Spaces:
Runtime error
Runtime error
File size: 11,785 Bytes
1ac8cff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 | """
Graph Neural Network (GNN) for Football Prediction
Uses team relationships and match context as a graph structure.
Based on the blueprint for advanced deep learning models.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, List, Tuple, Optional
import numpy as np
import logging
logger = logging.getLogger(__name__)
# Check for torch_geometric
try:
from torch_geometric.nn import GCNConv, GATConv, SAGEConv
from torch_geometric.data import Data, Batch
HAS_TORCH_GEOMETRIC = True
except ImportError:
HAS_TORCH_GEOMETRIC = False
logger.warning("torch_geometric not installed. GNN features limited.")
class TeamEmbedding(nn.Module):
"""Learnable team embeddings."""
def __init__(self, num_teams: int, embedding_dim: int = 64):
super().__init__()
self.embedding = nn.Embedding(num_teams, embedding_dim)
def forward(self, team_ids: torch.Tensor) -> torch.Tensor:
return self.embedding(team_ids)
class MatchGraphEncoder(nn.Module):
"""
Encode match context using graph neural networks.
Nodes: Teams
Edges: Recent matches between teams
Node features: Team statistics
Edge features: Match statistics
"""
def __init__(
self,
node_features: int = 64,
hidden_dim: int = 128,
output_dim: int = 64,
num_layers: int = 3,
dropout: float = 0.2
):
super().__init__()
if not HAS_TORCH_GEOMETRIC:
# Fallback to simple MLP
self.use_gnn = False
self.fallback = nn.Sequential(
nn.Linear(node_features * 2, hidden_dim),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, output_dim)
)
return
self.use_gnn = True
# Graph convolution layers
self.convs = nn.ModuleList()
self.bns = nn.ModuleList()
# First layer
self.convs.append(GATConv(node_features, hidden_dim, heads=4, concat=False))
self.bns.append(nn.BatchNorm1d(hidden_dim))
# Hidden layers
for _ in range(num_layers - 2):
self.convs.append(GATConv(hidden_dim, hidden_dim, heads=4, concat=False))
self.bns.append(nn.BatchNorm1d(hidden_dim))
# Output layer
self.convs.append(GATConv(hidden_dim, output_dim, heads=1, concat=False))
self.dropout = nn.Dropout(dropout)
def forward(
self,
x: torch.Tensor,
edge_index: torch.Tensor = None,
batch: torch.Tensor = None
) -> torch.Tensor:
if not self.use_gnn or edge_index is None:
# Fallback
return self.fallback(x) if hasattr(self, 'fallback') else x
for i, conv in enumerate(self.convs[:-1]):
x = conv(x, edge_index)
x = self.bns[i](x)
x = F.elu(x)
x = self.dropout(x)
x = self.convs[-1](x, edge_index)
return x
class GraphFootballPredictor(nn.Module):
"""
Complete GNN-based football prediction model.
Architecture:
1. Team embeddings
2. Graph encoder for league context
3. Match predictor head
"""
def __init__(
self,
num_teams: int = 1000,
team_embed_dim: int = 64,
feature_dim: int = 128,
hidden_dim: int = 256,
num_gnn_layers: int = 3,
dropout: float = 0.3
):
super().__init__()
# Team embeddings
self.team_embedding = TeamEmbedding(num_teams, team_embed_dim)
# Feature encoder
self.feature_encoder = nn.Sequential(
nn.Linear(feature_dim, hidden_dim),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, team_embed_dim)
)
# Graph encoder
self.graph_encoder = MatchGraphEncoder(
node_features=team_embed_dim * 2,
hidden_dim=hidden_dim,
output_dim=hidden_dim // 2,
num_layers=num_gnn_layers,
dropout=dropout
)
# Match representation
match_dim = hidden_dim // 2 + team_embed_dim * 2
# Prediction heads
# 1X2 Result
self.result_head = nn.Sequential(
nn.Linear(match_dim, hidden_dim),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, 64),
nn.ReLU(),
nn.Linear(64, 3)
)
# Goals prediction
self.home_goals_head = nn.Sequential(
nn.Linear(match_dim, 64),
nn.ReLU(),
nn.Linear(64, 8) # 0-7 goals
)
self.away_goals_head = nn.Sequential(
nn.Linear(match_dim, 64),
nn.ReLU(),
nn.Linear(64, 8)
)
# BTTS
self.btts_head = nn.Sequential(
nn.Linear(match_dim, 32),
nn.ReLU(),
nn.Linear(32, 2)
)
# Over 2.5
self.over25_head = nn.Sequential(
nn.Linear(match_dim, 32),
nn.ReLU(),
nn.Linear(32, 2)
)
def forward(
self,
home_team_id: torch.Tensor,
away_team_id: torch.Tensor,
match_features: torch.Tensor,
edge_index: torch.Tensor = None,
return_embeddings: bool = False
) -> Dict[str, torch.Tensor]:
# Get team embeddings
home_embed = self.team_embedding(home_team_id)
away_embed = self.team_embedding(away_team_id)
# Encode features
encoded_features = self.feature_encoder(match_features)
# Combine for graph
combined = torch.cat([home_embed, away_embed], dim=-1)
# Graph encoding
if edge_index is not None:
graph_out = self.graph_encoder(combined, edge_index)
else:
graph_out = self.graph_encoder(combined)
# Match representation
match_repr = torch.cat([
graph_out,
home_embed,
away_embed
], dim=-1)
# Predictions
result = F.softmax(self.result_head(match_repr), dim=-1)
home_goals = F.softmax(self.home_goals_head(match_repr), dim=-1)
away_goals = F.softmax(self.away_goals_head(match_repr), dim=-1)
btts = F.softmax(self.btts_head(match_repr), dim=-1)
over25 = F.softmax(self.over25_head(match_repr), dim=-1)
output = {
'result': result,
'home_goals': home_goals,
'away_goals': away_goals,
'btts': btts,
'over_25': over25
}
if return_embeddings:
output['home_embedding'] = home_embed
output['away_embedding'] = away_embed
output['match_representation'] = match_repr
return output
def predict(self, home_team_id: int, away_team_id: int, features: np.ndarray) -> Dict:
"""Generate predictions for a single match."""
self.eval()
with torch.no_grad():
home_id = torch.tensor([home_team_id])
away_id = torch.tensor([away_team_id])
feat_tensor = torch.FloatTensor(features).unsqueeze(0)
output = self.forward(home_id, away_id, feat_tensor)
# Calculate correct scores
home_probs = output['home_goals'].squeeze().cpu().numpy()
away_probs = output['away_goals'].squeeze().cpu().numpy()
correct_scores = {}
for h in range(8):
for a in range(8):
correct_scores[f'{h}-{a}'] = float(home_probs[h] * away_probs[a])
# Normalize
total = sum(correct_scores.values())
if total > 0:
correct_scores = {k: v/total for k, v in correct_scores.items()}
return {
'result': {
'home_win': float(output['result'][0, 0]),
'draw': float(output['result'][0, 1]),
'away_win': float(output['result'][0, 2])
},
'correct_scores': dict(sorted(
correct_scores.items(),
key=lambda x: x[1],
reverse=True
)[:10]),
'btts_yes': float(output['btts'][0, 1]),
'over_25': float(output['over_25'][0, 1])
}
class TransformerPredictor(nn.Module):
"""
Transformer-based model for sequence prediction.
Processes team's recent match history.
"""
def __init__(
self,
feature_dim: int = 128,
d_model: int = 256,
nhead: int = 8,
num_layers: int = 4,
dropout: float = 0.2
):
super().__init__()
# Input projection
self.input_proj = nn.Linear(feature_dim, d_model)
# Positional encoding
self.pos_encoding = nn.Parameter(torch.randn(1, 50, d_model) * 0.1)
# Transformer encoder
encoder_layer = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=d_model * 4,
dropout=dropout,
batch_first=True
)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
# Output heads
self.result_head = nn.Sequential(
nn.Linear(d_model * 2, d_model),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(d_model, 3)
)
self.goals_head = nn.Sequential(
nn.Linear(d_model * 2, d_model // 2),
nn.ReLU(),
nn.Linear(d_model // 2, 16) # 8 home + 8 away
)
def forward(
self,
home_sequence: torch.Tensor, # (batch, seq_len, feature_dim)
away_sequence: torch.Tensor
) -> Dict[str, torch.Tensor]:
batch_size, seq_len, _ = home_sequence.shape
# Project inputs
home_proj = self.input_proj(home_sequence)
away_proj = self.input_proj(away_sequence)
# Add positional encoding
home_proj = home_proj + self.pos_encoding[:, :seq_len, :]
away_proj = away_proj + self.pos_encoding[:, :seq_len, :]
# Transformer encoding
home_encoded = self.transformer(home_proj)
away_encoded = self.transformer(away_proj)
# Pool (mean over sequence)
home_pooled = home_encoded.mean(dim=1)
away_pooled = away_encoded.mean(dim=1)
# Combine
combined = torch.cat([home_pooled, away_pooled], dim=-1)
# Predictions
result = F.softmax(self.result_head(combined), dim=-1)
goals = self.goals_head(combined)
home_goals = F.softmax(goals[:, :8], dim=-1)
away_goals = F.softmax(goals[:, 8:], dim=-1)
return {
'result': result,
'home_goals': home_goals,
'away_goals': away_goals
}
# Factory functions
def get_gnn_model(num_teams: int = 1000, feature_dim: int = 128) -> GraphFootballPredictor:
"""Get GNN model instance."""
return GraphFootballPredictor(
num_teams=num_teams,
feature_dim=feature_dim
)
def get_transformer_model(feature_dim: int = 128) -> TransformerPredictor:
"""Get Transformer model instance."""
return TransformerPredictor(feature_dim=feature_dim)
|