Implement multi-task learning pipeline for CSGO predictions
Created comprehensive multi-objective modeling system: **6 Prediction Tasks:** 1. Match Winner (Binary Classification) - Who wins the match? 2. Map Winner (Binary Classification) - Who wins this specific map? 3. Team 1 Score (Regression) - Predict exact round score for team 1 4. Team 2 Score (Regression) - Predict exact round score for team 2 5. Round Difference (Regression) - Predict score margin 6. Total Maps (Regression) - Predict number of maps in match **Implementation:** - Updated preprocessing to generate all target variables - Created train_multitask.py with separate models per task - Classification tasks use Random Forest Classifier - Regression tasks use Random Forest Regressor - All models logged to MLflow experiment 'csgo-match-prediction-multitask' - Metrics tracked per task (accuracy/precision for classification, MAE/RMSE for regression) - Updated DVC pipeline to use new training script **No Data Leakage:** - All features are pre-match only (rankings, map, starting side) - Target variables properly separated and saved with 'target_' prefix This enables comprehensive match analysis and multiple betting/analytics use cases. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Alexis Bruneteau
parent
a28a363dd9
commit
9440f4eecd
6
dvc.yaml
6
dvc.yaml
@ -16,9 +16,9 @@ stages:
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cache: false
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train:
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cmd: python src/models/train.py
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cmd: python src/models/train_multitask.py
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deps:
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- src/models/train.py
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- src/models/train_multitask.py
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- data/processed/train.csv
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- data/processed/test.csv
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params:
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@ -26,7 +26,7 @@ stages:
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- train.max_depth
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- train.random_state
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outs:
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- models/model.pkl
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- models/
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metrics:
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- models/metrics.json:
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cache: false
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@ -43,10 +43,28 @@ def engineer_features(df):
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features['both_top_tier'] = ((features['rank_1'] <= 10) & (features['rank_2'] <= 10)).astype(int)
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features['underdog_matchup'] = (abs(features['rank_diff']) > 50).astype(int)
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# Target: match_winner (1 or 2) -> convert to 0 or 1
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target = df['match_winner'] - 1
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# Multi-task targets
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targets = {}
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return features, target
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# Task 1: Match Winner (Binary Classification)
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targets['match_winner'] = df['match_winner'] - 1 # Convert 1/2 to 0/1
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# Task 2: Exact Score (Regression - two outputs)
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targets['score_team1'] = df['result_1']
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targets['score_team2'] = df['result_2']
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# Task 3: Round Difference (Regression)
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targets['round_diff'] = df['result_1'] - df['result_2']
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# Task 4: Map Count for the match (Multi-class)
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# Group by match_id to get total maps played
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match_maps = df.groupby('match_id').size().to_dict()
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targets['total_maps'] = df['match_id'].map(match_maps)
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# Task 5: Map Winner (Binary Classification for this specific map)
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targets['map_winner'] = df['map_winner'] - 1 # Convert 1/2 to 0/1
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return features, targets
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def save_metrics(X_train, X_test, y_train, y_test):
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"""Save dataset metrics"""
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@ -72,44 +90,67 @@ def main():
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print("Loading raw data...")
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df = load_raw_data()
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print(f"Loaded {len(df)} matches")
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print(f"Loaded {len(df)} maps")
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print("Engineering features...")
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X, y = engineer_features(df)
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X, targets = engineer_features(df)
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print(f"Created {X.shape[1]} features")
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print(f"Created {len(targets)} prediction targets:")
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for target_name in targets.keys():
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print(f" - {target_name}")
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print("Splitting data...")
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X_train, X_test, y_train, y_test = train_test_split(
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X, y,
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# Use match_winner for stratification
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X_train, X_test, idx_train, idx_test = train_test_split(
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X, X.index,
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test_size=params["test_size"],
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random_state=params["random_state"],
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stratify=y
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stratify=targets['match_winner']
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)
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print("Saving processed data...")
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Path("data/processed").mkdir(parents=True, exist_ok=True)
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# Save full features
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full_features = X.copy()
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full_features['target'] = y
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full_features.to_csv("data/processed/features.csv", index=False)
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# Save train set
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# Save train set with all targets
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train_data = X_train.copy()
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train_data['target'] = y_train
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for target_name, target_values in targets.items():
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train_data[f'target_{target_name}'] = target_values.iloc[idx_train].values
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train_data.to_csv("data/processed/train.csv", index=False)
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# Save test set
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# Save test set with all targets
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test_data = X_test.copy()
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test_data['target'] = y_test
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for target_name, target_values in targets.items():
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test_data[f'target_{target_name}'] = target_values.iloc[idx_test].values
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test_data.to_csv("data/processed/test.csv", index=False)
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# Save metrics
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save_metrics(X_train, X_test, y_train, y_test)
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# Save full features with all targets
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full_features = X.copy()
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for target_name, target_values in targets.items():
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full_features[f'target_{target_name}'] = target_values.values
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full_features.to_csv("data/processed/features.csv", index=False)
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print("Preprocessing completed successfully!")
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# Save metrics
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print("\nDataset statistics:")
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print(f"Train set: {len(X_train)} samples")
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print(f"Test set: {len(X_test)} samples")
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print(f"Features: {X.shape[1]}")
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metrics = {
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"n_samples": len(X),
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"n_train": len(X_train),
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"n_test": len(X_test),
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"n_features": X.shape[1],
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"targets": list(targets.keys()),
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"class_balance_match_winner": {
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"class_0": int((targets['match_winner'] == 0).sum()),
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"class_1": int((targets['match_winner'] == 1).sum())
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}
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}
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with open("data/processed/data_metrics.json", "w") as f:
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json.dump(metrics, f, indent=2)
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print("Preprocessing completed successfully!")
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if __name__ == "__main__":
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main()
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260
src/models/train_multitask.py
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260
src/models/train_multitask.py
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@ -0,0 +1,260 @@
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"""
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Multi-task model training pipeline for CSGO match prediction.
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Trains separate models for different prediction objectives and logs to MLflow.
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"""
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import mlflow
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import mlflow.sklearn
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import yaml
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import json
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import pickle
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import os
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from pathlib import Path
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from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
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from sklearn.metrics import (
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accuracy_score, precision_score, recall_score, f1_score, roc_auc_score,
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mean_absolute_error, mean_squared_error, r2_score
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)
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import pandas as pd
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import numpy as np
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# Configure MLflow
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# MLflow will automatically use MLFLOW_TRACKING_USERNAME and MLFLOW_TRACKING_PASSWORD env vars
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tracking_uri = os.getenv("MLFLOW_TRACKING_URI", "https://mlflow.sortifal.dev")
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mlflow.set_tracking_uri(tracking_uri)
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if os.getenv("MLFLOW_TRACKING_USERNAME") and os.getenv("MLFLOW_TRACKING_PASSWORD"):
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print(f"MLflow configured with authentication for {tracking_uri}")
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else:
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print(f"MLflow configured without authentication for {tracking_uri}")
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# Try to set experiment, but handle auth errors gracefully
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USE_MLFLOW = True
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try:
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mlflow.set_experiment("csgo-match-prediction-multitask")
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print(f"Connected to MLflow at {mlflow.get_tracking_uri()}")
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except Exception as e:
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print(f"Warning: Could not connect to MLflow: {e}")
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print("Training will continue without MLflow tracking.")
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USE_MLFLOW = False
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def load_params():
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"""Load training parameters from params.yaml"""
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with open("params.yaml") as f:
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params = yaml.safe_load(f)
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return params["train"]
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def load_data():
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"""Load preprocessed training and test data"""
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train_df = pd.read_csv("data/processed/train.csv")
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test_df = pd.read_csv("data/processed/test.csv")
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# Separate features and targets
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feature_cols = [col for col in train_df.columns if not col.startswith('target_')]
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target_cols = [col for col in train_df.columns if col.startswith('target_')]
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X_train = train_df[feature_cols]
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X_test = test_df[feature_cols]
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# Extract all targets
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targets_train = {col.replace('target_', ''): train_df[col] for col in target_cols}
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targets_test = {col.replace('target_', ''): test_df[col] for col in target_cols}
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return X_train, X_test, targets_train, targets_test
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def train_classification_model(X_train, y_train, params, task_name):
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"""Train a classification model"""
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print(f"\n[{task_name}] Training Random Forest Classifier...")
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model = RandomForestClassifier(
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n_estimators=params["n_estimators"],
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max_depth=params["max_depth"],
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random_state=params["random_state"],
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n_jobs=-1
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)
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model.fit(X_train, y_train)
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return model
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def train_regression_model(X_train, y_train, params, task_name):
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"""Train a regression model"""
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print(f"\n[{task_name}] Training Random Forest Regressor...")
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model = RandomForestRegressor(
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n_estimators=params["n_estimators"],
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max_depth=params["max_depth"],
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random_state=params["random_state"],
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n_jobs=-1
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)
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model.fit(X_train, y_train)
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return model
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def evaluate_classification(model, X_test, y_test, task_name):
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"""Evaluate classification model"""
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print(f"[{task_name}] Evaluating...")
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y_pred = model.predict(X_test)
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y_pred_proba = model.predict_proba(X_test)[:, 1]
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metrics = {
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f"{task_name}_accuracy": float(accuracy_score(y_test, y_pred)),
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f"{task_name}_precision": float(precision_score(y_test, y_pred, zero_division=0)),
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f"{task_name}_recall": float(recall_score(y_test, y_pred, zero_division=0)),
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f"{task_name}_f1_score": float(f1_score(y_test, y_pred, zero_division=0)),
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f"{task_name}_roc_auc": float(roc_auc_score(y_test, y_pred_proba))
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}
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return metrics
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def evaluate_regression(model, X_test, y_test, task_name):
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"""Evaluate regression model"""
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print(f"[{task_name}] Evaluating...")
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y_pred = model.predict(X_test)
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metrics = {
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f"{task_name}_mae": float(mean_absolute_error(y_test, y_pred)),
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f"{task_name}_mse": float(mean_squared_error(y_test, y_pred)),
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f"{task_name}_rmse": float(np.sqrt(mean_squared_error(y_test, y_pred))),
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f"{task_name}_r2": float(r2_score(y_test, y_pred))
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}
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return metrics
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def save_models(models, all_metrics):
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"""Save all models and metrics locally"""
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Path("models").mkdir(parents=True, exist_ok=True)
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# Save each model
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for task_name, model in models.items():
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model_path = f"models/model_{task_name}.pkl"
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with open(model_path, "wb") as f:
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pickle.dump(model, f)
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print(f"Saved {task_name} model to {model_path}")
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# Save all metrics
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with open("models/metrics.json", "w") as f:
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json.dump(all_metrics, f, indent=2)
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print(f"Metrics saved to models/metrics.json")
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def main():
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"""Main multi-task training pipeline"""
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print("=" * 70)
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print("CSGO Match Prediction - Multi-Task Model Training")
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print("=" * 70)
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# Load parameters and data
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params = load_params()
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X_train, X_test, targets_train, targets_test = load_data()
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print(f"\nDataset info:")
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print(f" Training samples: {len(X_train)}")
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print(f" Test samples: {len(X_test)}")
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print(f" Features: {X_train.shape[1]}")
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print(f" Prediction tasks: {len(targets_train)}")
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# Define tasks
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tasks = {
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'match_winner': {'type': 'classification', 'description': 'Match Winner Prediction'},
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'map_winner': {'type': 'classification', 'description': 'Map Winner Prediction'},
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'score_team1': {'type': 'regression', 'description': 'Team 1 Score Prediction'},
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'score_team2': {'type': 'regression', 'description': 'Team 2 Score Prediction'},
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'round_diff': {'type': 'regression', 'description': 'Round Difference Prediction'},
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'total_maps': {'type': 'regression', 'description': 'Total Maps Prediction'}
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}
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models = {}
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all_metrics = {}
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if USE_MLFLOW:
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with mlflow.start_run(run_name="multitask-rf-csgo"):
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# Log parameters
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mlflow.log_params(params)
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mlflow.log_param("n_features", X_train.shape[1])
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mlflow.log_param("n_train_samples", len(X_train))
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mlflow.log_param("n_test_samples", len(X_test))
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mlflow.log_param("n_tasks", len(tasks))
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# Train and evaluate each task
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for task_name, task_config in tasks.items():
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print(f"\n{'='*70}")
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print(f"Task: {task_config['description']}")
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print(f"{'='*70}")
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if task_name not in targets_train:
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print(f"Warning: {task_name} not found in training data, skipping...")
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continue
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y_train = targets_train[task_name]
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y_test = targets_test[task_name]
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# Train model based on task type
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if task_config['type'] == 'classification':
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model = train_classification_model(X_train, y_train, params, task_name)
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metrics = evaluate_classification(model, X_test, y_test, task_name)
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else:
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model = train_regression_model(X_train, y_train, params, task_name)
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metrics = evaluate_regression(model, X_test, y_test, task_name)
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models[task_name] = model
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all_metrics.update(metrics)
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# Log metrics to MLflow
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mlflow.log_metrics(metrics)
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# Print results
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print(f"\n{task_name} Results:")
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for metric, value in metrics.items():
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print(f" {metric}: {value:.4f}")
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# Save models and metrics
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save_models(models, all_metrics)
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# Print summary
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print("\n" + "=" * 70)
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print("Training Summary:")
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print("=" * 70)
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print(f"Models trained: {len(models)}")
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print(f"Total metrics: {len(all_metrics)}")
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print("=" * 70)
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print(f"\nMLflow run ID: {mlflow.active_run().info.run_id}")
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print(f"View run at: {mlflow.get_tracking_uri()}")
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else:
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# Train without MLflow
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for task_name, task_config in tasks.items():
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print(f"\n{'='*70}")
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print(f"Task: {task_config['description']}")
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print(f"{'='*70}")
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if task_name not in targets_train:
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print(f"Warning: {task_name} not found in training data, skipping...")
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continue
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y_train = targets_train[task_name]
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y_test = targets_test[task_name]
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# Train model based on task type
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if task_config['type'] == 'classification':
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model = train_classification_model(X_train, y_train, params, task_name)
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metrics = evaluate_classification(model, X_test, y_test, task_name)
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else:
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model = train_regression_model(X_train, y_train, params, task_name)
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metrics = evaluate_regression(model, X_test, y_test, task_name)
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models[task_name] = model
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all_metrics.update(metrics)
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# Print results
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print(f"\n{task_name} Results:")
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for metric, value in metrics.items():
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print(f" {metric}: {value:.4f}")
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# Save models and metrics
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save_models(models, all_metrics)
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print("\n" + "=" * 70)
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print("Training Summary:")
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print("=" * 70)
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print(f"Models trained: {len(models)}")
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print(f"Total metrics: {len(all_metrics)}")
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print("=" * 70)
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print("\nMulti-task training pipeline completed successfully!")
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if __name__ == "__main__":
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main()
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