: - return ( - play_df.loc[play_df.side == 'off', 'nflId'].nunique() == 11 and - play_df.loc[play_df.side == 'def', 'nflId'].nunique() == 11 - ) - -def _slot_order_by_left_to_right(play_df: pd.DataFrame, side: str) -> list: - side_df = play_df.loc[play_df["side"] == side] - stats = (side_df - .groupby('nflId', as_index=True)[['x','y']] - .median() - .rename(columns={'x':'x_med','y':'y_med'}) - .sort_values(['x_med','y_med'])) - return stats.index.tolist() # list of sorted NFL player ids for this play to determine median x --> y player locs - -def _build_side_feature_cube(play_df: pd.DataFrame, side: str, frames: np.ndarray, feature_cols: tuple) -> np.ndarray: - """ - For one side ('off' or 'def'), build a 3D tensor: - (T, 11, F) where F=len(feature_cols), with rows aligned to `frames` - and slots 0..10 as columns. Missing -> NaN. - """ - - # pivot to (frames x slots) for x and y, fill missing with NaN, then stack → (min_frames, 11, F) - side_df = play_df.loc[play_df["side"] == side] - - mats = [] - for col in feature_cols: - # Takes the long df and goes from frameId, slot, x, y as cols to: - # slot, 0, 1, 2 as cols ... with frameId 1, frameId 2... etc as the rows....shape is (min_frames, 11) - mat = side_df.pivot_table(index="frameId", columns="slot", values=col) - # It's possible certain players don't have exact tracking data throughout (ie one player has frame 10 and 12 but not frame 11), this will end up breaking our shape and cause issues downstream for model training - # So this forces the matrix to have for each frame - mat = mat.reindex(index=frames, columns=range(11), fill_value=np.nan) - mats.append(mat.to_numpy()) # shape: (T, 11) - - # stack features on the last axis to shape: (T, 11, F) - return np.stack(mats, axis=-1) + """ + Checks whether a play has exactly eleven unique offensive and defensive players. + + Inputs: + - play_df: Rows for a single play. + + Outputs: + - has_eleven: True when both sides have eleven participants. + """ + return play_df.loc[play_df.side == "off", "nflId"].nunique() == 11 and play_df.loc[play_df.side == "def", "nflId"].nunique() == 11 + + +def _slot_order_by_left_to_right(play_df: pd.DataFrame, side: str) -> list[int]: + """ + Orders players on one side by median field position to assign slots. + + Inputs: + - play_df: Rows for a single play. + - side: "off" or "def" to choose which group to order. + + Outputs: + - slot_order: List of player ids sorted left to right, then low to high y. + """ + side_df = play_df.loc[play_df["side"] == side] + stats = side_df.groupby("nflId", as_index=True)[["x", "y"]].median().rename(columns={"x": "x_med", "y": "y_med"}).sort_values(["x_med", "y_med"]) + return stats.index.tolist() # list of sorted NFL player ids for this play to determine median x --> y player locs + + +def _build_side_feature_cube(play_df: pd.DataFrame, side: str, frames: np.ndarray, feature_cols: tuple[str, ...]) -> np.ndarray: + """ + Builds a (frames x players x features) cube for one side of the ball. + + Inputs: + - play_df: Rows for a single play with slot assignments. + - side: "off" or "def" slice to process. + - frames: Ordered frame ids to include. + - feature_cols: Tuple of feature column names to stack. + + Outputs: + - side_cube: Numpy array shaped (T, 11, F) with NaNs for missing data. + """ + + # pivot to (frames x slots) for x and y, fill missing with NaN, then stack → (min_frames, 11, F) + side_df = play_df.loc[play_df["side"] == side] + + mats = [] + for col in feature_cols: + # Takes the long df and goes from frameId, slot, x, y as cols to: + # slot, 0, 1, 2 as cols ... with frameId 1, frameId 2... etc as the rows....shape is (min_frames, 11) + mat = side_df.pivot_table(index="frameId", columns="slot", values=col) + # It's possible certain players don't have exact tracking data throughout (ie one player has frame 10 and 12 but not frame 11), this will end up breaking our shape and cause issues downstream for model training + # So this forces the matrix to have for each frame + mat = mat.reindex(index=frames, columns=range(11), fill_value=np.nan) + mats.append(mat.to_numpy()) # shape: (T, 11) + + # stack features on the last axis to shape: (T, 11, F) + return np.stack(mats, axis=-1) + @time_fcn -def build_frame_data(merged_df: pd.DataFrame) -> tuple[dict, dict]: - """Build frame data cubes for offense and defense from the merged dataframe.""" +def build_frame_data(merged_df: pd.DataFrame) -> tuple[SeriesDict, SeriesDict]: + """ + Constructs offense and defense frame cubes for plays that meet criteria. + + Inputs: + - merged_df: Presnap tracking rows with side assignments. - min_frames = _determine_sequence_length(merged_df) + Outputs: + - off_series: Mapping of (gameId, playId) to offense cubes. + - def_series: Mapping of (gameId, playId) to defense cubes. + """ - # Init series maps - off_series = {} - def_series = {} + min_frames = _determine_sequence_length(merged_df) - # Lists to peek at later if we skip plays - skipped_wrong_player_count_list = [] # plays where offense or defense had >11 unique players - skipped_under_min_frames_list = [] # plays with fewer than min_frames + # Init series maps + off_series = {} + def_series = {} - # Iterate on each play - for (game_id, play_id), play in merged_df.groupby(['gameId','playId'], sort=False): - # Skip if not 11 players - if not _exactly_eleven_per_side(play): - skipped_wrong_player_count_list.append((game_id, play_id)) - continue + # Lists to peek at later if we skip plays + skipped_wrong_player_count_list = [] # plays where offense or defense had >11 unique players + skipped_under_min_frames_list = [] # plays with fewer than min_frames - # Define slot maps (left→right by median x, tie-break median y) - off_slots = _slot_order_by_left_to_right(play, 'off') - def_slots = _slot_order_by_left_to_right(play, 'def') + # Iterate on each play + for (game_id, play_id), play in merged_df.groupby(["gameId", "playId"], sort=False): + # Skip if not 11 players + if not _exactly_eleven_per_side(play): + skipped_wrong_player_count_list.append((game_id, play_id)) + continue - # Create a map that goes player id --> index so we can assign each player to an index as we go frame by frame - off_id2slot = {pid: i for i, pid in enumerate(off_slots)} - def_id2slot = {pid: i for i, pid in enumerate(def_slots)} + # Define slot maps (left→right by median x, tie-break median y) + off_slots = _slot_order_by_left_to_right(play, "off") + def_slots = _slot_order_by_left_to_right(play, "def") - # Assign slots (if offense use offensive map, if defense, use defensive map) - tmp = play.copy() - tmp['slot'] = np.where( - tmp['side'] == 'off', - tmp['nflId'].map(off_id2slot), - tmp['nflId'].map(def_id2slot) - ) + # Create a map that goes player id --> index so we can assign each player to an index as we go frame by frame + off_id2slot = {pid: i for i, pid in enumerate(off_slots)} + def_id2slot = {pid: i for i, pid in enumerate(def_slots)} - # Choose frame window (last min_frames frames) - frames_all = np.sort(tmp['frameId'].unique()) - if frames_all.size < min_frames: - skipped_under_min_frames_list.append((game_id, play_id)) - continue - frames = frames_all[-min_frames:] # Get the last min frames, so each play is consistent + # Assign slots (if offense use offensive map, if defense, use defensive map) + tmp = play.copy() + tmp["slot"] = np.where(tmp["side"] == "off", tmp["nflId"].map(off_id2slot), tmp["nflId"].map(def_id2slot)) - # Build offense/defense cubes: (min_frames, 11, 2) the 2 is x and y coords - feature_cols = ("x", "y", "s", "a") - off_arr = _build_side_feature_cube(tmp, "off", frames, feature_cols) - def_arr = _build_side_feature_cube(tmp, "def", frames, feature_cols) + # Choose frame window (last min_frames frames) + frames_all = np.sort(tmp["frameId"].unique()) + if frames_all.size < min_frames: + skipped_under_min_frames_list.append((game_id, play_id)) + continue + frames = frames_all[-min_frames:] # Get the last min frames, so each play is consistent - off_series[(game_id, play_id)] = off_arr - def_series[(game_id, play_id)] = def_arr + # Build offense/defense cubes: (min_frames, 11, 2) the 2 is x and y coords + feature_cols = ("x", "y", "s", "a") + off_arr = _build_side_feature_cube(tmp, "off", frames, feature_cols) + def_arr = _build_side_feature_cube(tmp, "def", frames, feature_cols) - logging.info(f"Kept plays: {len(off_series)}") - logging.info(f"Skipped (>11 players): {len(skipped_wrong_player_count_list)}") - logging.info(f"Skipped (<{min_frames} frames): {len(skipped_under_min_frames_list)}") + off_series[(game_id, play_id)] = off_arr + def_series[(game_id, play_id)] = def_arr + logging.info(f"Kept plays: {len(off_series)}") + logging.info(f"Skipped (>11 players): {len(skipped_wrong_player_count_list)}") + logging.info(f"Skipped (<{min_frames} frames): {len(skipped_under_min_frames_list)}") + + return off_series, def_series - return off_series, def_series def _impute_timewise(X_np: np.ndarray) -> np.ndarray: - """ - X_np: (T, F) with NaNs. - Impute per feature (column) along time: - 1) forward-fill if we miss a frame, assume the player stayed where he was last seen. - 2) back-fill - 3) fill remaining NaNs with column mean (0 if all NaN) - """ - df = pd.DataFrame(X_np) # (T, 11 players * 2 features = 44) - - # Copy the last known values fwd in time if there's missing NaNs - # Fill any leading NaNs that had no earlier data - # Example: [NaN, 3, 4, NaN, NaN, 7] ---> foward fill [NaN, 3, 4, 4, 4, 7] ---> backward fill [3, 3, 4, 4, 4, 7] - # If a whole column has NaNs we then fill it with 0s (only time this realistically kicks in) - df = df.ffill().bfill().fillna(0.0) - - return df.values.astype(np.float32) + """ + Imputes missing frame values by carrying forward/backward and filling zeros. -@time_fcn -def build_plays_data_numpy(off_series, def_series): + Inputs: + - X_np: Two-dimensional array of frame features with NaNs. + + Outputs: + - imputed: Array with timewise imputation applied. + """ + df = pd.DataFrame(X_np) # (T, 11 players * 2 features = 44) + + # Copy the last known values fwd in time if there's missing NaNs + # Fill any leading NaNs that had no earlier data + # Example: [NaN, 3, 4, NaN, NaN, 7] ---> foward fill [NaN, 3, 4, 4, 4, 7] ---> backward fill [3, 3, 4, 4, 4, 7] + # If a whole column has NaNs we then fill it with 0s (only time this realistically kicks in) + df = df.ffill().bfill().fillna(0.0) + + return df.values.astype(np.float32) - # Build labels dict mapping of (gameId, playId) --> 0/1 - label_map = {'Man': 1, 'Zone': 0} - labels_dict = {(r.gameId, r.playId): label_map[r.pff_manZone] for r in filtered_plays_df.itertuples()} - X_np, y_np = [], [] - for key, off_arr in off_series.items(): - def_arr = def_series[key] - X_play = np.concatenate([off_arr, def_arr], axis=1).reshape(off_arr.shape[0], -1) # (T, 22 * F) - X_play = _impute_timewise(X_play) - X_np.append(X_play.astype(np.float32)) - y_np.append(labels_dict[key]) - return X_np, np.array(y_np, dtype=int) +@time_fcn +def build_plays_data_numpy(off_series: SeriesDict, def_series: SeriesDict, filtered_plays_df: pd.DataFrame) -> tuple[list[np.ndarray], np.ndarray]: + """ + Stacks offense/defense cubes into play-level tensors and builds labels. + + Inputs: + - off_series: Mapping of offense cubes keyed by (gameId, playId). + - def_series: Mapping of defense cubes keyed by (gameId, playId). + + Outputs: + - X_np: List of per-play tensors shaped (frames, features). + - y_np: Array of play-level man/zone labels. + """ + # Build labels dict mapping of (gameId, playId) --> 0/1 + label_map = {"Man": 1, "Zone": 0} + labels_dict = {(r.gameId, r.playId): label_map[r.pff_manZone] for r in filtered_plays_df.itertuples()} + + X_np, y_np = [], [] + for key, off_arr in off_series.items(): + def_arr = def_series[key] + X_play = np.concatenate([off_arr, def_arr], axis=1).reshape(off_arr.shape[0], -1) # (T, 22 * F) + X_play = _impute_timewise(X_play) + X_np.append(X_play.astype(np.float32)) + y_np.append(labels_dict[key]) + return X_np, np.array(y_np, dtype=int) + @time_fcn -def create_dataloaders(X_np: np.ndarray, y_np: np.ndarray) -> tuple[DataLoader, DataLoader, np.ndarray]: - - # Splittys - idx_train, idx_val = train_test_split( - np.arange(len(X_np)), # Create an array from 0 to x number of plays - test_size=0.2, # Choosing standard 20% for test size - random_state=42, # Life universe and everything - stratify=y_np # Says split the data while keeping same ratio of 0s and 1s in both train and validation sets - ) - - # Combine all frames from training plays - train_stacked = np.vstack([X_np[i] for i in idx_train]) # shape: (total_train_frames, 22*F) - - # Scale data - scaler = StandardScaler() - scaler.fit(train_stacked) # computes mean_ and scale_ only on training data - joblib.dump(scaler, "plays_standard_scaler.pkl") - - def apply_scaler_to_list(X_list, idxs, scaler): - for i in idxs: - X_list[i] = scaler.transform(X_list[i]) - - apply_scaler_to_list(X_np, idx_train, scaler) - apply_scaler_to_list(X_np, idx_val, scaler) - - # Make tensor datasets - # NOTE: X will be of shape (play_count, min_frames, 44) - # NOTE: Y will be of shape (play_count, ) - train_ds = TensorDataset( - torch.stack([torch.from_numpy(X_np[i]).float() for i in idx_train]), # Each x is (min_frame, 22*F) - torch.from_numpy(y_np[idx_train]).long() - ) - val_ds = TensorDataset( - torch.stack([torch.from_numpy(X_np[i]).float() for i in idx_val]), - torch.from_numpy(y_np[idx_val]).long() - ) - - # Reproducibility seeds - SEED = 42 - np.random.seed(SEED) - torch.manual_seed(SEED) - torch.cuda.manual_seed_all(SEED) - - # For deterministic behavior (slower, optional) - torch.backends.cudnn.deterministic = True - torch.backends.cudnn.benchmark = False - - # Make dataloaders - train_loader = DataLoader(train_ds, batch_size=64, shuffle=True) - val_loader = DataLoader(val_ds, batch_size=64) - - return train_loader, val_loader, idx_train - -def collect_probs(model, loader: DataLoader, device) -> tuple[np.ndarray, np.ndarray]: - """ - Collect the raw probabilities so downstream we can try many thresholds without re-running the model. - """ - - model.eval() # inference mode, disables dropout and batch norm updates - y_true, y_prob = [], [] # lists to collect true labels and predicted probabilities - with torch.no_grad(): # no need to calcualte gradients for validation - for x, y in loader: - x = x.to(device) # move to GPU - logits = model(x) # forward pass to get raw class scores of shape [B, 2] - prob1 = torch.softmax(logits, dim=1)[:, 1] # turn logits into probabilties, get the man column - y_prob.append(prob1.cpu().numpy()) # move to CPU and convert to numpy - y_true.append(y.numpy()) # true labels on CPU as numpy - return np.concatenate(y_true), np.concatenate(y_prob) # flatten all batches into single arrays - -def report_at_threshold(y_true, y_prob, thr, beta=1.0): - # Turns probabilities into binary predictions at threshold - y_pred = (y_prob >= thr).astype(int) - - # Get structured metrics - (prec0, prec1), (rec0, rec1), (f10, f11), _ = precision_recall_fscore_support(y_true, y_pred, average=None, labels=[0,1], beta=beta, zero_division=0) - - # Return threshold, man precision, man recall, and man f1 score - return {"thr":thr, "man_prec":prec1, "man_rec":rec1, "man_f":f11} - -def tune_threshold_for_precision(y_true, y_prob, min_recall=None, beta=0.5): - - # Star off with unique probs and a coarse grid of thresholds - unique_probs = np.unique(np.clip(y_prob, 1e-6, 1-1e-6)) - grid = np.linspace(0.05, 0.95, 37) - - # Merge and de-duplicate - thresholds = np.unique(np.concatenate([unique_probs, grid])) - - # Find best threshold - best = {"thr":0.5, "man_prec":0.0, "man_rec":0.0, "man_f":0.0} - for t in thresholds: - y_pred = (y_prob >= t).astype(int) - (prec0, prec1), (rec0, rec1), (f10, f11), _ = precision_recall_fscore_support(y_true, y_pred, average=None, labels=[0,1], beta=beta, zero_division=0) - if min_recall is not None and rec1 < min_recall: - continue - if prec1 > best["man_prec"]: - best = {"thr": float(t), "man_prec": float(prec1), "man_rec": float(rec1), "man_f": float(f11)} - - # Return the best threshold (and associated metrics) - return best +def create_dataloaders(X_np: list[np.ndarray], y_np: np.ndarray) -> tuple[DataLoader, DataLoader, np.ndarray]: + """ + Splits play tensors into train/validation sets and builds loaders. + + Inputs: + - X_np: List of play tensors. + - y_np: Array of labels aligned with X_np. + + Outputs: + - train_loader: DataLoader for training plays. + - val_loader: DataLoader for validation plays. + - idx_train: Indices used for the training split. + """ + # Splittys + idx_train, idx_val = train_test_split( + np.arange(len(X_np)), # Create an array from 0 to x number of plays + test_size=0.2, # Choosing standard 20% for test size + random_state=42, # Life universe and everything + stratify=y_np, # Says split the data while keeping same ratio of 0s and 1s in both train and validation sets + ) + + # Combine all frames from training plays + train_stacked = np.vstack([X_np[i] for i in idx_train]) # shape: (total_train_frames, 22*F) + + # Scale data + scaler = StandardScaler() + scaler.fit(train_stacked) # computes mean_ and scale_ only on training data + joblib.dump(scaler, "plays_standard_scaler.pkl") + + def apply_scaler_to_list(X_list: list[np.ndarray], idxs: np.ndarray, scaler: StandardScaler) -> None: + for i in idxs: + X_list[i] = scaler.transform(X_list[i]) + + apply_scaler_to_list(X_np, idx_train, scaler) + apply_scaler_to_list(X_np, idx_val, scaler) + + # Make tensor datasets + # NOTE: X will be of shape (play_count, min_frames, 44) + # NOTE: Y will be of shape (play_count, ) + train_ds = TensorDataset( + torch.stack([torch.from_numpy(X_np[i]).float() for i in idx_train]), # Each x is (min_frame, 22*F) + torch.from_numpy(y_np[idx_train]).long(), + ) + val_ds = TensorDataset( + torch.stack([torch.from_numpy(X_np[i]).float() for i in idx_val]), + torch.from_numpy(y_np[idx_val]).long(), + ) + + # Reproducibility seeds + SEED = 42 + np.random.seed(SEED) + torch.manual_seed(SEED) + torch.cuda.manual_seed_all(SEED) + + # For deterministic behavior (slower, optional) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + # Make dataloaders + train_loader = DataLoader(train_ds, batch_size=64, shuffle=True) + val_loader = DataLoader(val_ds, batch_size=64) + + return train_loader, val_loader, idx_train + + +def collect_probs(model: nn.Module, loader: DataLoader, device: torch.device) -> tuple[np.ndarray, np.ndarray]: + """ + Gathers true labels and predicted probabilities for a dataset. + + Inputs: + - model: Trained classifier. + - loader: DataLoader providing batches. + - device: Target device for inference. + + Outputs: + - y_true: Concatenated ground-truth labels. + - y_prob: Concatenated probabilities for the positive class. + """ + + model.eval() # inference mode, disables dropout and batch norm updates + y_true, y_prob = [], [] # lists to collect true labels and predicted probabilities + with torch.no_grad(): # no need to calcualte gradients for validation + for x, y in loader: + x = x.to(device) # move to GPU + logits = model(x) # forward pass to get raw class scores of shape [B, 2] + prob1 = torch.softmax(logits, dim=1)[:, 1] # turn logits into probabilties, get the man column + y_prob.append(prob1.cpu().numpy()) # move to CPU and convert to numpy + y_true.append(y.numpy()) # true labels on CPU as numpy + return np.concatenate(y_true), np.concatenate(y_prob) # flatten all batches into single arrays + + +def report_at_threshold(y_true: np.ndarray, y_prob: np.ndarray, thr: float, beta: float = 1.0) -> dict[str, float]: + """ + Calculates precision/recall metrics at a given probability threshold. + + Inputs: + - y_true: Ground-truth labels. + - y_prob: Predicted probabilities. + - thr: Threshold for classifying positive. + - beta: Beta value for f-score if needed. + + Outputs: + - metrics: Dictionary of threshold and class 1 precision/recall/f-score. + """ + # Turns probabilities into binary predictions at threshold + y_pred = (y_prob >= thr).astype(int) + + # Get structured metrics + (prec0, prec1), (rec0, rec1), (f10, f11), _ = precision_recall_fscore_support(y_true, y_pred, average=None, labels=[0, 1], beta=beta, zero_division=0) + + # Return threshold, man precision, man recall, and man f1 score + return {"thr": thr, "man_prec": prec1, "man_rec": rec1, "man_f": f11} + + +def tune_threshold_for_precision(y_true: np.ndarray, y_prob: np.ndarray, min_recall: float | None = None, beta: float = 0.5) -> dict[str, float]: + """ + Searches thresholds to maximize precision on the positive class. + + Inputs: + - y_true: Ground-truth labels. + - y_prob: Predicted probabilities. + - min_recall: Optional recall floor. + - beta: Beta value for f-score filtering. + + Outputs: + - best: Dictionary containing best threshold and its metrics. + """ + # Star off with unique probs and a coarse grid of thresholds + unique_probs = np.unique(np.clip(y_prob, 1e-6, 1 - 1e-6)) + grid = np.linspace(0.05, 0.95, 37) + + # Merge and de-duplicate + thresholds = np.unique(np.concatenate([unique_probs, grid])) + + # Find best threshold + best = {"thr": 0.5, "man_prec": 0.0, "man_rec": 0.0, "man_f": 0.0} + for t in thresholds: + y_pred = (y_prob >= t).astype(int) + (prec0, prec1), (rec0, rec1), (f10, f11), _ = precision_recall_fscore_support(y_true, y_pred, average=None, labels=[0, 1], beta=beta, zero_division=0) + if min_recall is not None and rec1 < min_recall: + continue + if prec1 > best["man_prec"]: + best = { + "thr": float(t), + "man_prec": float(prec1), + "man_rec": float(rec1), + "man_f": float(f11), + } + + # Return the best threshold (and associated metrics) + return best + @time_fcn def train_model(train_loader: DataLoader, val_loader: DataLoader, y_np: np.ndarray, idx_train: np.ndarray) -> LSTMClassifier: + """ + Trains the LSTM classifier with early stopping based on precision. + + Inputs: + - train_loader: Batches for training. + - val_loader: Batches for validation. + - y_np: All labels to compute class weights. + - idx_train: Indices corresponding to the training split. + + Outputs: + - model: Trained LSTMClassifier with best threshold stored. + """ + # Set device to GPU + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Init LSTM model + model = LSTMClassifier(input_size=88, hidden_size=64, num_layers=2, dropout=0.4, bidir=False, num_classes=2).to(device) + + # Create criterion with CE losss weighted with class weights to account for higher proportion of man coverage + # Zone dominates class weighting, calc distribution then assign man a higher waiting on the CE loss + y_train = y_np[idx_train] # Slice to the training fold + classes = np.array([0, 1], dtype=int) # 0=Zone, 1=Man + w = compute_class_weight(class_weight="balanced", classes=classes, y=y_train) + logging.info("Class weights (Zone, Man): %s", w) + class_weights = torch.tensor(w, dtype=torch.float32, device=device) + criterion = nn.CrossEntropyLoss(weight=class_weights) + + # Using Adam + optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) + + # Init values for finding best theshold + set_min_recall = 0.25 # minimum recall floor when tuning thresholds + best_state = None + best_man_prec = -1.0 # init so first epoch can win + best_thr = 0.5 # default threshold + patience = 10 # how many epochs to wait until early stop + bad = 0 + + # Train + for epoch in range(50): + ##################################### + # Train + ##################################### + model.train() + for X, y in train_loader: + X, y = X.to(device), y.to(device) + logits = model(X) + loss = criterion(logits, y) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + ##################################### + # Validation + ##################################### + # Collect raw probabilities on validation set + y_true_val, y_prob_val = collect_probs(model, val_loader, device) + + # Tune threshold for max Man precision with a recall floor + tuned = tune_threshold_for_precision(y_true_val, y_prob_val, min_recall=set_min_recall, beta=0.5) + + # Report at tuned threshold + # stats_tuned = report_at_threshold(y_true_val, y_prob_val, thr=tuned["thr"], beta=0.5) #TODO: Not used + logging.info(f"[epoch {epoch + 1}] Tuned thr={tuned['thr']:.3f} | Man P={tuned['man_prec']:.2f} R={tuned['man_rec']:.2f}") + + # Early-stop on best Man precision-at-tuned-threshold + if tuned["man_prec"] > best_man_prec: + best_man_prec = tuned["man_prec"] + best_thr = tuned["thr"] + best_state = {k: v.cpu() for k, v in model.state_dict().items()} + bad = 0 + else: + bad += 1 + if bad >= patience: + logging.info(f"Early stopping at epoch {epoch + 1}") + break + + # Restore best weights and attach tuned threshold + if best_state is not None: + model.load_state_dict({k: v.to(device) for k, v in best_state.items()}) + logging.info(f"Final chosen Man threshold = {best_thr:.3f} (best Man precision={best_man_prec:.2f})") + model.best_man_threshold = best_thr # <-- sets the attribute used by viz + + return model - # Set device to GPU - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - - # Init LSTM model - model = LSTMClassifier(input_size=88, hidden_size=64, num_layers=2, dropout=0.4, bidir=False, num_classes=2).to(device) - - # Create criterion with CE losss weighted with class weights to account for higher proportion of man coverage - # Zone dominates class weighting, calc distribution then assign man a higher waiting on the CE loss - y_train = y_np[idx_train] # Slice to the training fold - classes = np.array([0, 1], dtype=int) # 0=Zone, 1=Man - w = compute_class_weight(class_weight='balanced', classes=classes, y=y_train) - logger.info("Class weights (Zone, Man): %s", w) - class_weights = torch.tensor(w, dtype=torch.float32, device=device) - criterion = nn.CrossEntropyLoss(weight=class_weights) - - # Using Adam - optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) - - # Init values for finding best theshold - set_min_recall = 0.25 # minimum recall floor when tuning thresholds - best_state = None - best_man_prec = -1.0 # init so first epoch can win - best_thr = 0.5 # default threshold - patience = 10 # how many epochs to wait until early stop - bad = 0 - - # Train - for epoch in range(50): - - ##################################### - # Train - ##################################### - model.train() - for X, y in train_loader: - X, y = X.to(device), y.to(device) - logits = model(X) - loss = criterion(logits, y) - optimizer.zero_grad() - loss.backward() - optimizer.step() - - ##################################### - # Validation - ##################################### - # Collect raw probabilities on validation set - y_true_val, y_prob_val = collect_probs(model, val_loader, device) - - # Tune threshold for max Man precision with a recall floor - tuned = tune_threshold_for_precision(y_true_val, y_prob_val, min_recall=set_min_recall, beta=0.5) - - # Report at tuned threshold - stats_tuned = report_at_threshold(y_true_val, y_prob_val, thr=tuned["thr"], beta=0.5) - logging.info(f"[epoch {epoch+1}] Tuned thr={tuned['thr']:.3f} | "f"Man P={tuned['man_prec']:.2f} R={tuned['man_rec']:.2f}") - - # Early-stop on best Man precision-at-tuned-threshold - if tuned["man_prec"] > best_man_prec: - best_man_prec = tuned["man_prec"] - best_thr = tuned["thr"] - best_state = {k: v.cpu() for k, v in model.state_dict().items()} - bad = 0 - else: - bad += 1 - if bad >= patience: - logging.info(f"Early stopping at epoch {epoch+1}") - break - - # Restore best weights and attach tuned threshold - if best_state is not None: - model.load_state_dict({k: v.to(device) for k, v in best_state.items()}) - logging.info(f"Final chosen Man threshold = {best_thr:.3f} (best Man precision={best_man_prec:.2f})") - model.best_man_threshold = best_thr # <-- sets the attribute used by viz - - return model @time_fcn def viz_results(val_loader: DataLoader, model: LSTMClassifier) -> None: + """ + Evaluates the trained model on validation data and plots diagnostics. + + Inputs: + - val_loader: Validation DataLoader. + - model: Trained LSTM model containing tuned threshold. + + Outputs: + - Logs metrics and renders confusion matrix and ROC plots. + """ + # Set device + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Use tuned threshold if available + thr = getattr(model, "best_man_threshold", 0.5) + + # Evaluate + model.eval() + all_true, all_prob = [], [] + with torch.no_grad(): + for X, y in val_loader: + X = X.to(device) + logits = model(X) # [B, 2] + prob_man = torch.softmax(logits, dim=1)[:, 1].cpu().numpy() + all_prob.extend(prob_man) + all_true.extend(y.numpy()) + all_true = np.array(all_true) + all_prob = np.array(all_prob) + all_pred = (all_prob >= thr).astype(int) + + # Classification report + logging.info(f"Using threshold = {thr:.3f}") + logging.info(classification_report(all_true, all_pred, target_names=["Zone", "Man"])) + + # Plot confusion matrix + cm = confusion_matrix(all_true, all_pred, labels=[0, 1]) + disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=["Zone", "Man"]) + disp.plot(cmap="Blues", values_format="d") + plt.title(f"Confusion Matrix (thr={thr:.3f})") + plt.show() + + # Plot ROC curve + fpr, tpr, thresholds = roc_curve(all_true, all_prob) # uses probabilities (not thresholded) + roc_auc = auc(fpr, tpr) + + plt.figure(figsize=(6, 6)) + plt.plot(fpr, tpr, color="darkorange", lw=2, label=f"ROC curve (AUC = {roc_auc:.3f})") + plt.plot([0, 1], [0, 1], color="navy", lw=1, linestyle="--", label="Chance line") + plt.xlabel("False Positive Rate (1 - Specificity)") + plt.ylabel("True Positive Rate (Recall)") + plt.title("Receiver Operating Characteristic (ROC)") + plt.legend(loc="lower right") + plt.grid(True, linestyle="--", alpha=0.6) + plt.show() + logging.info(f"ROC AUC = {roc_auc:.3f}") - # Set device - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - - # Use tuned threshold if available - thr = getattr(model, "best_man_threshold", 0.5) - - # Evaluate - model.eval() - all_true, all_prob = [], [] - with torch.no_grad(): - for X, y in val_loader: - X = X.to(device) - logits = model(X) # [B, 2] - prob_man = torch.softmax(logits, dim=1)[:, 1].cpu().numpy() - all_prob.extend(prob_man) - all_true.extend(y.numpy()) - all_true = np.array(all_true) - all_prob = np.array(all_prob) - all_pred = (all_prob >= thr).astype(int) - - # Classification report - logging.info(f"Using threshold = {thr:.3f}") - logging.info(classification_report(all_true, all_pred, target_names=["Zone", "Man"])) - - # Plot confusion matrix - cm = confusion_matrix(all_true, all_pred, labels=[0, 1]) - disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=["Zone", "Man"]) - disp.plot(cmap='Blues', values_format='d') - plt.title(f"Confusion Matrix (thr={thr:.3f})") - plt.show() - - # Plot ROC curve - fpr, tpr, thresholds = roc_curve(all_true, all_prob) # uses probabilities (not thresholded) - roc_auc = auc(fpr, tpr) - - plt.figure(figsize=(6, 6)) - plt.plot(fpr, tpr, color='darkorange', lw=2, label=f"ROC curve (AUC = {roc_auc:.3f})") - plt.plot([0, 1], [0, 1], color='navy', lw=1, linestyle='--', label='Chance line') - plt.xlabel("False Positive Rate (1 - Specificity)") - plt.ylabel("True Positive Rate (Recall)") - plt.title("Receiver Operating Characteristic (ROC)") - plt.legend(loc="lower right") - plt.grid(True, linestyle='--', alpha=0.6) - plt.show() - logging.info(f"ROC AUC = {roc_auc:.3f}") @time_fcn -def main(): - # Configure basic logging - logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') - logger = logging.getLogger(__name__) +def main() -> None: + """ + Orchestrates data prep, training, and evaluation for the LSTM workflow. + + Inputs: + - None (configuration is defined in code). + + Outputs: + - Trains the model and produces validation diagnostics. + """ + # Configure basic logging + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + + # Get raw data + from common.data_loader import RawDataLoader + + loader = RawDataLoader() + games_df, plays_df, players_df, location_data_df = loader.get_data(weeks=[week for week in range(1, 10)]) - # Get raw data - from common.data_loader import RawDataLoader - loader = RawDataLoader() - games_df, plays_df, players_df, location_data_df = loader.get_data(weeks=[week for week in range (1,10)]) + # Filter data + filtered_plays_df = filter_plays(plays_df, location_data_df) - # Filter data - filtered_plays_df = filter_plays(plays_df) + # Create merged df + merged_df = create_merged_df(location_data_df, filtered_plays_df) - # Create merged df - merged_df = create_merged_df(location_data_df, filtered_plays_df) + # Create cube data + off_series, def_series = build_frame_data(merged_df) - # Create cube data - off_series, def_series = build_frame_data(merged_df) + # Impute data and convert to numpy + X_np, y_np = build_plays_data_numpy(off_series, def_series, filtered_plays_df) - # Impute data and convert to numpy - X_np, y_np = build_plays_data_numpy(off_series, def_series) + # Create dataloaders + train_loader, val_loader, idx_train = create_dataloaders(X_np, y_np) - # Create dataloaders - train_loader, val_loader, idx_train = create_dataloaders(X_np, y_np) + # Train model + model = train_model(train_loader, val_loader, y_np, idx_train) - # Train model - model = train_model(train_loader, val_loader, y_np, idx_train) + # Create classification report and viz confusion matrix + viz_results(val_loader, model) - # Create classification report and viz confusion matrix - viz_results(val_loader, model) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/src/train_transformer.py b/src/train_transformer.py index 8cf7648..03ad55a 100644 --- a/src/train_transformer.py +++ b/src/train_transformer.py @@ -1,403 +1,430 @@ #!/usr/bin/env python - """ -Trains transformer model on location tracking data - -Requires that create_features.py has already been ran and produced: -- features_training.pt -- features_val.pt -- targets_training.pt -- targets_val.pt - -Will train 50 epochs (unless it early stops) and produce model.pth +Trains and tunes a transformer model for man/zone classification on tracking data. """ -import os +import argparse +import json import logging -import math -from pathlib import Path +import os import random -import json -import joblib +from pathlib import Path -# Util imports -import pandas as pd import numpy as np -import matplotlib.pyplot as plt - -# Torch imports +import ray import torch import torch.nn as nn -from torch.utils.data import TensorDataset, DataLoader -from torch.optim import AdamW -from torch.amp import autocast, GradScaler - -# ML imports -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler - -# Ray Tune -import ray -from ray import train, tune, air -from ray.air.integrations.mlflow import MLflowLoggerCallback, setup_mlflow -from ray.tune import Tuner, RunConfig, TuneConfig, FailureConfig, Checkpoint -from ray.tune import Tuner, RunConfig, TuneConfig, FailureConfig +from ray import tune +from ray.air.integrations.mlflow import MLflowLoggerCallback +from ray.tune import FailureConfig, RunConfig, TuneConfig, Tuner from ray.tune.schedulers import ASHAScheduler +from torch.amp import autocast +from torch.utils.data import DataLoader, TensorDataset -# Local imports -from models.transformer import ManZoneTransformer -from common.decorators import set_time_decorators_enabled, time_fcn -from common.paths import PROJECT_ROOT, SAVE_DIR from common.args import parse_args +from common.decorators import set_time_decorators_enabled, time_fcn +from common.paths import PROCESSED_DIR, PROJECT_ROOT +from models.transformer import create_transformer_model def set_seed(seed: int = 42) -> torch.Generator: - # Python & NumPy - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - g = torch.Generator() # Creates a generator that fixes the shuffle in torch Dataloader - g.manual_seed(seed) - - os.environ["PYTHONHASHSEED"] = str(seed) + """ + Sets Python, NumPy, and Torch seeds for reproducible runs. - # PyTorch CPU & CUDA - torch.manual_seed(seed) - if torch.cuda.is_available(): - torch.cuda.manual_seed(seed) - torch.cuda.manual_seed_all(seed) + Inputs: + - seed: Seed value to apply. - # Keep cudnn deterministic, but allow normal algorithms - # torch.backends.cudnn.benchmark = False - # torch.backends.cudnn.deterministic = True + Outputs: + - generator: Torch generator for deterministic DataLoader shuffling. + """ - # DO NOT enforce deterministic algorithms globally - # torch.use_deterministic_algorithms(False) + # Set seeds + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + g = torch.Generator() # Creates a generator that fixes the shuffle in torch Dataloader + g.manual_seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) - return g - - -@time_fcn -def train_epoch(train_loader: DataLoader, model, optimizer, loss_fn, device, amp_dtype) -> float: - # Training - model.train() - running_loss = 0.0 - for features, targets in train_loader: - - # Transfer from CPU to GPU, non_blocking for pinned memory - features = features.to(device, non_blocking=True) - targets = targets.to(device, non_blocking=True) + # PyTorch CPU & CUDA + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) - # Zero gradients - optimizer.zero_grad(set_to_none=True) + # Keep cudnn deterministic, but allow normal algorithms + # torch.backends.cudnn.benchmark = False + # torch.backends.cudnn.deterministic = True - # For forward pass, allow mixed precision for speed in some operations - # NOTE: PyTorch keeps some sensitive ops in FP32 for stability - with autocast(device_type="cuda", dtype=amp_dtype): - outputs = model(features) - loss = loss_fn(outputs, targets) + # DO NOT enforce deterministic algorithms globally + # torch.use_deterministic_algorithms(False) - # Backpropagate and optimize - # NOTE: We don't want the gradients in less than FP32, PyTorch would handle this for us but being explicit - loss.backward() - optimizer.step() - - # Record running loss - running_loss += loss.detach().item() * features.size(0) - - # Record running metrics - avg_train_loss = running_loss / len(train_loader.dataset) - - # Return losses - return avg_train_loss + return g @time_fcn -def validate_epoch(val_loader: DataLoader, model, loss_fn, device, amp_dtype) -> tuple[float, float]: - # Validation - model.eval() - val_running_loss = 0.0 - correct = 0 - with torch.no_grad(): - for val_features_batch, val_targets_batch in val_loader: +def train_epoch(train_loader: DataLoader, model: nn.Module, optimizer: torch.optim.Optimizer, loss_fn: nn.Module, device: torch.device, amp_dtype: torch.dtype) -> float: + """ + Runs one training epoch over the provided dataloader. + + Inputs: + - train_loader: Batches of training tensors. + - model: Transformer model to optimize. + - optimizer: Optimizer instance. + - loss_fn: Loss function. + - device: Target device for computation. + - amp_dtype: Mixed precision dtype for autocast. + + Outputs: + - avg_train_loss: Mean loss across the epoch. + """ + + # Training + model.train() + running_loss = 0.0 + for features, targets in train_loader: + # Transfer from CPU to GPU, non_blocking for pinned memory + features = features.to(device, non_blocking=True) + targets = targets.to(device, non_blocking=True) + + # Zero gradients + optimizer.zero_grad(set_to_none=True) + + # For forward pass, allow mixed precision for speed in some operations + # NOTE: PyTorch keeps some sensitive ops in FP32 for stability + with autocast(device_type="cuda", dtype=amp_dtype): + outputs = model(features) + loss = loss_fn(outputs, targets) + + # Backpropagate and optimize + # NOTE: We don't want the gradients in less than FP32, PyTorch would handle this for us but being explicit + loss.backward() + optimizer.step() + + # Record running loss + running_loss += loss.detach().item() * features.size(0) + + # Record running metrics + avg_train_loss = running_loss / len(train_loader.dataset) + + # Return losses + return avg_train_loss - # Transfer from CPU to GPU, non_blocking for pinned memory - val_features_batch = val_features_batch.to(device, non_blocking=True) - val_targets_batch = val_targets_batch.to(device, non_blocking=True) - # AMP - with autocast(device_type="cuda", dtype=amp_dtype): - val_outputs = model(val_features_batch) - val_loss = loss_fn(val_outputs, val_targets_batch) - _, predicted = torch.max(val_outputs, 1) - - # Grab metrics - val_running_loss += val_loss.item() * val_features_batch.size(0) - correct += (predicted == val_targets_batch).sum().item() +@time_fcn +def validate_epoch(val_loader: DataLoader, model: nn.Module, loss_fn: nn.Module, device: torch.device, amp_dtype: torch.dtype) -> tuple[float, float]: + """ + Validates the model on a held-out split. + + Inputs: + - val_loader: Batches of validation tensors. + - model: Transformer being evaluated. + - loss_fn: Loss function. + - device: Target device for computation. + - amp_dtype: Mixed precision dtype for autocast. + + Outputs: + - avg_val_loss: Mean validation loss. + - val_accuracy: Classification accuracy. + """ + + # Validation + model.eval() + val_running_loss = 0.0 + correct = 0 + with torch.no_grad(): + for val_features_batch, val_targets_batch in val_loader: + # Transfer from CPU to GPU, non_blocking for pinned memory + val_features_batch = val_features_batch.to(device, non_blocking=True) + val_targets_batch = val_targets_batch.to(device, non_blocking=True) + + # AMP + with autocast(device_type="cuda", dtype=amp_dtype): + val_outputs = model(val_features_batch) + val_loss = loss_fn(val_outputs, val_targets_batch) + _, predicted = torch.max(val_outputs, 1) + + # Grab metrics + val_running_loss += val_loss.item() * val_features_batch.size(0) + correct += (predicted == val_targets_batch).sum().item() + + # Record running metrics + avg_val_loss = val_running_loss / len(val_loader.dataset) + val_accuracy = correct / len(val_loader.dataset) + + # Return losses + return avg_val_loss, val_accuracy - # Record running metrics - avg_val_loss = val_running_loss / len(val_loader.dataset) - val_accuracy = correct / len(val_loader.dataset) - # Return losses - return avg_val_loss, val_accuracy +@time_fcn +def run_trial(config: dict[str, float | int], args: argparse.Namespace) -> None: + """ + Executes a single training run with the supplied hyperparameters. + + Inputs: + - config: Hyperparameters for the transformer and training loop. + - args: Command-line arguments controlling CI/tuning behavior. + + Outputs: + - Trains the model and optionally reports metrics to Ray Tune. + """ + + # Set seeds + g = set_seed(42) + + ###################################################################### + # Set things that allow for speed-up in training (valid on Ampere+ GPUs) + ###################################################################### + # NOTE: Importing torch here because Ray serializes the fcn, if we allow the global scope torch it fails in trying to serialize the global chain + import torch + + # Use mixed precision with FP16 for speed for forward pass (see train_epoch) + amp_dtype = torch.float16 + + # Set TensorFloat-32 (TF32) mode for matmul and cudnn (speeds up training on Ampere+ GPUs with minimal impact on accuracy) + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + + # Set device + device = torch.device("cuda") # this is the Ray-assigned GPU (Ray sets CUDA_VISIBLE_DEVICES) + + ###################################################################### + # Create model, loss, optimizer + ###################################################################### + # Create transformer with given config, move to GPU, and compile model for better inference (will save as compiled model) + model = create_transformer_model(config) + model = model.to(device) + model = torch.compile( + model, + mode="default", # default, reduces overhead, generally stable + fullgraph=True, # enable full graph fusion, helps reduce kernel launch overhead + ) + + # Set optimizer and loss fcn + optimizer = torch.optim.AdamW( + model.parameters(), + lr=float(config["lr"]), + weight_decay=float(config["weight_decay"]), + fused=True, # All operations on single CUDA kernel for speed + ) + loss_fn = nn.CrossEntropyLoss() + + ###################################################################### + # Create dataloaders + ###################################################################### + # Load in data and create tensor datasets + train_features = torch.load(PROCESSED_DIR / "features_training.pt") + train_targets = torch.load(PROCESSED_DIR / "targets_training.pt") + + val_features = torch.load(PROCESSED_DIR / "features_val.pt") + val_targets = torch.load(PROCESSED_DIR / "targets_val.pt") + + # Create data loaders for batching + train_loader = DataLoader( + TensorDataset(train_features, train_targets), + batch_size=int(config["batch_size"]), + shuffle=True, # We want random mini batches so GD doesn't overfit to specific ordering patterns, lets shuffle + generator=g, # Fixes the shuffle + num_workers=0, # Eliminate worker non-determinism + pin_memory=True, # Batches are allocated on page-locked ("pinned") memory on the host, allows GPU driver to perform faster async DMA + ) + + val_loader = DataLoader( + TensorDataset(val_features, val_targets), + batch_size=int(config["batch_size"]), + shuffle=False, # In eval we aren't updating the weights, so it doesn't really matter if we imply ordering or not + num_workers=0, # Eliminate worker non-determinism + pin_memory=True, # Batches are allocated on page-locked ("pinned") memory on the host, allows GPU driver to perform faster async DMA + ) + + ###################################################################### + # Train model (and evaluate) + ###################################################################### + # Init + train_losses = [] + val_losses = [] + val_accuracies = [] + early_stopping_patience = 20 + best_val_loss = float("inf") + epochs_no_improve = 0 + + # If running in CI mode, reduce epochs for speed, we just want to ensure it can actually train, not train a whole model in testing here (for pipelines later) + if args.ci: + config["epochs"] = 5 + + for epoch in range(int(config["epochs"])): + # Train + avg_train_loss = train_epoch(train_loader, model, optimizer, loss_fn, device, amp_dtype) + train_losses.append(avg_train_loss) + + # Validate + avg_val_loss, val_accuracy = validate_epoch(val_loader, model, loss_fn, device, amp_dtype) + + # Info + logging.info(f"Epoch [{epoch + 1}/{int(config['epochs'])}]") + logging.info(f"Train Loss: {avg_train_loss:.4f}, Val Loss: {avg_val_loss:.4f}, Val Accuracy: {val_accuracy:.4f}") + + if args.tune: + ###################################################################### + # Tune logging (with MLflow callback this is all mirrored there as well) + # NOTE: By calling tune.report here effectively once per epoch, that becomes our time scale! + ###################################################################### + # Record metrics + metrics = { + "val_accuracy": val_accuracy, + "val_loss": float(avg_val_loss), + "train_loss": float(avg_train_loss), + } + tune.report(metrics) + else: + # Record metrics + val_losses.append(avg_val_loss) + val_accuracies.append(val_accuracy) + + # Adding early stopping check (effort to prevent overfitting) + best_model_path = PROJECT_ROOT / "data" / "training" / "transformer.pt" + if avg_val_loss < best_val_loss: + best_val_loss = avg_val_loss + epochs_no_improve = 0 + torch.save(model.state_dict(), best_model_path) + else: + epochs_no_improve += 1 + if epochs_no_improve >= early_stopping_patience: + logging.info("Early stopping triggered") + break @time_fcn -def run_trial(config, args): - - # Set seeds - g = set_seed(42) - - ###################################################################### - # Set things that allow for speed-up in training (valid on Ampere+ GPUs) - ###################################################################### - # NOTE: Importing torch here because Ray serializes the fcn, if we allow the global scope torch it fails in trying to serialize the global chain - import torch - - # Use mixed precision with FP16 for speed for forward pass (see train_epoch) - amp_dtype = torch.float16 - - # Set TensorFloat-32 (TF32) mode for matmul and cudnn (speeds up training on Ampere+ GPUs with minimal impact on accuracy) - torch.backends.cuda.matmul.allow_tf32 = True - torch.backends.cudnn.allow_tf32 = True - - ###################################################################### - # Create model, loss, optimizer - ###################################################################### - device = torch.device("cuda") # this is the Ray-assigned GPU (Ray sets CUDA_VISIBLE_DEVICES) - - # Defining ManZoneTransformer params, initializing optimizer and loss_fn - model = ManZoneTransformer( - feature_len=5, # num of input features (x, y, v_x, v_y, defense) - model_dim=int(config["model_dim"]), # from ray tune or loaded - num_heads=int(config["num_heads"]), # from ray tune or loaded - num_layers=int(config["num_layers"]), # from ray tune or loaded - dim_feedforward=int(config["model_dim"]) * int(config["multiplier"]), # from ray tune or loaded - dropout=float(config["dropout"]), # from ray tune or loaded - output_dim=2 # man or zone classification - ) - # Move model to device (GPU) - model = model.to(device) - - # Compile with fullgraph - model = torch.compile( - model, - mode="default", # default, reduces overhead, generally stable - fullgraph=True # enable full graph fusion, helps reduce kernel launch overhead - ) - - # Set optimizer and loss fcn - optimizer = torch.optim.AdamW( - model.parameters(), - lr=float(config["lr"]), - weight_decay=float(config["weight_decay"]), - fused=True, # All operations on single CUDA kernel for speed - ) - loss_fn = nn.CrossEntropyLoss() - - ###################################################################### - # Create dataloaders - ###################################################################### - # Load in data and create tensor datasets - train_features = torch.load(SAVE_DIR / f"features_training.pt") - train_targets = torch.load(SAVE_DIR / f"targets_training.pt") - - val_features = torch.load(SAVE_DIR / f"features_val.pt") - val_targets = torch.load(SAVE_DIR / f"targets_val.pt") - - # Create data loaders for batching - train_loader = DataLoader( - TensorDataset(train_features, train_targets), - batch_size=int(config["batch_size"]), - shuffle=True, # We want random mini batches so GD doesn't overfit to specific ordering patterns, lets shuffle - generator=g, # Fixes the shuffle - num_workers=0, # Eliminate worker non-determinism - pin_memory=True, # Batches are allocated on page-locked ("pinned") memory on the host, allows GPU driver to perform faster async DMA - ) - - val_loader = DataLoader( - TensorDataset(val_features, val_targets), - batch_size=int(config["batch_size"]), - shuffle=False, # In eval we aren't updating the weights, so it doesn't really matter if we imply ordering or not - num_workers=0, # Eliminate worker non-determinism - pin_memory=True, # Batches are allocated on page-locked ("pinned") memory on the host, allows GPU driver to perform faster async DMA - ) - - ###################################################################### - # Train model (and evaluate) - ###################################################################### - # Init - train_losses = [] - val_losses = [] - val_accuracies = [] - early_stopping_patience = 20 - best_val_loss = float('inf') - epochs_no_improve = 0 - - # If running in CI mode, reduce epochs for speed, we just want to ensure it can actually train, not train a whole model in testing here (for pipelines later) - if args.ci: - config["epochs"] = 5 - - for epoch in range(int(config["epochs"])): - - # Train - avg_train_loss = train_epoch(train_loader, model, optimizer, loss_fn, device, amp_dtype) - train_losses.append(avg_train_loss) - - # Validate - avg_val_loss, val_accuracy = validate_epoch(val_loader, model, loss_fn, device, amp_dtype) - - # Info - logging.info(f"Epoch [{epoch + 1}/{int(config['epochs'])}]") - logging.info(f"Train Loss: {avg_train_loss:.4f}, Val Loss: {avg_val_loss:.4f}, Val Accuracy: {val_accuracy:.4f}") - - if args.tune: - ###################################################################### - # Tune logging (with MLflow callback this is all mirrored there as well) - # NOTE: By calling tune.report here effectively once per epoch, that becomes our time scale! - ###################################################################### - # Record metrics - metrics = { - "val_accuracy": val_accuracy, - "val_loss": float(avg_val_loss), - "train_loss": float(avg_train_loss), - } - tune.report(metrics) - else: - # Record metrics - val_losses.append(avg_val_loss) - val_accuracies.append(val_accuracy) - - # Adding early stopping check (effort to prevent overfitting) - best_model_path = PROJECT_ROOT / "data" / "training" / "best_model.pth" - if avg_val_loss < best_val_loss: - best_val_loss = avg_val_loss - epochs_no_improve = 0 - torch.save(model.state_dict(), best_model_path) - else: - epochs_no_improve += 1 - if epochs_no_improve >= early_stopping_patience: - logging.info(f"Early stopping triggered") - break - - -@time_fcn -def run_hpo(args) -> None: - - ###################################################################### - # Start parent HPO, MLflow session - ###################################################################### - mlflow_tracking_uri = "sqlite:///mlflow.db" - experiment = "transformer" - - ###################################################################### - # Define search space and scheduler - ###################################################################### - transformer_params = { - # Varying model shape - "model_dim": tune.choice([32, 64, 96, 128]), - "num_heads": tune.choice([2, 4, 8]), - "num_layers": tune.choice([2, 3, 4, 6]), - "dropout": tune.choice([0.0, 0.1, 0.2, 0.3]), - "multiplier": tune.choice([2, 3, 4]), - - # Training - "lr": tune.loguniform(1e-5, 5e-3), - "weight_decay": tune.loguniform(1e-6, 1e-2), - "batch_size": 19200, # Configured for L4 GPU utilization - - # Epochs - "epochs": 100, - } - params=transformer_params - - # Async Successive Halfing Scheduler (ASHA) - # Instead of running all trials for all epochs, it allocates more resources to promising ones and kills of bad ones early - scheduler = ASHAScheduler( - max_t=params["epochs"], # Max amount of "things" on our whatever our scale is (since we call tune.report once per epoch this max epochs per trial) - grace_period=20, # Allow for 20 epochs before pruning (so each trial gets at least this many epochs) - reduction_factor=2, # ASHA keeps about 50% of the top trials each time it prunes - ) - - ###################################################################### - # Build tuner; pass MLflow context and PARENT RUN ID to workers via env vars - ###################################################################### - trainable = tune.with_parameters(run_trial, args=args) # Allows each training run to have any specific params - tuner = Tuner( - tune.with_resources(trainable, resources={"cpu": 16, "gpu": 1}), # Gives 16 CPU and one GPU per trial - param_space=params, - tune_config=TuneConfig( - metric="val_accuracy", - mode="max", - scheduler=scheduler, - num_samples=1, # total trials, set to 1 for CI, modify here for HPO runs - ), - run_config=RunConfig( - name="transformer_hpo", - storage_path=os.path.abspath("./ray_results"), - failure_config=FailureConfig(fail_fast=True), - callbacks=[ - MLflowLoggerCallback( - tracking_uri=mlflow_tracking_uri, - experiment_name=experiment, - save_artifact=True, - ) - ], - ), - ) - - ###################################################################### - # Execute HPO - ###################################################################### - results = tuner.fit() - best = results.get_best_result(metric="val_accuracy", mode="max") - print("Best config:", best.config) - - # Save file to best config - output_file = PROJECT_ROOT / "data" / "training" / "model_params.json" - with open(output_file, 'w') as json_file: - json.dump(best.config, json_file, indent=4) # indent for pretty-printing +def run_hpo(args: argparse.Namespace) -> None: + """ + Launches Ray Tune to search over transformer hyperparameters. + + Inputs: + - args: Command-line options controlling CI/tuning behavior. + + Outputs: + - Writes best config to disk and logs metrics to MLflow. + """ + + ###################################################################### + # Start parent HPO, MLflow session + ###################################################################### + mlflow_tracking_uri = "sqlite:///mlflow.db" + experiment = "transformer" + + ###################################################################### + # Define search space and scheduler + ###################################################################### + transformer_params = { + # Varying model shape + "model_dim": tune.choice([32, 64, 96, 128]), + "num_heads": tune.choice([2, 4, 8]), + "num_layers": tune.choice([2, 3, 4, 6]), + "dropout": tune.choice([0.0, 0.1, 0.2, 0.3]), + "multiplier": tune.choice([2, 3, 4]), + # Training + "lr": tune.loguniform(1e-5, 5e-3), + "weight_decay": tune.loguniform(1e-6, 1e-2), + "batch_size": 19200, # Configured for L4 GPU utilization + # Epochs + "epochs": 100, + } + params = transformer_params + + # Async Successive Halfing Scheduler (ASHA) + # Instead of running all trials for all epochs, it allocates more resources to promising ones and kills of bad ones early + scheduler = ASHAScheduler( + max_t=params["epochs"], # Max amount of "things" on our whatever our scale is (since we call tune.report once per epoch this max epochs per trial) + grace_period=20, # Allow for 20 epochs before pruning (so each trial gets at least this many epochs) + reduction_factor=2, # ASHA keeps about 50% of the top trials each time it prunes + ) + + ###################################################################### + # Build tuner; pass MLflow context and PARENT RUN ID to workers via env vars + ###################################################################### + trainable = tune.with_parameters(run_trial, args=args) # Allows each training run to have any specific params + tuner = Tuner( + tune.with_resources(trainable, resources={"cpu": 16, "gpu": 1}), # Gives 16 CPU and one GPU per trial + param_space=params, + tune_config=TuneConfig( + metric="val_accuracy", + mode="max", + scheduler=scheduler, + num_samples=1, # total trials, set to 1 for CI, modify here for HPO runs + ), + run_config=RunConfig( + name="transformer_hpo", + storage_path=os.path.abspath("./ray_results"), + failure_config=FailureConfig(fail_fast=True), + callbacks=[ + MLflowLoggerCallback( + tracking_uri=mlflow_tracking_uri, + experiment_name=experiment, + save_artifact=True, + ) + ], + ), + ) + + ###################################################################### + # Execute HPO + ###################################################################### + results = tuner.fit() + best = results.get_best_result(metric="val_accuracy", mode="max") + print("Best config:", best.config) + + # Save file to best config + output_file = PROJECT_ROOT / "data" / "training" / "model_params.json" + with open(output_file, "w") as json_file: + json.dump(best.config, json_file, indent=4) # indent for pretty-printing @time_fcn def main() -> None: - """Main function to run HPO or single trial.""" - - # Set logging - logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') - - # Get input args - args = parse_args() - - # Enable/disable timing decorators - if args.profile: - set_time_decorators_enabled(True) - logging.info("Timing decorators enabled") - else: - set_time_decorators_enabled(False) - logging.info("Timing decorators disabled") - - # We are doing HPO with Ray - if args.tune: - # Set the runtime env to only ship src/, the whole repo that has a bunch of data - script_dir = Path(__file__).resolve().parent # Directory containing train_transformer.py (i.e., src/) - ray.init( - # Set the system config for the metrics exporter to pick a free port - _metrics_export_port=5001, - # Set the runtime to only bundle the src dir - runtime_env={ - "working_dir": str(script_dir), - }, - ) - run_hpo(args) - - # We are doing a single run with fixed model parameters located at nfl/data/training/model_params.json - else: - with open(PROJECT_ROOT / "data" / "training" / "model_params.json", 'r') as file: - model_params_map = json.load(file) - run_trial(model_params_map, args) + """ + Entry point to run a single trial or hyperparameter search. + + Inputs: + - CLI flags for tuning and profiling. + + Outputs: + - Trains models and persists artifacts to disk. + """ + + # Set logging + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + + # Get input args + args = parse_args() + + # Enable/disable timing decorators + if args.profile: + set_time_decorators_enabled(True) + logging.info("Timing decorators enabled") + else: + set_time_decorators_enabled(False) + logging.info("Timing decorators disabled") + + # We are doing HPO with Ray + if args.tune: + # Set the runtime env to only ship src/, the whole repo that has a bunch of data + script_dir = Path(__file__).resolve().parent # Directory containing train_transformer.py (i.e., src/) + ray.init( + # Set the system config for the metrics exporter to pick a free port + _metrics_export_port=5001, + # Set the runtime to only bundle the src dir + runtime_env={ + "working_dir": str(script_dir), + }, + ) + run_hpo(args) + + # We are doing a single run with fixed model parameters located at nfl/data/training/model_params.json + else: + with open(PROJECT_ROOT / "data" / "training" / "model_params.json") as file: + model_params_map = json.load(file) + run_trial(model_params_map, args) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/src/viz/animate_play.py b/src/viz/animate_play.py index 3b42096..2dd3f1c 100644 --- a/src/viz/animate_play.py +++ b/src/viz/animate_play.py @@ -1,15 +1,13 @@ #!/usr/bin/env python - """ -Animates play based on location tracking data to visualize pre snap - through end of play +Animates a single play using tracking data to visualize player movement. """ -import os - import matplotlib.pyplot as plt -from matplotlib.animation import FuncAnimation import numpy as np import pandas as pd +from matplotlib.animation import FuncAnimation +from matplotlib.artist import Artist """ # TODO @@ -23,103 +21,134 @@ - Make a driver so I can start loading all these plays (later) """ -def animate_play(game_info_df: pd.DataFrame, play_data_single_play_df: pd.DataFrame, players_df: pd.DataFrame, location_data_single_play_df: pd.DataFrame): - - # Unpack contents - game_id = game_info_df['gameId'] - week = game_info_df['week'] - home_team = game_info_df['homeTeamAbbr'] - away_team = game_info_df['visitorTeamAbbr'] - home_score = game_info_df['homeFinalScore'] - away_score = game_info_df['visitorFinalScore'] - play_id = play_data_single_play_df['playId'].values[0] - quarter = play_data_single_play_df['quarter'].values[0] - down = play_data_single_play_df['down'].values[0] - yards_to_go = play_data_single_play_df['yardsToGo'].values[0] - play_description = play_data_single_play_df['playDescription'].values[0] - - # Grab contents of the play - frame_ids = sorted(location_data_single_play_df['frameId'].unique()) - - # Find the frame where the ball is snapped - snap_row = location_data_single_play_df[location_data_single_play_df['event'] == 'ball_snap'] - if not snap_row.empty: - snap_frame_id = snap_row['frameId'].min() - else: - if len(frame_ids) == 0: - raise ValueError(f"No frames found for game {game_id}, play {play_id}.") - snap_frame_id = frame_ids[0]pd.DataFrame - print(f"No 'ball_snap' event found for game {game_id}, play {play_id}. Using first frame ({snap_frame_id}) as snap.") - - # Setup plot - fig, ax = plt.subplots(figsize=(15, 6)) - ax.set_xlim(0, 120) - ax.set_ylim(0, 53.3) - ax.set_facecolor('green') - ax.set_aspect('equal') - - # Draw football field - for x in range(10, 111, 10): - ax.axvline(x, color='white', linewidth=1) - ax.axhline(0, color='white') - ax.axhline(53.3, color='white') - - player_dots = [] - arrows = [] - labels = [] - - # Placeholder for relative time display - time_text = ax.text(60, 51, '', fontsize=12, ha='center', color='white', bbox=dict(facecolor='black', alpha=0.5)) - - def init(): - return player_dots + arrows + labels + [time_text] - - def update(frame_id): - for artist in player_dots + arrows + labels: - artist.remove() - player_dots.clear() - arrows.clear() - labels.clear() - - frame_df = location_data_single_play_df[location_data_single_play_df['frameId'] == frame_id] - - for _, row in frame_df.iterrows(): - x, y = row['x'], row['y'] - dir_angle = row['dir'] if not np.isnan(row['dir']) else 0 - dx = np.cos(np.radians(dir_angle)) - dy = np.sin(np.radians(dir_angle)) - - if pd.isna(row['nflId']): - dot, = ax.plot(x, y, 'o', color='brown', markersize=8) - player_dots.append(dot) - else: - arrow = ax.arrow(x, y, dx, dy, head_width=1, color='blue', length_includes_head=True) - label = ax.text(x + 0.5, y + 0.5, row['jerseyNumber'], fontsize=7, color='white') - dot, = ax.plot(x, y, 'o', color='blue', markersize=6) - arrows.append(arrow) - labels.append(label) - player_dots.append(dot) - - # Show relative time from snap - frame_offset = frame_id - snap_frame_id - time_sec = frame_offset * 0.1 # 0.1 seconds per frame - time_text.set_text(f"Time: {time_sec:+.1f} s (relative to snap)") - - # Down specification - if down == 1: - down_text = "1st" - elif down == 2: - down_text = "2nd" - elif down == 3: - down_text = "3rd" - else: - down_text = "4th" - - ax.set_title(f'Week {week}: {away_team} ({away_score}) vs {home_team} ({home_score}) \n Q{quarter} {down_text} and {yards_to_go} \n {play_description}',) - - return player_dots + arrows + labels + [time_text] - - ani = FuncAnimation(fig, update, frames=frame_ids, init_func=init, - blit=False, interval=100) - - plt.show() \ No newline at end of file + +def animate_play( + game_info_df: pd.DataFrame, + play_data_single_play_df: pd.DataFrame, + players_df: pd.DataFrame, + location_data_single_play_df: pd.DataFrame, +) -> None: + """ + Renders an animation of player movement for a single play. + + Inputs: + - game_info_df: Metadata for the current game. + - play_data_single_play_df: Play-level details (down, distance, etc.). + - players_df: Player metadata table. + - location_data_single_play_df: Tracking rows for the play. + + Outputs: + - Displays an animated matplotlib figure of the play. + """ + + # Unpack contents + game_id = game_info_df["gameId"] + week = game_info_df["week"] + home_team = game_info_df["homeTeamAbbr"] + away_team = game_info_df["visitorTeamAbbr"] + home_score = game_info_df["homeFinalScore"] + away_score = game_info_df["visitorFinalScore"] + play_id = play_data_single_play_df["playId"].values[0] + quarter = play_data_single_play_df["quarter"].values[0] + down = play_data_single_play_df["down"].values[0] + yards_to_go = play_data_single_play_df["yardsToGo"].values[0] + play_description = play_data_single_play_df["playDescription"].values[0] + + # Grab contents of the play + frame_ids = sorted(location_data_single_play_df["frameId"].unique()) + + # Find the frame where the ball is snapped + snap_row = location_data_single_play_df[location_data_single_play_df["event"] == "ball_snap"] + if not snap_row.empty: + snap_frame_id = snap_row["frameId"].min() + else: + if len(frame_ids) == 0: + raise ValueError(f"No frames found for game {game_id}, play {play_id}.") + snap_frame_id = frame_ids[0] + print(f"No 'ball_snap' event found for game {game_id}, play {play_id}. Using first frame ({snap_frame_id}) as snap.") + + # Setup plot + fig, ax = plt.subplots(figsize=(15, 6)) + ax.set_xlim(0, 120) + ax.set_ylim(0, 53.3) + ax.set_facecolor("green") + ax.set_aspect("equal") + + # Draw football field + for x in range(10, 111, 10): + ax.axvline(x, color="white", linewidth=1) + ax.axhline(0, color="white") + ax.axhline(53.3, color="white") + + player_dots = [] + arrows = [] + labels = [] + + # Placeholder for relative time display + time_text = ax.text(60, 51, "", fontsize=12, ha="center", color="white", bbox=dict(facecolor="black", alpha=0.5)) + + def init() -> list[Artist]: + """Initializes artists for FuncAnimation.""" + + return player_dots + arrows + labels + [time_text] + + def update(frame_id: int) -> list[Artist]: + """ + Updates plot artists for a specific frame in the animation. + + Inputs: + - frame_id: Frame identifier to render. + + Outputs: + - artists: List of matplotlib artists for blitting. + """ + + for artist in player_dots + arrows + labels: + artist.remove() + player_dots.clear() + arrows.clear() + labels.clear() + + frame_df = location_data_single_play_df[location_data_single_play_df["frameId"] == frame_id] + + for _, row in frame_df.iterrows(): + x, y = row["x"], row["y"] + dir_angle = row["dir"] if not np.isnan(row["dir"]) else 0 + dx = np.cos(np.radians(dir_angle)) + dy = np.sin(np.radians(dir_angle)) + + if pd.isna(row["nflId"]): + (dot,) = ax.plot(x, y, "o", color="brown", markersize=8) + player_dots.append(dot) + else: + arrow = ax.arrow(x, y, dx, dy, head_width=1, color="blue", length_includes_head=True) + label = ax.text(x + 0.5, y + 0.5, row["jerseyNumber"], fontsize=7, color="white") + (dot,) = ax.plot(x, y, "o", color="blue", markersize=6) + arrows.append(arrow) + labels.append(label) + player_dots.append(dot) + + # Show relative time from snap + frame_offset = frame_id - snap_frame_id + time_sec = frame_offset * 0.1 # 0.1 seconds per frame + time_text.set_text(f"Time: {time_sec:+.1f} s (relative to snap)") + + # Down specification + if down == 1: + down_text = "1st" + elif down == 2: + down_text = "2nd" + elif down == 3: + down_text = "3rd" + else: + down_text = "4th" + + ax.set_title( + f"Week {week}: {away_team} ({away_score}) vs {home_team} ({home_score}) \n Q{quarter} {down_text} and {yards_to_go} \n {play_description}", + ) + + return player_dots + arrows + labels + [time_text] + + FuncAnimation(fig, update, frames=frame_ids, init_func=init, blit=False, interval=100) + + plt.show() diff --git a/src/viz/draw_play_driver.py b/src/viz/draw_play_driver.py index c75b8ab..e2ca5fc 100644 --- a/src/viz/draw_play_driver.py +++ b/src/viz/draw_play_driver.py @@ -4,45 +4,41 @@ Driver code to animate play for all given plays """ -import os import random -from data_processing.process import load_data from data_processing.draw_play import animate_play +from data_processing.process import load_data from common.paths import PROJECT_ROOT - if __name__ == "__main__": - - # Set random seed for reproducibility - random.seed(42) - - # Load data - games_df = load_data(PROJECT_ROOT / 'data' / 'raw' / 'games.csv') - plays_df = load_data(PROJECT_ROOT / 'data' / 'raw' / 'plays.csv') - players_df = load_data(PROJECT_ROOT / 'data' / 'raw' / 'players.csv') - week1_df = load_data(PROJECT_ROOT / 'data' / 'raw' / 'tracking_week_1.csv') - - # Draw play frame - for i in range(0,len(games_df)): - # Get the game df and id - game_info_df = games_df.iloc[i] - game_id = game_info_df['gameId'] - - # Filter the plays to only the current game - single_game_plays_df = plays_df[plays_df['gameId'] == game_id] - - # Loop across the plays in the game - for play_id in single_game_plays_df['playId'].unique(): - - # Filter the tracking data to only the current play - location_data_single_play_df = week1_df[(week1_df['gameId'] == game_id) & (week1_df['playId'] == play_id)] - - # Filter all the play data to the current play - play_data_single_play_df = single_game_plays_df[single_game_plays_df['playId'] == play_id] - - if not location_data_single_play_df.empty: - animate_play(game_info_df, play_data_single_play_df, players_df, location_data_single_play_df) - else: - print(f"No tracking data found for game {game_id}, play {play_id}.") \ No newline at end of file + # Set random seed for reproducibility + random.seed(42) + + # Load data + games_df = load_data(PROJECT_ROOT / "data" / "raw" / "games.csv") + plays_df = load_data(PROJECT_ROOT / "data" / "raw" / "plays.csv") + players_df = load_data(PROJECT_ROOT / "data" / "raw" / "players.csv") + week1_df = load_data(PROJECT_ROOT / "data" / "raw" / "tracking_week_1.csv") + + # Draw play frame + for i in range(0, len(games_df)): + # Get the game df and id + game_info_df = games_df.iloc[i] + game_id = game_info_df["gameId"] + + # Filter the plays to only the current game + single_game_plays_df = plays_df[plays_df["gameId"] == game_id] + + # Loop across the plays in the game + for play_id in single_game_plays_df["playId"].unique(): + # Filter the tracking data to only the current play + location_data_single_play_df = week1_df[(week1_df["gameId"] == game_id) & (week1_df["playId"] == play_id)] + + # Filter all the play data to the current play + play_data_single_play_df = single_game_plays_df[single_game_plays_df["playId"] == play_id] + + if not location_data_single_play_df.empty: + animate_play(game_info_df, play_data_single_play_df, players_df, location_data_single_play_df) + else: + print(f"No tracking data found for game {game_id}, play {play_id}.") diff --git a/uv.lock b/uv.lock index ec4e8d5..1cb3636 100644 --- a/uv.lock +++ b/uv.lock @@ -1,4 +1,5 @@ version = 1 +revision = 2 requires-python = ">=3.12" [[package]] @@ -10,27 +11,27 @@ dependencies = [ { name = "sqlalchemy" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/02/a6/74c8cadc2882977d80ad756a13857857dbcf9bd405bc80b662eb10651282/alembic-1.17.2.tar.gz", hash = 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